vba journaal 22e jaargang voorjaar 2006 The long-term risk and return effects of investing in private equity 12

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vba

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journaal

22e jaargang voorjaar 2006

The long-term risk and return effects of investing in private equity 12 Sources of Hedge Fund Risk and Return 20

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Alternative Assets from a Liability Management Perspective 31 Global Value Added/Opportunistic Real Estate 44

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journaal nr. 1, voorjaar 2006

Inhoud

Agenda Programma VBA

The Long-term Risk and Return Effects of Investing in Private Equity 12 In dit artikel wordt een nieuwe methode geïntroduceerd die de “echte” risico en rendementskarakteristieken van private equity analyseert. Uit research van de auteurs blijkt dat het “uitsmeren” van zowel rendements- als risicohorizon over een langere periode een hogere volatiliteit en correlatie met genoteerde aandelen weergeeft. Echter, nog steeds leidt het toevoegen van private equity in een portefeuille context tot een efficientere en hoger renderende portefeuille.

Sources of Hedge Fund Risk and Return

april 2006 12 april 2006

18 april 2006

25 april 2006

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In dit artikel wordt ingezoomd op de risico’s die samenhangen met het beleggen in hedge funds. De auteurs stellen dat het rendementen risicoprofiel van hedge funds in het algemeen moeilijk is vast te stellen. Deze bijdrage is er dan ook vooral op gericht om een overzicht en een conceptueel raamwerk te verschaffen van de meest voorkomende rendements- en risicobronnen van hedge funds.

Alternative Assets from a Liability Management Perspective 31 De auteurs onderzoeken de merites van het beleggen in alternative assets in een ALM-raamwerk. De mogelijke positieve aspecten van alternatieve beleggingen kunnen danig beperkt worden indien het risico dat samenhangt met ALM-mismatches in beschouwing wordt genomen. Uit het onderzoek van de auteurs blijkt dat het gebruik van een derivatenoverlay om het risico ten opzichte van de verplichtingen expliciet te managen, een effectieve manier is om de positieve aspecten van beleggingen in alternatives ten volle te kunnen benutten.

Global Value Added / Opportunistic Real Estate 44

mei 2006 3 mei 2006

18 mei 2006

23 mei 2006

juni 2006 1 juni 2006

Presentatie door Middle East Investments Rosarium te Amsterdam Trends in Private Equity – Drs. A.J.C. van den Ouweland RBA Rosarium te Amsterdam Eerste bijeenkomst in de cyclus van drie lezingen rond het thema alternatieve energie – ExxonMobil De Industrieele Groote Club te Amsterdam

Lezing Professor Kenneth French Hoogleraar op het gebied van Finance uit de VS Tweede bijeenkomst in de cyclus van drie lezingen rond het thema alternatieve energie – Prof.Dr. Jos Bruggink Derde bijeenkomst in de cyclus van drie lezingen rond het thema alternatieve energie – Prof.Dr. Frans Sluijter

Negende Lustrum van de VBA “De toekomst van de financiële industrie in Nederland”

Informatie over bovenstaande bijeenkomsten te verkrijgen bij het secretariaat: telefoon: 020 - 618 28 12 e-mail: [email protected] of website www.nvba.nl

In een beschouwend artikel geven de auteurs hun visie op een enigszins meer “hands-on” benadering ten aanzien van onroerend goed beleggingen: niet alleen internationaal, maar ook via een opportunische en een zogenaamde “value added” strategie. Zij concluderen dat er wel degelijk alpha wordt gegenereerd indien binnen het optimale adjusted risk/return profiel een superieure manager- en stijlselectie wordt nagestreeft.

VBA Journaal is een uitgave van VBABeroepsvereniging van Beleggingsdeskundigen Herengracht 479 1017 BS Amsterdam

Redactie Drs. H. van Capelleveen Drs. E. van Gelderen Drs. J.L.H. van der Kolk RBA A.H. Leuftink Drs. Ph.D.H. Menco RBA

Hoofdredacteur Drs. A.J.C. de Ruiter

Redactieadres & opgave advertenties VBA Irma Willemsen telefoon: 020 - 618 2812 e-mail: [email protected]

Plaatsvervangend hoofdredacteur Prof.Dr. Ph. Stork

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Abonnementen VBA Herengracht 479 1017 BS Amsterdam telefoon: 020 - 618 2812 e-mail: [email protected] Abonnementenprijs 2006: ¤ 82,73 inclusief verzendkosten

De in het VBA Journaal geplaatste artikelen geven de mening weer van de auteurs en niet noodzakelijk de mening van de redactie.

Vormgeving en realisatie a z az grafisch serviceburo bv, Den Haag. www.az-gsb.nl

Copyright © 2006 VBA-Beroepsvereniging van Beleggingsdeskundigen

ISSN-nummer 0920-2269

Niets uit deze uitgave mag worden verveelvoudigd en/of openbaar gemaakt door middel van druk, fotokopie, microfilm of op welke wijze dan ook, zonder voorafgaande schriftelijke toestemming van de uitgever.

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Van de voorzitter

Zoals iedereen in de pers heeft kunnen lezen, heeft het bestuur een samenwerkingsovereenkomst gesloten met NSIP. NSIP is de Nederlandse chapter van CFA – Institute. Op het gebied van basisopleidingen zouden we kunnen stellen dat de VBA opleiding een concurrent is van de CFA opleiding. De VBA opleiding is evenwel meer toegespitst op de Nederlandse en Europese wet- en regelgeving en ook de aanpak met veel cases doet het niveau van de VBA opleiding uitstijgen boven de CFA. En dat terwijl de VBA opleiding 2 jaar en de CFA opleiding 3 jaar duurt. Maar concurrentie is een goede zaak en het houdt de aanbieders van onze opleiding scherp op verbeteringen en aanpassingen. Deze concurrentie is één zaak, maar het bestaan van twee Beroepsverenigingen van beleggingsprofessionals is een tweede. Daarvoor is Nederland te klein. De VBA is een actieve vereniging met veel activiteiten, commissies, bijeenkomsten, publicaties én permanente educatie. De besturen van VBA en van NSIP zijn na enkele gesprekken snel tot de conclusie gekomen dat samenwerking tot toegevoegde waarde leidt, waar de leden alleen maar profijt van zullen hebben. De samenwerking richt zich in eerste instantie op het tegen speciale, gereduceerde tarieven bijwonen van presentaties en bijeenkomsten van beide verenigingen. Daarnaast stelt de VBA haar programma van permanente educatie open voor NSIP leden en kunnen CFA-ers een abonnement nemen op het VBA Journaal tegen een verlaagd tarief. Er is een gemeenschappelijk forum opgericht waarin de VBA commissie regelgeving zal participeren. Voorzitter van dit gemeenschappelijke forum zal Harry Panjer zijn. Dit forum zal gevraagd en ongevraagd advies uitbrengen over actuele zaken op het gebied van wet- en regelgeving. Het forum kan zich in verband met het vele werk, door deskundige buitenstaanders laten bijstaan. Wij als bestuur gaan er van uit dat dit het begin is van een langdurige samenwerking, waarbij de tijd zal leren hoe deze verbroedering nog verder ontwikkeld kan worden. Beide besturen hebben er vertrouwen in dat een hecht verbond tot de mogelijkheden behoort.

Dr. René Th. H. Willemsen RBA, voorzitter VBA

dingen laat nog veel te wensen over. Binnen EFFAS is een werkgroep actief die de achterstanden inventariseert en met oplossingen komt. Financiële ondersteuning hoort daar ook bij. In dat kader heeft een aantal landen binnen EFFAS besloten de deelname van de nationale organisaties de komende jaren te sponsoren. Het bestuur van de VBA heeft voor dit jaar € 4.150 ter beschikking gesteld. De volgende landen kunnen nu deelnemen aan EFFAS: Polen, Roemenië, Bulgarije, Letland, Kazakhstan, Oekraïne, Rusland en Griekenland. Over de voortgang van dit project zullen we u blijven informeren.

René Willemsen

Binnen EFFAS heeft de VBA het voorstel gedaan om nationale organisaties in opkomende Europese landen ter zijde te staan. Veel voormalige Oost-Europese landen kampen met forse achterstanden wat betreft wet- en regelgeving en ook het niveau van de oplei-

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. Van het bestuur De Beroepsvereniging van Beleggingsdeskundigen (VBA) en The Netherlands Society of Investment Professionals (NSIP) hebben 6 maart 2006 een samenwerkingsovereenkomst gesloten. Het Financieele Dagblad besteedde hier een artikel aan onder de kop ‘Nederlandse analistenclubs bundelen hun krachten’ in de krant van 7 maart 2006.

. Commissie Tactische Asset Allocatie Op 27 oktober 2005 organiseerde de commissie een bijeenkomst voor de leden van het VBA. U kunt hieronder het verslag van deze bijeenkomst lezen. De commissie is uitgebreid met enkele nieuwe leden tot ongeveer tien personen.

Namens de leden van de commissie wil ik Loranne van Lieshout en Lodewijk van der Kroft hartelijk bedanken voor hun jarenlange inzet als voorzitter en secretaris van de werkgroep. Loranne en Lodewijk hebben zich altijd met veel inzet, enthousiasme en flair ingezet voor de werkgroep en blijven gelukkig ook lid. Marcel van Ostaden is de nieuwe secretaris. Voor nadere informatie over onze activiteiten, kunt u zich tot Marcel wenden (Marcel_vanOstaden@ ml.com). Namens de commissie TAA, Patrick Bronger, voorzitter

Verslag van het TAA seminar Op 27 oktober 2005 organiseerde de Commissie Tactische Asset Allocatie een bijeenkomst voor de leden van de VBA. Tijdens deze bijeenkomst werden de resultaten gepresenteerd van een eerder dat jaar gehouden enquête onder VBA leden evenals van de

Van links naar rechts achterste rij: Anne-Marie Munnik (VBA), Bart Noordman (NSIP), Harry Panjer (VBA), André Broijl (NSIP) en Rob Soentken (NSIP) Van links naar rechts voorste rij: Roelie van Wijk (VBA), Sylvia van de Kamp (VBA), René Willemsen (VBA) en Mikan van Zanten (NSIP)

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diepte-interviews die met verschillende respondenten waren gevoerd. Voor deze bijeenkomst waren gastsprekers van ABN AMRO Asset Management, ABP en Bridgewater uitgenodigd om hun visie te geven op tactische asset allocatie (TAA). Hieronder volgt een samenvatting van genoemde onderzoeksresultaten en van de bijdragen van de sprekers.

Resultaten onderzoek In de presentatie van de commissie werden de resultaten van het TAA onderzoek gepresenteerd. Circa 50 organisaties hadden deelgenomen aan het onderzoek. Het betrof hier een redelijk gelijkmatig verdeelde groep, bestaande uit pensioenfondsen, asset managers, verzekeraars en banken. De overgrote meerderheid van de respondenten (80%) was het eens met de door de commissie gehanteerde definitie voor TAA (‘De korte termijn afwijking van de strategische asset allocatie, met als doel het behalen van extra rendement en/of het tijdelijk aanpassen van het risicoprofiel’). Personen die zich niet konden vinden in de definitie wezen erop dat TAA ook zonder strategische allocatie mogelijk is. In dat geval staat TAA op zichzelf en worden haar resultaten afgezet tegen een benchmark van 0 (‘absolute return’) of tegen het rendement op liquiditeiten. Ook de commissie onderschrijft deze mogelijke toepassing van TAA. Er bestond geen overduidelijke voorkeur voor kwantitatieve of kwalitatieve indicatoren in het TAA beslissingsproces. Tijdens de diepte-interviews kwam naar voren dat de kwantitatieve/technische indicatoren beter meetbaar zijn en daardoor minder gevoelig voor emotie. Overigens bleken pensioenfondsen meer te neigen naar kwantitatieve indicatoren, daar waar vermogensbeheerders een voorkeur hebben voor kwalitatieve indicatoren. Per saldo waren de respondenten positief gestemd over de toegevoegde waarde van TAA. Een mening die niet door ieder onderzoek ondersteund wordt (zie VBA journaal, nummer 3, 2004). Uit de enquête bleek verder dat pensioenfondsen de toegevoegde waarde van TAA vergelijkbaar achten met andere beleggingsbeslissingen, terwijl vermogensbeheerders juist een grotere waarde aan TAA toedichten. In de enquête werd gevraagd naar de belangrijkste redenen voor de hernieuwde aandacht voor TAA. De door de commissie geformuleerde redenen: focus op

absoluut rendement, uitbreiding van het beleggingsuniversum, verruiming van de implementatiemogelijkheden en meer aandacht voor risicobudgettering werden onderschreven door de overgrote meerderheid van de respondenten. Vooral de laatste reden werd door verschillende personen aangegrepen om aan te geven dat TAA nuttiger is dan in het verleden. Wat niet wegneemt dat het behalen van extra rendement, ook voor de meeste pensioenfondsen, de belangrijkste reden bleef om TAA te hanteren in het beleggingsproces. Uit het onderzoek bleek dat TAA nog op een vrij traditionele manier wordt toegepast. In de eerste plaats doordat zij vooral over de traditionele beleggingscategorieën (aandelen, obligaties en liquiditeiten) wordt uitgevoerd. Slechts in de helft van de gevallen werd ook TAA toegepast binnen de beleggingscategorieën. Bridgewater gaf in haar bijdrage tijdens het seminar (zie hierna) aan dat bij hen vooral TAA binnen asset classes de nadruk heeft. In de tweede plaats kan uit de antwoorden geconcludeerd worden dat TAA veelal wordt uitgevoerd door middel van transacties in de onderliggende stukken. Dit is opmerkelijk, aangezien het gebruik van derivaten een efficiëntere manier is om TAA beslissingen te implementeren. Het gebruik van derivaten blijkt wel in opkomst. Slechts zeer weinig respondenten gaven aan dat TAA door middel van een ‘overlay’ programma of als apart mandaat (met een hoge tracking error of een absolute return mandaat) wordt ingevuld. Het merendeel van de respondenten heeft aangegeven dat de horizon van TAA beslissingen zes maanden of korter bedraagt. In het door de commissie geschreven artikel, Tactische Asset Allocatie: Eigentijds vermogensbeheer, een nieuw elan voor TAA (VBA journaal, nummer 3, 2004) wordt aangegeven dat de grenzen tussen TAA en strategische asset allocatie (SAA) lijken te vervagen. Dit zou op termijn invloed kunnen hebben op de lengte van de TAA horizon.

TAA binnen het nFTK In een bijdrage van Maarten Roest van ABN AMRO Structured Asset Management werden de seminarbezoekers geïnformeerd over de consequenties van TAA bezien vanuit het Nieuw Financieel Toetsingskader (nFTK). Roest gaf aan dat het nFTK pensioenfondsen oplegt om uitkeringen tegen de marktrente en niet langer tegen de actuariële rekenrente van 4%

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contant te maken. Hierbij mag de kans op onderdekking na 1 jaar maximaal 2,5% zijn. De vereiste ‘buffer’ die pensioenfondsen derhalve dienen aan te houden kan worden berekend met behulp van de ‘Wortelformule’. In deze formule wordt niet expliciet rekening gehouden met TAA. Volgens het consultatiedocument moeten pensioenfondsen met behulp van scenario’s rekening houden met actief risico en concentratierisico. Aangezien TAA invloed heeft op het actief risico in de portefeuille, dienen pensioenfondsen TAA daarom mee te nemen in de onderbouwing van het totale beleggingsrisico. Roest is van mening dat TAA leidt tot fluctuatie van de benodigde buffer. Hij stelde dat gekozen kan worden om deze op te vangen door een ‘veiliger’ strategische mix. Door bijvoorbeeld minder in aandelen te beleggen en daardoor ruimte te creëren voor TAA, wordt ‘beta exposure’ ingeruild voor ‘alpha exposure’. Verder meende hij dat TAA eenvoudig aan een nieuwe benchmark kan worden toegevoegd; TAA schaalbaar is, en een hoger rendement per Euro belegd vermogen kan opleveren bij een gematigd positieve informatieratio. TAA past daarom volgens Roest prima binnen het nFTK.

TAA bij ABP Ivan Moen is bij het ABP medeverantwoordelijk voor TAA. In de presentatie werd begonnen met de definitie van TAA: “de exploitatie van alpha-mogelijkheden middels actieve allocatie tussen verschillende geaggregeerde assets”. Moen gaf aan dat het ABP daarbij een beleggingshorizon van 1 tot 18 maanden hanteert. Het aantal assets dat momenteel voor TAA in aanmerking komt, bestaat uit aandelen (wereldwijd) en staatsobligaties en zal de komende jaren waarschijnlijk worden uitgebreid. TAA wordt mede als alpha source en diversificatiemiddel gezien. ABP voert TAA intern uit, mede om synergetische en kostenredenen. Het proces wordt onder andere gekenmerkt door een top down benadering en een mix van fundamentele en kwantitatieve criteria. Daarbij worden sentiment, macro-indicatoren en waarderingen geanalyseerd. Ten behoeve van risk management wordt onder andere gebruik gemaakt van riskmetrics. Na een ‘papieren’ portefeuille in

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2004 is ABP in 2005 overgegaan op een fysieke portefeuille. De resultaten zijn tot op heden zeer goed.

TAA bij Bridgewater In de bijdrage van Bridgewater aan het seminar, stelde Lionel Kaliff dat de traditionele manier van portefeuilleconstructie beleggers beperkt in hun mogelijkheden. Beleggers alloceren kapitaal in plaats van risico en daarnaast zouden zij beleggingscategorieën teveel accepteren in hun standaardverschijning. Kaliff pleitte ervoor om alpha en beta meer van elkaar te scheiden. Op die manier kan een belegger op zoek gaan naar de beste mix aan beta’s en alpha’s. Afhankelijk van het vertrouwen dat de belegger heeft in de mate waarin alpha en beta kan worden gerealiseerd, vindt vervolgens de allocatie plaats. Volgens Kaliff, kan een portefeuille die ingericht wordt langs haar risicobudget een duidelijk hoger rendement behalen en minder risicovol zijn. TAA volgens Bridgewater betekent een zoektocht naar alpha, waarbij twee belangrijke principes in acht dienen te worden genomen. Ten eerste dienen beleggers symmetrische beleggingsbeslissingen te nemen c.q. dienen zij zowel ‘long’ als ‘short’ posities in iedere beschikbare beleggingscategorie aan te houden. Ten tweede dienen beleggers optimaal gebruik te maken van de mogelijkheden tot diversificatie. Het aantal beleggingscategorieën dat nodig is om specifieke risico’s weg te diversifiëren neemt af naar mate de correlatie lager is. In de presentatie kwam een portefeuille aan de orde waarin zeven beleggingscategorieën zijn opgenomen. Binnen die categorieën wordt een groot aantal, laag correlerende alpha strategieën uitgevoerd. Kaliff stelde dat beleggers omgevingen dienen te identificeren waarin de portefeuille geen bescherming kent; dienen zij hun middelen vervolgens te herverdelen ten einde wel beschermt te zijn, om vervolgens een ‘overlay’ alpha strategie over de portefeuille te voeren ten einde een hoger rendement zonder een materieel hoger portefeuillerisico te realiseren.

Vervolg… De commissie TAA zal de komende periode gebruiken om een aantal zaken nader uit te werken. Voorbeelden hiervan zijn: TAA en het gebruik van risicobudgettering, TAA als overlay strategie, TAA binnen een

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LDI omgeving, TAA in relatie tot SAA. Het is de bedoeling om dit uiteindelijk in de vorm van een TAA katern aan de leden van de VBA ter beschikking te stellen.

. Dutch Commission on Bonds MiFid: wederom regelgeving

. Programmacommissie Verslag van de bijeenkomst over Technische Analyse Op 23 februari 2006 gaf Juus de Kempenaer, voorzitter van de Commissie Technische Analyse, een presentatie over Technische Analyse. Juus de Kempenaer begon zijn presentatie met een korte uitleg over het relatieve sterkte concept in algemene zin. Op basis van RS analyses kan een uitspraak worden gedaan met betrekking tot de aantrekkelijkheid van een bepaald aandeel of een sector ten opzichte van een bepaalde benchmark. Het is echter lastiger om aan te geven wat bijvoorbeeld “de beste” of “de slechtste” sector is. Om antwoord te geven op deze vraag heeft hij een methode ontwikkeld om aandelen of sectoren binnen een universum te rangschikken en zo de (relatieve) aantrekkelijkheid binnen een universum te kunnen bepalen. Om enig inzicht te krijgen in de winstgevendheid c.q. de toegevoegde waarde voor (institutionele) beleggers van het gebruik van deze methode had Juus een aantal “backtests” uitgevoerd op verschillende universa. Ondanks het feit dat dergelijke tests toch de werkelijkheid niet helemaal benaderen geeft het toch een goed inzicht in de mogelijke toegevoegde waarde van deze benadering. Tenslotte heeft hij laten zien hoe het mogelijk is om de relatieve bewegingen van aandelen of sectoren binnen een universum in te beeld brengen middels een zogenaamd “scatter diagram”. Hiermee kan een fundmanager of een handelaar in een oogopslag zien welke aandelen of sectoren interessant zijn voor overof onderwegingen op basis van relatieve sterkte. Circa twintig leden van de VBA woonden deze bijeenkomst bij. Geïnteresseerden om lid te worden van de Commissie Technische Analyse worden opgeroepen om zich met Juus de Kempenaer in verbinding te stellen.

In het voorjaar van 2005 heeft de bijdrage van de DCB aan het VBA-journaal volledig in het teken gestaan van regelgeving. Nu is dat niet anders! Na FTK, IFRS en Basel II zijn nu aangescherpte compliance-richtlijnen en in het verlengde daarvan MiFID aan de beurt. MiFID staat voor ‘Markets in Financial Instruments Directive’ en is de nieuwe Europese directive die tracht de Europese financiële markten te harmoniseren en transparantie daarbinnen te vergroten. Belangrijkste doelstellingen zijn bescherming van (retail-)beleggers, verwijderen van obstakels tussen markten van verschillende EU-landen, gezondere concurrentie en creatie van een level playing field voor de handel in effecten. Senior managers zullen meer dan voorheen worden afgerekend op non-compliance. MiFID vervangt de inmiddels achterhaalde Investment Services Directive. Volledige implementatie van de nieuwe directive staat gepland voor het najaar 2007. Niettemin is binnen het bankwezen bij het gros van de werknemers de kennis over MiFID en haar impact nog beperkt, als men al bekend is met de term. In de European Bond Commission (EBC) hebben we eind februari al een uitgebreide sessie belegd over deze nieuwe richtlijnen, in de DCB zullen we er in de april-vergadering meer aandacht aan besteden. Zoals DCB altijd belijdt, ook nu weer een zeer actueel thema met ongetwijfeld ingrijpende gevolgen voor de financiële industrie.

Januari-vergadering De vergadering vond plaats bij ABN AMRO Asset Management. De presentatie van de ABN AMRO stond in het teken van Covered Bonds, een voor Nederland nieuwe emissievorm. Beleggers kunnen investeren in hoogwaardig (AAA-rated) schuldpapier, doordat deze schuld een hypotheekportefeuille als onderpand heeft. Deze portefeuille wordt als het ware van de bank balans afgehaald, en dient als collateral voor de bonds. In Nederland ontbreekt echter vooralsnog de benodigde wetgeving om in aanmerking te komen voor een lagere credit weighting in het licht van Basel II. In internationale context doen vooral Pfandbriefe (Duitse covered bonds) al langer van zich spreken, en staat Nederland derhalve nog in de kinderschoenen.

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ABN AMRO is tot op heden de enige speler in de nationale markt, met twee uitstaande Covered Bonds.

. Investment Performance Measurement Commissie

Mini-seminar DCB is momenteel bezig met de voorbereiding van een thema-middag rondom het fenomeen ‘Inverse Yield Curves’. Met name wat de eventuele voorspellingswaarde van een dergelijk curve verloop heeft voor de groei van de economie. Is het een vooraankondiging van een recessie? Op dit moment is de curve in Amerika volledig vlak, wat betekent dit voor de Amerikaanse economie? Wat hebben we in het verleden gezien in vergelijkbare situaties van een inverse curve? Wat staat de eurozone te wachten? Gaan we ‘achter Amerika aan’? We hopen antwoorden te vinden op dit mini-seminar. U wordt via het VBA-secretariaat op de hoogte gehouden omtrent het programma en de planning.

Europese Bond Commissie Tijdens de plenaire vergadering in februari 2006 (Luxemburg) is – volledig in lijn met DCB – veel aandacht besteed aan twee onderwerpen: (1) MiFID en transparantie in de financiële markten, en (2) de Europese Covered Bond markt. Zoals hierboven aangegeven is die markt in Nederland weliswaar nog jong, maar in Europa staat er eind 2004(!) al zo’n EUR 1,6 mld uit. Interessant was de discussie tussen representanten van verschillende covered bond markten, waaronder HBOS, Association of German Pfandbriefe Banks, Danske Bank en S&P. De volgende sessie van EBC staat gepland voor juni 2006 in Budapest. Het centrale thema zal Fixed Income e-Trading zijn, met aandacht voor de e-trading platforms, aanbieders en beleggers. DCB heeft in januari 2001 al eens een seminar georganiseerd rondom dit thema.

Onze leden De DCB verwelkomt al uw ideeën of suggesties ten aanzien van genoemde of nieuwe onderwerpen. U kunt daartoe te allen tijde contact opnemen met het VBA-secretariaat of rechtstreeks met de leden van onze commissie. U kunt ook naar de vernieuwde website gaan op www.nvba.nl onder ‘Commissies’.

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In het onderstaande stelt de Commissie Investment Performance Measurement u in kennis van een aantal ontwikkelingen. De meeste hebben te maken met de wereldwijde Global Investment Performance Standards (GIPS). GIPS wordt gehanteerd door vermogensbeheerders bij de informering van prospects om te waarborgen dat historische rendementen op ethisch verantwoorde wijze worden gepresenteerd. De standaarden zijn een initiatief van het Amerikaanse CFA Institute. De standaarden worden ontwikkeld door een zogenaamde standing committee van CFA Institute: het Investment Performance Council (IPC). Dit IPC kent een wereldwijde bezetting. In het organisatorische plaatje is voorts nog belangrijk dat sprake is van een Europese IPC (EIPC) waarin de Commissie rechtstreeks participeert en nationale commissies zoals VBA IPM die input en commentaren leveren.

GIPS 2006 Het meest belangrijke nieuwsfeit in het lopende jaar is dat per 1 januari 2006 de nieuwe versie van de GIPS van kracht is. Wij hebben u daarover reeds eerder bericht. In dit kader is er nog een aantal ontwikkelingen waar we u over willen informeren.

GIPS 2006 in het Nederlands Begin januari hebben alle VBA-leden de Nederlandse vertaling van de nieuwe GIPS standaarden ontvangen. Hiermee was de VBA de eerste country sponsor die de nieuwe standaard vertaald heeft en heeft laten goedkeuren door het CFA Institute. De nieuwe vertaling krijgt de naam GIPS en vervangt de VBA-PPS 2003 (welke reeds een vertaling was van GIPS 1999). Hiermee is tevens de merknaam VBA-PPS 2003 van het toneel verdwenen. Deze uniforme naamgeving wordt ook toegepast in andere landen. De grote toegevoegde waarde is dat wereldwijd nog maar één naam voor de standaard wordt gebruikt en dat is GIPS.

VBA-bijeenkomst GIPS Op 26 januari 2006 heeft de commissie de nieuwe GIPS standaarden gepresenteerd aan de leden van de VBA. In deze presentatie heeft de commissie een uiteenzetting gegeven van de historische ontwikkeling

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Verenigingsnieuws

van de standaarden en de nieuwe aspecten binnen de nieuwe versie van GIPS. Mocht u deze bijeenkomst gemist hebben, dan zijn de sheets op te vragen bij het secretariaat. Tevens zijn rond deze bijeenkomst twee artikelen verschenen in Het Financieele Dagblad.

Consistentie met bestaande guidance statements GIPS kent drie lagen met regels. Bovenal is er de feitelijke tekst van GIPS en daarnaast zijn er Guidance Statements en Questions & Answers waar compliant vermogensbeheerders zich ook aan moeten houden. Met het publiceren van GIPS 2006 is een aantal inconsistenties ontstaan met de Guidance Statements en Q&A’s. De afgelopen maanden heeft het CFA Institute deze Guidance Statements aangesloten met GIPS 2006. De volgende Guidance Statements zijn in lijn gebracht met de nieuwe GIPS standaarden: • Guidance Statement on Calculation Methodology • Guidance Statement on Composite Definition • Guidance Statement on Definition of the Firm • Guidance Statement on Performance Record Portability • Guidance Statement on the Treatment of CarveOuts • Guidance Statement on the Treatment of Significant Cash Flows • Guidance Statement on the Use of Supplemental Information • Guidance Statement for Verification behalf Op de website van het CFA Institute zijn de laatste versies van deze Guidance Statements te vinden. Met betrekking tot de Q&A zijn de aanpassingen als gevolg van GIPS 2006 nog niet verwerkt. Uit navraagt bij het CFA Institute bleek dat het CFA Institute bezig is met een algehele opschoonactie van de Q&A database. Hierbij worden de GIPS Q&A en de AIMRPSS Q&A databases samengevoegd en worden achterhaalde vragen verwijderd. Daarnaast worden als gevolg van de nieuwe versie van GIPS nieuwe vragen toegevoegd en worden de vragen afgestemd met de nieuwe Guidance Statements.

GIPS Handboek Zoals eerder vermeld in het VBA journaal is het CFA Institute voornemens om voor de nieuwe versie van GIPS een aangepast handboek op te stellen. Navraag bij het CFA Institute leverde op dat de tweede versie van het handboek is ontwikkeld door het Interpretations Subcommittee en momenteel bij de drukker ligt. Het CFA Institute verwacht dat het nieuwe handboek uiterlijk eind april 2006 beschikbaar is op website (http://www.cfainstitute.org/cfacentre/ips/).

Status Guidance Statements Zoals eerder beschreven zijn de bestaande Guidance Statements in lijn gebracht met de nieuwe versie van GIPS. Naast deze bestaande Guidance Statements blijft het IPC nieuwe Guidance Statements ontwikkelen om de GIPS standaarden aan te laten sluiten met de laatste ontwikkelingen in de industrie. Guidance statement goedgekeurd door het IPC: • Guidance Statement on Verifier Independence (oktober 2005) Concept Guidance Statements, waarvan de commentaarperiode inmiddels is gesloten: • Guidance Statement on Performance Examination • Guidance on Portfolio Recordkeeping Requirements • Guidance on the Use of Leverage and Derivatives • Guidance on Error Correction Geplande of in ontwikkeling zijnde Guidance Statements: • Firm Mergers and Acquisitions • Composite Maintenance (nog geen tijdslijn) • Risk Measures (nog geen tijdslijn) • Hedge Funds (nog geen tijdslijn) De commissie geeft op ieder van de gepubliceerde voorstellen een reactie. Op de website van het CFA Institute kan te allen tijde de laatste stand van zaken in het kader van deze Guidance Statements worden gevonden.

9

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Verenigingsnieuws

. Seniorenconvent SECO Beleid en activiteiten in 2006 Senior beleggingskundigen 55 plussers verheugt u. SECO biedt een boeiend programma gedurende 2006 dat u zonder kosten kunt bijwonen. De leden van het Seniorenconvent – oud-leden van de VBA – hebben besloten u daartoe de gelegenheid te geven. Behalve u zullen ook gastleden van de SECO-activiteiten kunnen genieten. Datum

Tijd

Gastheer

4-1-2006

15.00- VBA 18.00

15-3-2006

13.30- Bezoekers centrum Achtergracht 4 18.00 DNB Amsterdam

Verder hopen wij ook oud-leden van de VBA die niet lid zijn van SECO op te sporen om hen uit te nodigen mee te doen. Mede door de uitbreiding van het aantal senior beleggingskundigen is het mogelijk een voor ieder interessant programma te ontwikkelen. In 2006 ziet dit er als volgt uit:

Plaats

Spreker(s)

Onderwerp

I.G.C.

Pim Meijer

Wat is er met onze pensioenen aan de hand?

Prof.Dr Marius Nieuwkerk

Introductiefilm, Betalingsverkeer, Goud, Toezichtspel Hollands Gouden Glorie

André E. Teeuw

Verminderde concurrentiekracht in Nederlandse financiële dienstverlening

10-5-2006 15.00- Binck Effectenbank Vijzelstraat 20 17.30 Amsterdam 12-7-2006

14.30- KAS Bank 17.30

N.Z.Voorburgwal 225 Drs Th.(Ted) J.M.van Heese Rol en plaats van de KAS Bank Amsterdam Drs John S.A.van Scheijndel

13-9-2006

15.00- Koninklijke Brill 18.00

Leiden

Diverse sprekers

Bedrijfspresentaties

2-11-2006

14.00- Aegon 21.00

Aegonplein 50 ’s-Gravenhage

Drs Jaap F.M.Peters Prof.Dr Jaap van Duijn

Corporate Governance revisited Invloed van regelgeving Praktijk van regelgeving bij Aegon

Elke bijeenkomst wordt afgesloten met een borrel, die door de gastheer wordt aangeboden. Na afloop kunnen de deelnemers (op eigen kosten) gezamenlijk in een gezellig restaurant in de buurt dineren. Ter afsluiting van het jaar 2006 wordt een diner door Aegon aangeboden. Om de bijeenkomsten te verlevendigen wordt steeds een aantal leden gevraagd zich hierop gezamenlijk voor te bereiden. U kunt hieronder het verslag lezen van de bijeenkomst bij De Nederlandsche Bank.

SECO Senior Beleggingskundigen bezochten DNB SECO Senior Beleggingskundigen hield op 15 maart zijn tweede bijeenkomst van het jaar. 22 leden waren te gast bij De Nederlandsche Bank, waar een uitgebreid programma was voorbereid.

10

Ingegaan werd op verschillende aktiviteiten van DNB. Daarnaast hield Prof.Dr. M. van Nieuwkerk een zeer boeiende inleiding naar aanleiding van het door hem geschreven boek “Holland Gouden Glorie”. Uit dit boek blijkt dat wij best trots mogen zijn op de geschiedenis van ons land en volk. Het boek neemt niet alleen afstand van de waan van de dag, maar ook de waan van het jaar. Wat overblijft, zijn de grote verbanden en trends. Dan lijken zelfs de schijnbaar hectische schommelingen in de koers van de dollar weg te vallen. Het boek staat overigens bol van de interessante voorvallen, overzichten en illustraties die het verleden tot leven roepen. Keer op keer wordt de koppeling van het verleden naar het heden gemaakt, hetgeen bijdraagt aan de aantrekkelijkheid van dit boek dat inmiddels zijn derde druk ziet en ook in het Engels wordt vertaald. Prof.Dr. Van Nieuwkerk voorziet een toenemende aandacht van de monetaire autoriteiten voor de “liqui-

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Verenigingsnieuws dity glut” die is ontstaan na jaren waarin de geldhoeveelheid in verschillende economische blokken met dubbele cijfers groeide. Deze hernieuwde aandacht vormt een groot verschil tussen het beleid van Bernanke ten opzichte van zijn voorganger Greenspan. Zolang doelstellingen ten aanzien van inflatie en economische groei dit toelaten verwacht Van Nieuwkerk renteverhogingen door de centrale banken. Overigens benadrukt Bernanke het belang van het terugdringen van het Amerikaanse begrotingstekort ook veel sterker dan Greenspan. Dankzij een dalende loonquote en een ruilvoetverbetering voorziet Van Nieuwkerk een sterkere concurrentiepositie en positieve winstontwikkeling voor het Nederlandse bedrijfsleven. De volgende bijeenkomst van SECO Senior Beleggingskundigen is gepland voor 10 mei. Dan zal André E. Teeuw van Binck Effectenbank N.V. gastheer zijn.

European Institutional Investor Securities Services

Carlo J. Bogerd, secretaris SECO Senior Beleggingskundigen

KAS BANK is een onafhankelijke Europese bank gespecialiseerd in clearing, settlement en custody. Onze neutrale en onafhankelijke positie is uniek binnen de effecten- en bankenwereld. Onze basisdienstverlening bestaat uit bewaarneming, afwikkelen van transacties, beheerhandelingen, belastingterugvordering en de mogelijkheid om interactief uw effectenportefeuilles online, middels diverse dwarsdoorsneden, in te zien. Buiten onze kernactiviteiten bieden wij u ook: • Real-time online custody informatie • Beleggings en Financiële administratie • Treasury & Cash Management • Performance meting & Risico analyse • Securities Lending • Compliance Monitoring • Transactiekosten analyse & Commission Recapture • Global Proxy Voting • Administratie beschikbare premieregelingen

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KAS BANK N.V. is geregistreerd bij de Autoriteit financiele markten in Amsterdam.

11

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The long-term risk and return effects of investing in private equity Abstract Private equity is gaining popularity with Dutch institutional investors. At first sight its characteristics appear very attractive: high returns, low risk and low correlation with public equity and bonds. However, the risk characteristics in particular may be biased by the appraisal-based nature of private equity returns. For a better assessment of the ‘real’ private equity characteristics, we analyze the long term risk and return characteristics of private equity investments using a novel approach for “unsmoothing” reported

Gerben de Zwart, Senior Quantitative Researcher, Quantitative Strategies Department Robeco Group, g.j.de. [email protected] David Blitz, Deputy Head, Quantitative Strategies Department Robeco Group, [email protected] Mikan van Zanten, Partner, Private Equity Department Robeco Group, m.van. [email protected] Brian Frieser, Analyst, Private Equity Department Robeco Group, [email protected]

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private equity valuations. This approach consists of using longer (multi-year) investment horizons when estimating risk characteristics. We find that unadjusted historical private equity returns have generally been superior to those of public equity. Secondly our “unsmoothing” approach leads to higher volatility and correlation estimates, although volatility still turns out to be lower than for public stocks. We conclude that private equity adds value to traditional investment portfolios.

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1.

Introduction

In line with the global trend, Dutch institutional investors are showing increasing interest in alternative investments, especially in hedge funds and private equity. Cumming and Johan (2006) survey the attitude of Dutch institutional investors towards private equity and find that currently 29% of the respondents are investing in private equity, while another 6% of the respondents intends to invest in private equity over the next two to five years. Secondly, the respondents who already invest in private equity expect an increase of the private equity portfolio weight. Currently 44% of these respondents allocate more than 2.5% of their portfolio to private equity while this is expected to be 54% in the future. Despite this enthusiasm there is not much formal research into the long term risk and return characteristics of private equity and its role in a mean-variance context. This study seeks to better understand the impact of adding private equity to the strategic asset mix. We present a novel, transparent approach to adjust the appraisal-based reported private equity valuations, in order to get a better estimation of the ‘real’ private equity volatility and correlation. In addition, most studies concentrate on the US market, while in this study we will include both US and European private equity, as well as the two main private equity sub-classes: buy-out capital and venture capital financing. Earlier studies focus on the role of separate private equity classes in the strategic allocation for US public equity investors. The set-up of each study is different however, which may explain the mixed conclusions: Chen et al. (2002) suggest a 2-9% venture capital allocation, Milner and Vos (2003) recommend a 13% allocation for early stage and 69% for buy-out and Ennis and Sebastian (2005) suggest a 5% allocation for mixed portfolios with more than 60% equity allocations. To our knowledge only two studies, Artus and Teïletche (2004) and Kaserer and Diller (2004), focus on the European market, both concluding that investors should invest 5-10% of their total portfolio in private equity. This study is organized as follows. We introduce the data in section 2. Sections 3 and 4 discuss private equity return and risk characteristics respectively. Section 5 combines the outcomes in a mean-variance framework and, finally, the conclusions are presented in section 6.

2.

Data

In our research we use the Thomson Venture Economics (TVE) data, a Thomson Financial subsidiary, because of the relatively large scope of available data. TVE has gathered private equity data dating back to 1969, using annual reports of private equity funds, personal contacts to funds’ personnel, IPO prospectuses, investor public information such as prospectuses and other media sources. We refer to Kaplan et al. (2002), Kaplan and Schoar (2005) and Phalippou and Zollo (2005) for more information about the method TVE employs to collect the data. TVE claims to cover a large share, as much as 70% of the total private equity market. However, like all providers of private equity data, they have to rely on (voluntary) reporting by investors and fund managers. This may give rise to a self-selection bias in the database, as successful funds may be more likely to report their performance than less successful ones. Furthermore, the historical database changes through time, as the track records of new funds are added (potential back-filling bias). The data might also suffer from survivorship bias, i.e. the exclusion of funds which have ceased to exist. Another issue is that return data provided by the funds is not verified by a third party, although generally the funds’ financial statements are independently audited. We feel comfortable to use the TVE data, as Kaplan and Schoar (2005) acknowledge the selection- and survivorship bias, but do not adjust for it. In a more recent working paper however, Phalippou and Zollo (2005) adjust historical returns in three ways and conclude that the expected performance of private equity funds is 2% lower after correction for sampleselection bias. We conclude that more effort needs to be done on this subject. Due to the private nature of private equity investments, market valuations are not readily available. Investors have to rely on appraisal-based valuations instead, which tend to result in a smoothing effect. As value creation is a multi-year process for private equity, investments are usually kept at book value for up to several years. Subsequent accounting is conservative in the sense that clear justification must be available before an upward revaluation is done, for example a new financing round. On the other hand, a downward revaluation may be implemented more swiftly, e.g. when business prospects are judged to

13

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of all cash-flows equal to zero, but is not readily applicable for asset allocation purposes.

Figure 1: Number of funds in our dataset 3000

2000

1500

1000

500

0 Q2 69 Q2 70 Q2 71 Q2 72 Q2 73 Q2 74 Q2 75 Q2 76 Q2 77 Q2 78 Q2 79 Q2 80 Q2 81 Q2 82 Q2 83 Q2 84 Q2 85 Q2 86 Q2 87 Q2 88 Q2 89 Q2 90 Q2 91 Q2 92 Q2 93 Q2 94 Q2 95 Q2 96 Q2 97 Q2 98 Q2 99 Q2 00 Q2 01 Q2 02 Q2 03 Q2 04 Q2 05

Cumulative number of funds

2500

US buyouts

US venture capital

Europe venture capital

Europe buyouts

have deteriorated. When these smoothing effects are ignored, the estimated volatility of private equity and its correlation with other asset classes may be severely underestimated. In our analysis we employ time-weighted pooled returns as reported by TVE’s VentureXpert website. Pooling refers to the aggregation of cash-flows and valuations over all funds for which data is available. Time-weighted indicates that a return is calculated for each period, based on initial and ending value and intermediate cashflows; these returns are subsequently chain-linked on an equally weighted basis. This approach is useful to discuss private equity performance on an index level. In practice the internal rate of return (IRR) is a more popular return measure for individual private equity funds. The IRR is defined as the rate of return that sets the net present value

The number of funds in our dataset is shown in Figure 1. We observe that data is available from as early as the late sixties, though coverage is very low if we go back more than 20 years. The cumulative number of buy-out and venture capital funds rises steadily to over 2600 currently, with about a third of these being of European origin. The US funds represent a total value of over $600 billion, while the European funds total over €140 billion. Despite the fact that the number of venture capital funds is about twice the number of buyout/mezzanine funds, they represent less than a third of the total value. Return data for other regions, most notably the Pacific, are not available (yet). We will compare US private equity with US stocks and bonds as represented by the S&P 500 and Lehman Aggregate total return indices respectively, all in US dollars. European private equity will be compared with European stocks and bonds (MSCI Europe and JP Morgan EMU aggregate), all in euros. Data for the traditional equity and bond indices have been obtained from Thomson Financial Datastream.

3.

Return of private equity

Table 1 provides an overview of the annualized (timeweighted) historical returns of private equity, as well as those of traditional stock and bond investments. In addition to the aggregate returns on private equity (PE), the returns associated with the venture capital (VC) and buy-outs/mezzanine (BO) sub-styles are also displayed. Figure 2 compares the growth of one dollar (euro) invested twenty years ago in US (European) private equity to public stocks.

Table 1: Annualized historical returns as of 30/6/2005 US Bonds Stocks

Europe

PE

VC

BO

PE

VC

BO

-3y

5,8%

8,3%

7,5%

0,9%

10,0%

Bonds Stocks 8,1%

5,7%

1,1%

-4,5%

4,1%

-5y

7,4%

-2,4%

-1,0%

-9,2%

2,6%

7,4%

-3,6%

2,4%

-2,8%

6,1%

-10y

6,8%

9,9%

13,7%

18,7%

10,2%

7,3%

11,3%

13,6%

10,8%

16,3%

-15y

7,7%

10,7%

14,0%

17,4%

11,6%

8,2%

8,8%

10,8%

8,6%

12,7%

-20y

8,4%

12,3%

15,0%

14,9%

16,6%

7,5%

10,2%

9,7%

7,0%

14,1%

-30y

8,9%

12,3%

17,1%

17,3%

n.a.

n.a.

11,8%

9,0%

n.a.

12,7%

14

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One of the key issues in every asset allocation study is to what extent the historical average nominal return of an asset class is representative for its future expected return. For example, with bond yields having declined to levels of approximately 4% currently, future expected returns on bonds are clearly lower than the historically realized levels of 7-8%.

Figure 2: Growth of one dollar (euro) invested 20 years ago in stocks or private equity 20

16 14 12 10 8 6 4 2 Q2 05

Q2 04

Q2 03

Q2 02

Q2 01

Q2 00

Q2 99

Q2 98

Q2 97

Q2 96

Q2 95

Q2 94

Q2 92

US stocks US aggregate private equity

Q2 93

Q2 91

Q2 89

Q2 90

Q2 87

Q2 88

Q2 85

0 Q2 86

Cumulative value of investment

18

EU stocks EU aggregate private equity

Based on the return data in Table 1, US private equity has clearly outperformed public equity in the long term. European private equity shows mixed results however, outperforming stocks over the past 5, 10 and 15 year period, but underperforming in earlier years (the past 20 and 30 years). This is in line with the findings of Kaserer and Diller (2004). Note that European private equity was still an immature asset class in the early days, with the total number of funds being below 100 until 1989 and even less than 10 until 1983. Table 1 also shows that, in contrast to the US, European buyouts performed better than venture capital over the last 15 and 20 years. A possible explanation for this could be the fact that during the 1980s successful US venture capital business models were directly applied in Europe. Unfortunately venture capital managers learned their lessons as these concepts needed to be adjusted to be used in Europe. In addition, the exit market for VC investments was lacking in Europe. US private equity generated higher returns than public equities during both the bull market of the late nineties and the subsequent bear market. European private equity exhibits less extreme returns during both periods, with the bear market being quite mild in particular. The venture capital sub-style did exceptionally well during the bull market, while buy-outs were more rewarding during the bear market, even managing to achieve positive absolute returns on average during these difficult years.

Theoretically there are good reasons to assume a systematically higher risk premium on private equity than on public stocks. The three main arguments follow from economic theory: a lower market efficiency, a liquidity premium and compensation for higher risk: • Lower market efficiency refers to several important differences between private equity and public equity. In public markets information is abundantly available and distributed quickly to all market participants. The private equity market clearly does not share these characteristics. Well-informed private equity managers can have an informational advantage and may add additional value by being directly involved in comanaging the companies in which they invest. This is confirmed by Kaplan and Schoar (2005) who report strong persistence in fund returns across different funds from the same general partner. Although such market inefficiencies complicate the analysis of private equity, they may actually be an important source of excess returns for investors. • Liquidity in the private equity market is much lower than in public markets and transaction costs are higher. The illiquid nature of private equity warrants a liquidity premium for investors. • Private equity investors can also expect an excess return relative to public stocks because of the inherently riskier nature of the underlying investments. Venture capital investments are known to be more risky than their large-cap counterparts as their business is not well diversified, future cash-flows are relatively uncertain and the probability of failure (bankruptcy) is relatively high. The transaction values of buyout investments are usually larger than that of venture capital investments, and business prospects are associated with less uncertainty. However, buyouts often involve significant debt leverage, which increases risk. As we will see in the next section, quantifying the level of risk associated with private equity is

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not an easy task. Nevertheless, it makes sense to assume a higher inherent risk for private equity than for public stocks, for which investors are rewarded by means of a higher expected return. In practice the excess return of private equity versus public equity may be partly diluted due to higher management costs. Returns for private equity funds in our sample are on an after-cost (net) basis, but investing in these funds usually involves additional costs, such as retaining specialized investment professionals or hiring an external asset manager. For example, the typical management fee of a private equity fund-of-funds is 1%. In practice we see that the largest Dutch institutional investors have their own private equity professionals, while for smaller investors an external manager is economically more efficient. Fund-of-funds managers strive to at least make up for the additional layer of management fees by trying to select superior funds. Arguably, prospects for active management of private equity are relatively good, given the lower market efficiency and persistence in fund returns, as addressed earlier. Active fund selection is in fact both necessary, as no passive investment alternatives exist for private equity, and essential as returns of individual funds exhibit a large dispersion. This dispersion is very large when compared to dispersion in returns of diversified public equity portfolios. Therefore it is worth to put effort in trying to select the best funds and avoid the worst funds. For example, the median return for US private equity was 21% in 1999, but fund returns range from a median of -4% for bottom quartile funds to a median of 95% for top quartile funds.

es discussed in the previous section. The literature on hedge funds suggests that such biases can result in significantly overestimated returns, compared to the returns that would have actually been achieved by investors. Currently there is no consensus if, and if so, to what extent, the private equity dataset we use also suffers from these biases. Phalippou and Zollo (2005) are the first authors that shed light on this issue, penalizing historical returns by -2% due to a sample-selection bias. There is also a lack of consensus regarding private equity returns in the limited number of papers on this subject: Ljungqvist and Richardson (2003) calculate excess-IRRs to the S&P 500 of 5-8% per year, Jones and Rhodes-Kropf (2003) claim positive although not significant excess returns for buyout and venture capital funds over stocks as “the higher returns are commensurate with the factor risks that private equity investors bear”, Kaplan and Schoar (2005) conclude that “average fund returns approximately equal the S&P 500 although substantial heterogeneity across funds exists”, while Phalippou and Zollo (2005) conclude that the returns lag the S&P 500 by -3.3% after a correction for sample bias and “living dead” investments. These return differences can be explained by different data sources, time periods, data-samples or data adjustments. Because of this ambiguity we will use different scenarios for the expected return of private equity in the remainder of this paper, instead of trying to make one best guess.

4. Our data suggests that private equity indeed offers a higher long term average return than public stocks, although admittedly the European data is not totally unambiguous in this respect. We should also bear in mind that the data may suffer from the kind of bias-

Risk of private equity

A. Volatility In Table 2 we calculate annualized volatilities on quarterly as well as (overlapping) annually, bi-annually and tri-annually (log-transformed) returns. The

Table 2: Annualized past 20 year volatilities as of 30/6/2005 US Bonds Stocks

PE

Europe VC

BO

Bonds Stocks

PE

VC

BO

quarterly data

4,6%

16,5%

9,6%

16,2%

9,8%

3,9% 20,7%

8,5%

9,0%

9,9%

annual data

4,7%

16,3%

14,3%

26,5%

12,4%

4,9% 20,2%

11,7%

12,8%

12,7%

bi-annual data

3,8%

17,5%

16,1%

30,3%

15,0%

4,7% 20,9%

14,2%

16,2%

15,5%

tri-annual data

3,5%

18,6%

16,4% 30,6%

16,5%

4,6%

15,3%

17,8%

16,3%

16

21,6%

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more appropriate. Measuring volatilities over longer investment horizons is a simple yet effective way to accomplish this. The differences between short term and long term volatilities find their root in the appraisal-based valuations on which we are forced to rely due to absence of frequent mark-to-market valuations.

Figure 3: Quarterly returns of US aggregated private equity 30% 25%

15% 10% 5% 0% -5% -10%

Q2 05

Q2 03

Q2 04

Q2 01

Q2 02

Q2 00

Q2 98

Q2 99

Q2 97

Q2 95

Q2 96

Q2 94

Q2 93

Q2 91

Q2 92

Q2 90

Q2 89

Q2 87

Q2 88

Q2 85

-15% Q2 86

Quarterly return

20%

volatility of quarterly returns is remarkably low, in particular in comparison to public stocks. However, as the investment horizon is lengthened, volatility is observed to increase significantly. Nevertheless, even at a three year horizon the levels resulting for aggregated private equity are still below those of public stocks. Our finding that volatility increases as the investment horizon is extended is not surprising, as quarterly returns exhibit significant positive serial correlation. On a quarterly basis, positive returns tend to be followed by more positive returns, and negative returns by more negative returns. This behavior is confirmed by a visual assessment of the development of quarterly returns over time, as shown in Figure 3. The positive serial correlation implies that long term risk is higher than short term figures may suggest at first sight. As we are interested in the long term value-add of private equity, risk statistics that are adjusted for short term serial correlations are

Similar characteristics of appraisal-based returns have been documented for other non-listed asset classes, most notably direct real estate. In fact, the real estate literature provides additional inspiration for unsmoothing appraisal-based valuations. For example, Pagliari et al. (2003) find that the application of an unsmoothing methodology to annual direct real estate returns increases the estimated annualized volatility from 5.20% to 8.59%, i.e. over 65% in relative terms.

B. Correlation Using a longer investment horizon does not only affect estimated volatility levels. Table 3 shows that the estimated correlation between private equity and stocks increase sharply as we lengthen the investment horizon. In other words, the correlation with stocks is also higher than it appears initially. This is in line with the findings of Emery (2003), Artus and Teitletche (2004) and Kaserer and Diller (2004), who use different correction methods but reach the same conclusion. Nevertheless, diversification benefits exist, in particular for European private equity. A final advantage of focusing on longer holding periods is that the return data exhibits considerably less non-normality. Statistically significant positive skewness (asymmetry) and excess kurtosis (fat tails) is observed for quarterly returns, but these effects diminish rapidly at longer horizons. The more closely the data resemble a normal distribution, the more

Table 3: Past 20 year correlations with stocks as of 30/6/2005 US Bonds Stocks

Europe

PE

VC

BO

Bonds Stocks

PE

VC

BO

quarterly data

-0,03

1,00

0,49

0,42

0,34

-0,14

1,00

0,37

0,27

0,35

annual data

0,05

1,00

0,63

0,49

0,61

-0,06

1,00

0,52

0,50

0,45

bi-annual data

-0,04

1,00

0,73

0,56

0,66

0,03

1,00

0,65

0,68

0,52

tri-annual data

0,01

1,00

0,80

0,61

0,69

0,14

1,00

0,74

0,77

0,62

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valid it is to apply mean/variance for assessing the attractiveness of a specific allocation. •

5.

Mean/variance analysis

In the preceding sections we separately discussed the return and the risk associated with private equity. At this point we combine these factors by analyzing consequences for both risk and return if private equity is added to an existing equities and bond portfolio. This is done by applying a mean/variance approach to scenarios with varying risk and return characteristics of private equity. The following assumptions are used in each scenario and are commonly used in ALM studies in practice: • 3% expected annual risk premium on equities over bonds. • 20% risk (annual volatility) of stocks and 5% risk of bonds • Zero correlation with bonds for both public stocks and private equity The zero correlation assumption is conservative for private equity, as historical correlation with bonds has in fact been slightly negative. In the scenarios for private equity we distinguish between: • A 3% or 5% annual risk premium on private equity over bonds. The 5% scenario is consistent with unadjusted historical data and with the expectations of respondents in the Cumming and Johan (2006) Figure 4: Mean/variance analysis results 3,5%

Expected risk premium

3,0% 2,5% 2,0% 1,5% 1,0% 0,5% 0,0% 3%

6% No PE

9% Scenario I

18

12% Risk Scenario II

15% Scenario III

18% Scenario IV

21%

survey. The 3% scenario reflects identical return expectations for private equity and public equity. An 18% or 27% risk (annual volatility) for private equity. The 18% volatility (and 0.8 correlation with stocks) are the maximum risk and correlation levels implied by historical data. We also include a scenario in which private equity is assumed to be significantly riskier than public equity (which tends to be the consensus among investors in practice), consisting of a subjectively chosen 27% volatility (and 0.9 correlation with stocks).

The four resulting scenarios are summarized in Table 4. Scenario I was designed to be most consistent with the historical data discussed in previous sections. The other scenarios represent more conservative assumptions regarding private equity. Table 4: Private equity scenario definitions Scenario

I

II

III

IV

Risk premium

5%

3%

5%

3%

18%

18%

27%

27%

0,8

0,8

0,9

0,9

Volatility Correlation

In our mean/variance analysis we compare the efficient frontier of portfolios consisting only of stocks and bonds with the one in which a fixed allocation to private equity of 10% is included. Substantially higher allocations may in fact be optimal from a statistical point of view, but as most institutional investors will not consider these to be a realistic alternative in practice, we restrict our attention to private equity allocations of 10%. The resulting efficient frontiers are shown in Figure 4. Not surprisingly, we find strong evidence of added value in Scenario 1. Based on this scenario the expected risk premium is enhanced by about 0.25% for the same level of risk, or, alternatively, risk can be reduced by about 2% whilst preserving the expected return level. At least as important is the finding that even assuming the same risk premium as on stocks (Scenario II) or a significantly increased risk level (Scenario III), an allocation to private equity still adds value. Only in case both these assumptions are used (Scenario IV) do we find a deterioration of the efficient frontier.

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6.

Conclusion

Private equity is gaining popularity with Dutch institutional investors, but research in this area is still relatively scarce. In this paper we analyzed risk and return characteristics with a novel approach, which unsmoothes the reported private equity valuations for a better estimation of the private equity characteristics that long term investors might experience in their portfolios. Using aggregated data from Thomson Venture Economics we find that US private equity returns have generally been superior to those of public stocks. It is unclear to which extent the TVE data suffers from certain potential biases, but even after a downward adjustment of average returns by several percentage points, private equity returns would still exceed, or at least be comparable to public stock returns. European private equity outperformed public stocks over the past 15 years, except in earlier years, when, arguably, the number of funds was still small, indicating an immature market. We argue that superior returns are in line with general economic theory, as being caused by lower market efficiency, illiquidity and compensation for higher risk. Using volatility as a risk measure we find a lower risk for both European and US private equity compared to public stocks. After correcting the appraisalbased returns, by lengthening the investment horizon up to three years, the estimated risk levels rise, although they remain lower than for public stocks. This can be explained by positive serial correlation in short term returns, which are most likely the result of the appraisal-based valuations that are inherent to private equity. We conclude that the higher risk estimates, following from longer horizon returns, are more reflective of the “true” amount of risk involved. For the correlation between private equity and public equity we also find higher values after applying our unsmoothing approach. We combined risk and return characteristics in four mean/variance scenarios and found that private equity adds value to a traditional equity and bond portfolio. Only under extreme assumptions, private equity would not add value to this traditional portfolio.

References Artus, P. & Teïletche, J. 2004, ‘Asset allocation and European private equity: a first approach using aggregated data’, in ‘Performance Measurement and Asset Allocation for European Private Equity Funds’, Research Paper, European Private Equity & Venture Capital Association (EVCA) Chen, P., Baierl, G.T. & Kaplan, P.D. 2002, ‘Venture Capital and its Role in Strategic Asset Allocation’, Journal of Portfolio Management, Winter, vol. 28(2), 83-89 Cumming, D.J. & Johan, S.A. 2006, ‘Regulatory Harmonization and the Development of Private Equity Markets’, Working Paper, http://ssrn.com/abstract=842964 Emery, K. 2003, ‘Private Equity Risk and Reward: Assessing the Stale Pricing Problem’, Journal of Private Equity, Spring, vol. 6(2), 43-50 Ennis, R.M. & Sebastian, M.D. 2005, ‘Asset Allocation with Private Equity’, Journal of Private Equity, Summer, vol. 8(3), 81-87 Jones, C.M. & Rhodes-Kropf, M. 2003, ‘The Price of Diversifiable Risk in Venture Capital and Private Equity’, Working Paper, Columbia University Kaserer, C. & Diller, C. 2004, ‘European private equity funds – a cash flow based performance analysis’, in ‘Performance Measurement and Asset Allocation for European Private Equity Funds’, Research Paper, European Private Equity & Venture Capital Association (EVCA) Kaplan, S.N. & Schoar, A. 2005, ‘Private Equity Performance: Returns, Persistence and Capital Flows’, Journal of Finance, vol. 60(4), 1791-1823 Kaplan, S.N., Sensoy, B.A., & Strömberg, P. 2002, ‘How Well Do Venture Capital Databases Reflect Actual Investments?’, Working Paper, University of Chicago Ljungqvist, A. & Richardson, M.P. 2003, ‘The Cash Flow, Return and Risk Characteristics of Private Equity’, Finance Working Paper No. 03-001, New York University Milner, F. & Vos, E. 2003, ‘Private Equity: A Portfolio Approach’, Journal of Alternative Investments, Spring, vol. 5(4), 51-65 Pagliari, J.L., Scherer K.A. & Monopoli R.T. 2003, ‘Public versus Private Real Estate Equities’, Journal of Portfolio Management, Special Issue, 101-111 Phalippou, L. & Zollo, M. 2005, ‘Performance of Private Equity Funds: Another Puzzle?’, EFA 2005 Moscow Meetings Paper, http://ssrn.com/abstract=473221

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Sources of Hedge Fund Risk and Return Introduction During the last couple of years, the hedge fund industry has experienced rapid growth. As a result of positive returns and strong inflows from both private and especially institutional investors, assets under management have reached an estimated USD 1.1 trillion (Tremont 2005). While this number is still relatively small compared to the overall size of financial markets, the presence of hedge funds is increasingly felt. The growth in assets as well as market influence has raised attention from journalists, regulators, and even politicians. However, due to the relatively intransparant nature of hedge funds and a few well known, often quoted hedge fund liquidations1, the risk profile of hedge funds is generally poorly understood. Academic research on the subject is still in its infancy and a growing number of articles should be

Drs. A.J. van Stijn RBA (l), Senior Hedge Fund Analyst & Portfolio Manager Fortis Multi Management Alternative Investments sander.van.stijn@ fmm.fortis.com Drs. M.J. Geene RBA (r), Senior Hedge Fund Analyst & Portfolio Manager Fortis Multi Management Alternative Investments mark.geene@ fmm.fortis.com

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expected. Yet, as hedge funds are becoming a larger part of investors’ portfolios, a thorough understanding of hedge fund risk/return profiles is imperative. Even more as it impacts every part of the investment process: strategic asset allocation, portfolio construction, risk management and manager selection. Therefore this article will aim to provide an overview of and conceptual framework for the most frequently cited sources of hedge fund risk and return. This article will start with briefly explaining hedge funds and the various investment processes employed. The next paragraph will introduce the major hedge fund risk categories, being investment, credit, liquidity and operational risk, after which the remainder of the article will be used to dig deeper into the investment risk factors that are most com-

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– – –

Fundamental versus technical analysis Trading oriented versus investment oriented Quantitative versus qualitative decision making – Highly diversified versus concentrated portfolios – Significant directional exposures versus tightly controlled and neutralized market exposures – The amount of leverage used (if any) Investment universe: – Asset category and geography: some funds invest only in a few sectors in one country while other funds invest globally in a wide range of financial markets – Instruments: some funds only use traditional instruments, while others combine these with (exotic) derivatives Risk management process and procedures Terms and conditions: there are wide ranges of fee and liquidity conditions Staffing: some funds are run by a limited number of individuals while others have hundreds of employees

mon to hedge funds. These risk factors will be illustrated for two strategies: long short equity as it is the largest strategy in terms of AUM and convertible arbitrage due to its diversity in risk exposures. The conclusion will discuss the main findings.

Hedge Funds Hedge funds are lightly regulated investment funds that usually have limited investment constraints. They can short securities, use leverage and are very flexible with respect to the use of instruments, which might even include exotic derivatives. Another feature is the alignment of interest with investors due to the fee structure, as hedge funds usually charge a 20% performance fee. Hedge funds generally do not focus on a benchmark and aim to achieve absolute returns instead. However, while many market participants still claim that most hedge funds are market neutral and therefore uncorrelated to other asset classes2, research indicates that they, both individually and on aggregate, can have significant exposures to market factors such as equity, credit and interest rates, as well as somewhat more exotic factors. As hedge funds have very diverse investment processes and differing investment universes, risk factors and their impact can differ significantly from fund to fund, even within the same strategy group3. Hedge funds are very heterogeneous with respect to all of the following aspects: • Investment philosophy, process and portfolio construction:

•

• • •

This heterogeneity is further amplified by the growth in the number of funds, the increasing number of financial markets they operate in (e.g. chasing new opportunities such as direct lending, life insurance and private equity) and the growing number of different financial securities and derivatives available.

Major risk categories Figure 1: Major risk categories4 Operational Risk

Operational Risk Investment Risk

Asset Liquidity

Credit Risk

Funding Liquidity Liquidity Risk

Operational Risk

Credit risk associated with investments

When investing in hedge funds an investor is not only exposed to investment risks, but to credit risk, liquidity risk and operational risk as well. For traditional long only funds it is current practice to analyze these risks in isolation as they are generally perceived not to be interrelated. For hedge funds however these risks are highly interrelated and should therefore be monitored in combination. This paragraph will describe these major risk categories and how they are interconnected (see also figure 1).

Credit risk associated with counterparties

Operational Risk

The major risk categories for hedge funds are: • Investment risk relates to sensitivities towards changes in market factors such as equity markets, credit markets or the level of volatility. It relates to fluctuations in overall markets as well as individual securities. These are the primary

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risks most investors are focused on and generally carry a risk premium, i.e. a return can theoretically be expected in the long run. Investment risks will be covered more thoroughly in the next paragraph. Credit risk comes in two forms. It relates to potential losses due to changes in the credit quality of the investments of the fund (which in this article will be considered investment credit risk) and it relates to potential losses caused by the inability of trading counterparties to perform its obligations (counterparty credit risk). By identifying acceptable counterparties and using a set of appropriate exposure limits and collateral agreements, counterparty credit risk exposures5 can generally be controlled. Liquidity risk relates to losses due to declines in market liquidity (also known as asset liquidity, which in this article will be considered to be an investment risk) or to the ability of the fund to fund its investments (funding liquidity). Funding liquidity risk refers to the inability to meet payment obligations such as investor redemptions and margin calls, which may force early liquidation of positions. Asset and funding liquidity are highly interrelated. Liquidity management, in the form of asset liability management and specific provisions in the prospectus and prime brokerage agreements are therefore very important. Strategies that employ a large amount of leverage combined with a large short book generally have the largest exposure to funding liquidity risk. Operational risk relates to losses due to problems in the day to day operations of a fund. Operational risk is probably one of the most underestimated risks of hedge fund investing. Although risk analysis usually focuses on market risks, operational risk appears to be the largest risk factor within a hedge fund investment. Research of Capco (2003) indicated that approximately 50% of all hedge fund failures are solely due to operational issues, such as misrepresentation and misappropriation of investor’s funds, outright fraud, inability to manage a business or a combination of these. However, at the same time operational risk is probably the most difficult to quantify.

•

•

•

22

As mentioned earlier, in managing hedge funds it is important to realize that these four categories are highly interrelated and should thus be evaluated in combination. The reason for this is twofold: (1) the use of short selling and (2) the use of leverage. In shorting a security one first needs to borrow the underlying security from someone else, and the borrower needs to place a margin with the lender (usually the short selling proceeds). However, the lender often reserves the right to recall the security, or to increase the margin when he feels necessary (both usually during times when the position turns against the manager). Here, investment risk creates both counterparty and funding liquidity risk for the fund. When a fund is fully invested, a manager has to liquidate some of his positions to be able to perform his obligations. When his positions are illiquid, the forced liquidation can lead to substantial losses as the positions need to be sold at stressed prices (asset liquidity risk). Again, it is the relation between these risk factors in which hedge funds differ from traditional long only funds. Additionally, operational risks are usually smaller for long only funds than for hedge funds as they are more regulated, invest mainly in traditional securities as opposed to complicated instruments, provide more transparency and do not use leverage or short selling. Equally important, most hedge funds tend to be smaller organizations supported by smaller operational infrastructures. Operational, counterparty and funding liquidity risk are sources of risk that generally do not have an expected return, but can occasionally result in very large losses. Therefore practically speaking these risks should be minimized by well thought-out procedures and processes. As operational, counterparty and funding liquidity risks are much less quantifiable than investment risks, the remainder of this article will focus on the specific investment risks of hedge funds. However, when constructing and managing a hedge fund portfolio the investment risks should be weighted appropriately and evaluated in tandem with operational, (counterparty) credit and (funding) liquidity risks.

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Sources of investment risks and return While hedge fund heterogeneity makes it very difficult to generalize with respect to risk profiles, investors need a conceptual framework for these risks at every stage of the investment process. An example of such a framework is provided below. Research on the classification and segmentation of hedge fund returns is still developing and not much academic research is available on the topic. Up to now, research on hedge funds has predominantly focused on database bias properties and hedge fund return distributions while only recently academics have started analyzing hedge fund risk exposures6. However, only few articles explain in greater detail the realized and prospective hedge fund risk and

Figure 2: Disentangling hedge fund returns HEDGE FUND RETURNS = PASSIVE Cash

ACTIVE

+ Traditional beta + Alternative beta + Structural alpha + • Equity risk premium • Interest rates • Credit spreads • Commodities • FX

• • • • • •

Liquidity Value vs Growth Small vs Large Volatility Correlation Corporate Event Risk • Convexity • Risk Transfer Premiums

• Regulatory constraints • Structural inefficiencies • Incentives • Flexibility

Skill alpha SKILL

Figure 3: Beta of L/S equity to MSCI World, Beta of Distressed to US HY Yield (36 month rolling)9 2,0 1,8 1,6 1,4 1,2 1,0 0,8 0,6 0,4 0,2 dec-96 sep-97 jun-98 mrt-99 dec-99 sep-00 jun-01 mrt-02 dec-02 sep-03 jun-04 mrt-05 dec-05 Beta L/S Equity Benchmark & MSCI World Beta Hedgefund Distressed Benchmark & US High Yield

return characteristics in a coherent and consistent (theoretical) framework. Such a framework would be much welcomed by (prospective) investors in hedge funds, regulators and other interested parties. In classifying the diverse set of sources of hedge fund risks and returns this article is in some degree based upon Gehin and Vaissië (2005) and Harcourt (2004) (figure 2). It should be noted that the classification of sources of risk and return is somewhat arbitrary and should, in fact, be viewed as a continuum between traditional beta and alpha7. Investors can, and frequently do, have differing opinions on the classification of the different sources of risk. The main purpose for now is to give an overview of the major risk factors hedge funds are exposed to and to illustrate that hedge funds are not just skill based pure alpha generators as most aim to profit from multiple market risks premia as well. As a starting point, risks can be divided into passive and active risk exposures. Passive exposures relate to the average exposures to certain risk factors, such as equity markets or volatility for instance, in contrast to active exposures which arise from ‘tactical asset allocation’ or timing and individual stock picking. The passive risks can be further divided into traditional and alternative beta. Active risk on the other hand can be divided into structural alpha and pure alpha, which will be explained in more detail below. Traditional beta: These are average directional risk exposures towards equities, interest rates, credits, commodities and currencies and can be bought passively, both cheaply and easily. On average, equity long short hedge funds run a modest long equity bias towards the markets they invest in. Nevertheless, these equity betas can differ significantly between managers ranging from –0.8 (dedicated short bias) to larger than one. Distressed hedge funds generally run a long credit beta and therefore are exposed to the gyrations of the credit markets. Figure 3 represents the beta of indices for long short equity and distressed funds based on 36 months rolling regressions.8 The figure shows the overall net long exposure and illustrates its variability over time. The expected return on this part of the return equation can be based upon the long term expected return of the asset class times the exposure.

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Alternative beta: Alternative betas consist of passive risk exposures to specific characteristics of financial markets or instruments. These risk premia can be derived from a static long short exposure (profiting from spread relations) or a directional long or short exposure (profiting from specific supply/demand characteristics). Examples of alternative betas are:10 • Liquidity: A distressed manager investing in very illiquid high yield bonds or defaulted securities will over time earn a liquidity spread. A traditional long only fund will also be able to capture this alternative beta but to a lesser extent as he cannot manage his funding risk by using gates and strict redemption terms.11 • Style factors such as the value / growth or smallcap / largecap spreads. • Higher order risk factors such as volatility, convexity and correlation risks • Event risks and direct or indirect insurance risks: – Merger deal risk is an example of a directional static long short exposure. A merger arbitrage manager is usually long the stock of the acquired company and short the stock of the acquirer. As a result he is exposed to deal risk. When risk aversion increases merger spreads usually widen12 and hedge funds incur losses and vice versa. The merger deal spread is a premium for running this general market risk as well as the specific risk of the merger (alpha). – Insurance risks: hedge funds are willing to take the opposite side of the transaction of a hedger, i.e. provide insurance. They will invest if they have an opposite outlook on the prices of the instruments and/or when they expect to earn a structural premium.

of interest. They have a bigger opportunity set and alpha will therefore be easier to capture (provided that the manager has skill). The fundamental law of active management and its extension with the transfer coefficient (Clarke 2002) serves as one of the theoretical foundations for this argument. Clarke showed that managers can increase their information ratio by reducing investment constraints as this enables one to include a larger number of independent bets. It is important to realize that skill is required to generate these returns.

Alternative betas are static exposures towards these sources of risk. The expected returns (i.e. risk premia) on these could be captured using a more or less passive investment approach. However in contrast to traditional betas, investing in alternative beta exposures will generally be less easy and therefore more expensive.

The structural alpha comes from three related sources: • Regulatory and investment constraints: Hedge funds can short securities and leverage the investment portfolio. As a result they can also profit from finding overvalued securities and can leverage relatively small mis-pricings. The investment universe of hedge funds is virtually unlimited and therefore they can invest in securities that long only investors are generally forced to sell, such as defaulted securities, for instance. • Structural inefficiencies: Hedge funds have the flexibility to invest in securities which are not part of their normal investment universe and are therefore able to profit from structural inefficiencies between financial markets. An example is a long short equity investor that invests in a basket of oil companies which he thinks are undervalued because earnings estimates are still based on a low oil price. In contrast to traditional long only funds, he will be able to hedge the oil price risk by shorting a oil futures strip. • Flexibility and incentives: Hedge funds usually have smaller teams and more flexible investment processes than traditional long only funds. As a result, they are able to respond much quicker to a changing opportunity set. In addition, the hedge fund incentive structure results in a strong alignment of interest, i.e. focus on performance as opposed to growth in AUM.

Structural alpha: This part of hedge fund returns is facilitated by the specific hedge fund structure. Hedge funds have a superior set up compared to traditional long only funds as they have fewer investment constraints and a stronger alignment

Pure alpha: The last source of risk and return for hedge funds is the alpha component derived from the investment skills of the manager. This is a significant part of the overall portfolio risk, however at the same time it is the most difficult part of the return

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to generate and evaluate. Only a limited number of managers (long only and hedge fund managers) are able to consistently select the right securities at the right time. The alpha is generated because of superior skills in analyzing individual companies and securities, market savyness and/or superior portfolio and risk management skill and systems. One can argue that theoretically hedge funds offer better access to skill than traditional long only funds, as managers having skill are likely to set up a hedge fund. This will facilitate them in generating skill from both the long and the short side. More importantly, they will be able to earn far more money through the hedge fund incentive fee structure than they could at a traditional long only firm.

Analyzing hedge fund risk factors In order to properly evaluate any investment fund and construct a well balanced portfolio it is imperative that one gains a fundamental understanding of the various risks involved. A pure quantitative approach to analyzing hedge fund risks has significant pitfalls. Due to the frequent use of nonlinear financial instruments, the dynamic nature of trading strategies and the limited amount of (high quality) data, risk exposures and return distributions are difficult to estimate. Therefore a qualitative analysis with sound economic reasoning should always be leading.

and should be analyzed in tandem. Investment risks can either be passive exposures to traditional markets such as equity and bond markets or alternative risk factors such as liquidity and insurance risks. The active part of hedge fund risk can conceptually be divided into the structural alpha resulting from the advantages of a hedge fund structure, and the pure alpha component resulting from the skill of the manager. While the classification is theoretically appealing, how to categorize certain risk factors is subject to debate. Further difficulties arise in quantifying the return on various risk factors and more importantly the manager’s exposure towards these, as for most risk sources widely accepted benchmarks are not available. The lack of transparency of most managers and the diversity in risk reporting further complicates mathematical representations of hedge fund risks and returns. The first steps have been taken to explain and calculate hedge fund risk and returns. More effort is needed to further reduce the stubborn misperceptions about hedge fund investing. Quantitatively assessing risk and return is appealing. However, due to the flexible and dynamic nature of most hedge fund investment styles and the effect of less quantifiable risks such analysis should always be supplemented by a thorough qualitative assessment.

In the boxes the most important quantifiable risks involved and their dynamics through time will be illustrated for two strategies: long short equity and convertible arbitrage. For each of these strategies one fund will be profiled. The analysis is complemented with some basic regression analytics to illustrate the size and dynamics of the most common risk exposures. To display manager diversity, risk profiles of the strategy index and some additional funds13 are included.

Conclusion The eight to ten thousand hedge funds estimated to be operational all have very distinctive risk return profiles. Although the objective is to achieve absolute returns regardless of the direction of financial markets and deriving alpha from pure skill, a hedge fund investor is usually exposed to a wide range of different risks. These risks can be categorized into investment, credit, liquidity and operational risk

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Box 1: Long short equity Manager 1 is a fundamental value oriented long short equity manager. His strategy entails buying equity of undervalued stocks and selling short overvalued stocks, with a bias towards companies which are badly run by management. In realizing value, the hedge fund manager does not shy away from a healthy dose of shareholder activism to enforce appropriate changes in, for example, dividend policy, company strategy or even in board composition. The fund is approximately USD 3 billion in size and has grown considerably over the last couple of years through strong performance and new inflows. Although quite dynamic in managing its net exposure, the fund generally maintains a net long exposure of 30% to 80% (with spikes of over 100%), as a result of bottom up valuation considerations. As many other long short managers the manager finds most of his opportunities within the less researched and less liquid smallcap universe and therefore one can expect the fund to have a smallcap bias. These exposures are confirmed by a basic regression analysis (figure 5). The fund has statistically significant exposures

Figure 4: Disentangling long short equity returns LONG SHORT EQUITY RETURNS = PASSIVE Cash

ACTIVE

+ Traditional beta + Alternative beta + Structural alpha + • Equity risk premium • FX

• • • •

Liquidity Value vs Growth Small vs Large Corporate Event Risk


Skill alpha SKILL

Figure 5: Regression Statistics Manager 1 vs. CSFB Long Short Equity Index (Dec 1996 – Nov 2005): RManager 1

=

RF

R2adj = 31%

+ MSCIW + VMG + SML + α 0.42 0.24 0.36 1.10 (5.49) (2.96) (4.94) (3.59)

21.6%

= 3.9% +

RCSFB LSE

=

RF

R2adj = 63% 12.3%

+ 0.5% + 0.8% + 13.2%

+ MSCIW + VMG + SML + α 0.38 -0.17 0.33 0.43 (7.86) (3.34) (7.23) (2.20)

= 3.9% +

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3.2%

2.9%

+ -0.4% + 0.7% + 5.2%

to the MSCI world equity index (MSCIW) and to the value minus growth (VMG) and small minus large (SML) style factors. Except for the value bias, these exposures are broadly in line with those of the long short index. The smallcap bias is an example of an exposure which is inherent to the investment process of most long short managers. Since the smallcap universe is usually less well served in terms of research coverage, pricing inefficiencies usually occur here. However, as smallcap stocks are more difficult to borrow at a reasonable cost (as they are generally less liquid), it is very hard to successfully short within this universe. Therefore, most managers generally resort to the much more liquid largecap stocks which are easier and much cheaper to borrow and as a result easier to short. The resulting portfolio is long smallcaps and short largecaps. Although the unexplained part of the regression (α) is usually associated with pure skill, it is in fact a combination of: (i) omitted (alternative) investment risk factors (ii) structural alpha (iii) skill alpha Many of the alternative investment risks are rather difficult to quantify. As a result, the unexplained part of the regression will include (alternative) risk premia from factors omitted from the regression model. However, a bottom up qualitative assessment of the manager reveals that the portfolio is also exposed to the following (traditional & alternative) investment risks: • Liquidity risk: although the quantitative analysis did not reveal any autocorrelation in the return series (which is frequently used as an indication for stale pricing and therefore liquidity risk) the manager does invest part of his portfolio in relatively illiquid smallcaps and is therefore also prone to liquidity risk as well. • Event risk: due to the event driven approach the portfolio is somewhat exposed to event risk, i.e. the systematic risk that several corporate transactions are cancelled in reaction to changing market conditions. • Industry exposure: like many hedge funds the fund can have significant industry bets, as illustrated by the recent energy exposure. • Geographical exposures: as the manager is increasingly finding new opportunities in e.g. India this essentially adds emerging market risk to the portfolio. A regression equation by definition classifies returns earned from a net exposure to specified risk factors as beta. One should realize

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Figure 6: Regression betas 1994 – 2005 (rolling window analysis) MSCI WORLD EXPOSURES

1,0 0,8 0,6 0,4 0,2 0,0 -0,2 -0,4 -0,6

1,8 1,6 1,4 1,2 1,0 0,8 0,6 0,4 0,2 0,0 -0,2 -0,4 -0,6 INDEX

AVERAGE

MAN 1

MAX

MIN

MAN 2

SMALL - LARGE EXPOSURES

VALUE - GROWTH EXPOSURES

0,8 0,6 0,4 0,2 0,0 -0,2 -0,4 -0,6 -0,8 MAN 3

INDEX

MAN 1

MAN 2

MAN 3

INDEX

MAN 1

MAN 2

MAN 3

MED

Figure 7: Undiversified VAR (=exposure to risk factors * STDEV of risk factor) LONG SHORT INDEX

MANAGER 1 EQUITY

EQUITY

SML

EQUITY

25%

25%

25%

25%

20%

20%

20%

20%

15%

15%

15%

15%

10%

10%

10%

10%

5%

5%

5%

5%

0%

0%

0%

0%

VMG

AVERAGE

MANAGER 3

MANAGER 2 EQUITY

SML

VMG

SML

VMG

SML

VMG

MAX 36 MONTHS

however that this net exposure might actually have been the result of bottom up valuation considerations and therefore an active bet. The resulting payoff would therefore be classified as alpha by most investors although picked up as beta by regression analysis. By discussing the reasons for the net exposure with the manager, one should be able to get a clearer picture on the drivers of return, i.e. beta or skill. On top of this all, as manager 1 frequently changes his portfolio composition, including his net long bias, these exposures will be far from stable. Obviously, the best way to evaluate the variability in risk exposures is to directly evaluate changes in portfolio composition, i.e. by means of a holdings based analysis. However, as hedge funds do not always provide such levels of transparency additional insight can be gained through the use of regression analysis. Such an analysis can also provide a valuable crosscheck on exposure information provided by the manager against potential misrepresentation. In general, although a quantitative analysis can be a helpful addition in evaluating a fund, a thorough qualitative bottom up analysis is imperative for making well informed investment decisions.

Although manager 1 exhibits exposures which are broadly in line with those of the index, this is not necessarily representative for other managers. As noted earlier in this article, the hedge fund universe is very diverse and even funds within the same strategy generally exhibit low cross correlations. This heterogeneity is demonstrated by the following charts where the profiles of two additional funds are added. These charts also illustrate the dynamic nature of hedge fund risk exposures. Multiplying the regression betas with the volatility of the risk factors enables the comparison of the importance of the various risks to the fund (figure 7). Many hedge fund investors still predominantly focus on the equity beta of long short managers. However, figure 7 illustrates that for some managers value vs growth or small vs large can be a much larger risk factor in terms of value at risk.14 Therefore what would otherwise have appeared to be an uncorrelated investment from an equity exposure point of view would have turned out to be highly correlated during a style rotation.

27

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Box 2: Convertible Arbitrage Obviously the risk factors a hedge fund can be exposed to depend to a large extent on the instruments used. Therefore convertible arbitrage managers tend to have exposures very different from long short equity managers as will be illustrated here. Convertible securities are equivalent to a corporate bond and a call option on the stock of the issuer. The pricing relationship between the convertible bond and the stock can be quite complex (depending on the complexity of the convertible) and market pricing is often not completely efficient for this combination. Usually, the strategy consists of buying (undervalued) convertibles and shorting the corresponding stock. By shorting the stock, the manager hedges at least part of the equity risk of the convertible. Depending on the delta of the convertible, the bond is also exposed to credit risk, interest rate risk and through the option component, volatility risk. As a large part of the convertible bond universe is rather illiquid, liquidity risk can be a major factor.

Manager 1 is one of the larger players within the convertible universe. Although convertible arbitrage is the biggest part of the fund, the manager can also invest in equities as well as in other credits. Additionally, the manager runs his fund somewhat from a global macro point of view. Unlike most convertible arbitrage managers which tend to neutralize against equity markets and interest rates, manager 1 dynamically manages these exposures and does not shy away from taking significant directional bets. In addition to investments in developed markets such as US, Europe and Japan, the fund has some exposure to emerging markets and employs a merger arbitrage strategy. Due to the frequent (sizeable) directional bets one would obviously expect significant and dynamic exposures to equities, credits and rates, but potentially to emerging markets as well. As noted earlier, due to the liquidity profile of the convertible universe, liquidity is also an expected risk factor. Liquidity risk can be evaluated by analyzing the amount of autocorrelation in a fund’s return series or including lagged variables in a regression equation.15

Figure 8: Disentangling convertible arbitrage returns

Unlike its peer group, manager 1 has had a considerable equity exposure over the analyzed period. Although at first sight there does not appear to be any significant credit exposure, the fund does have a significant exposure to the lagged US High Yield index, indicating positions in illiquid corporate bonds. Additionally, the fund has been long volatility as illustrated by the exposure to changes in the VIX index. The unexplained part of the regression is again a combination of omitted risk factors, structural alpha and skill alpha.

CONVERTIBLE ARBITRAGE RETURNS = PASSIVE Cash

ACTIVE

+ Traditional beta + Alternative beta + Structural alpha + • Equity risk premium • Interest rates • Credit spreads • FX

• Liquidity • Volatility • Corporate Event Risk • Convexity


Skill alpha SKILL

Figure 9: Regression Statistics Manager 1 vs. Convertible Arbitrage Index (Jan 1998 – Nov 2005): RManager 1

=

RF

R2adj = 11%

+ MSCIW + 0.47 (3.39)

USHY -0.05 (0.29)

+ USHYt-1 + USGOV10yr + 0.35 0.09 (2.12) (0.47)

dVIX 0.32 (2.43)

+

α 0.84 (2.15)

+

-0.3%

+

+

-0.5%

+

10.1%

+ MSCIW + -0.02 (0.30)

USHY 0.27 (3.80)

+ USHYt-1 + USGOV10yr + 0.14 -0.08 (1.97) (1.08)

dVIX 0.04 (0.76)

+

α 0.22 (1.47)

1.4%

+

-0.1%

+

2.5%

18.1%

= 3.7% +

RCSFB CA

=

R2adj = 19% 7.8%

RF

= 3.7% +

28

2.8%

-0.1%

+

1.8%

0.7%

+

+

0.4%

-0.4%

+

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Figure 10: Regression betas 1994 – 2005 (rolling window analysis)

MSCI WORLD EXPOSURES

0,8

US HIGH YIELD EXPOSURES

1,2 1,0

0,6

LAGGED US HIGH YIELD EXPOSURES

1,0 0,8

0,8

0,6

0,4

0,6

0,2

0,4

0,4

0,2

0,2

0,0

0,0 -0,2

-0,2

-0,4

-0,4

INDEX

MAN 1

MAN 2

MAN 3

0,0 INDEX

US GOV BONDS (10YR) EXPOSURES

0,6

MAN 2

MAN 3

VOLATILITY EXPOSURES

0,8

0,4

MAN 1

-0,2

MAN 1

MAN 2

MAN 3

Full MAX

0,6

MIN

0,2

0,4

MED

0,0 0,2

-0,2

0,0

-0,4 -0,6

INDEX

INDEX

MAN 1

MAN 2

MAN 3

-0,2

INDEX

MAN 1

Unsurprisingly, given the profile of the fund, the rolling window analysis demonstrates the dynamic nature of the market exposures of the fund. Comparing these statistics to those of the index and two of its peers reveals that manager 1 manages his directional exposures more aggressively than most of his peers.

MAN 2

MAN 3

The charts clearly indicate that managers carefully select the risk exposures they wish to neutralize and which factors to take a view on. As convertible bonds are generally exposed to numerous risk factors, the diversity in styles within this strategy is quite large.

Multiplying the regression betas with the volatility of the risk factors enables a comparison of the importance of the various risk factors to the different funds.

Figure 11: Undiversified VAR (=exposure to risk factor * STDEV of risk factor)

VOL

25%

25%

25%

20%

20%

20%

20%

15%

15%

15%

15%

10%

10%

10%

HY

VOL

HY

5%

VOL

HY lag

GOV

HY

5%

0%

0%

AVERAGE

MANAGER 3 Equity

25%

5%

GOV

MANAGER 2 Equity

MANAGER 1 Equity

CONVERTIBLE ARBITRAGE INDEX Equity

10%

VOL

0%

HY lag

GOV

0%

HY lag

MAX 36 MONTHS

29

HY

5%

GOV

HY lag

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Literature Asness, C., Krail, R., Liew, J., ‘Do hedge funds hedge?’, Journal of Portfolio Management 28, 2001. Capco:’Operational risk in hedge funds investments’, white paper, 2003. Capocci, D, Corhay, A. Hubner, G.:’Hedge funds performance persistence in bull and bear markets’, working paper HEC University of Liege, 2005 Clarke, R. de Silva, H. and Thorley, S.:’Portfolio constraints and the fundamental law of active management’, Financial Analysts Journal, 2002. Gehin, W. and Vaissië, M.:’The right place for alternative betas in hedge fund performance: an answer to the capacity effect fantasy’, Edhec working paper, 2005 Geltner, D.M., ‘Smoothing in appraisal-based returns’, Estate Finance and Economics, 1991 Geltner, D.M., ‘Estimating market values from appraisal values without assuming an efficient market.’, Journal of Real Estate Research, 1993. Harcourt: ‘Hedge funds explained’, 2004 Ineichen, A.M.: ’Absolute returns: the risk and opportunities of hedge fund investing’, Wiley, 2002. Kat, H. and Lu, S: ’An excursion into the statistical properties of hedge fund returns’, working paper Cass Business School, 2002 Litterman, B. ‘Beyond active alpha’, Goldman Sachs, 2005. President’s Working Group on Financial Markets, “Sound Practices for Hedge Fund Managers”, Working Paper, 2000 (http:// www.aima.org) Swinkels, L.A.P., van der Sluis, P.J., ‘Return based style analysis with time-varying exposures’, Working Paper, 2002. Tremont, ’Tremont asset flows report Q3 2005’, 2005

Notes 1.

2.

3. 4. 5.

The frequently used terms ‘blow ups’ or ‘failures’ are often not correct as only very occasionally a liquidation results in a total loss of equity capital. Whether hedge funds are a separate asset class or an investment management style is an issue of semantics. Hedge funds actively invest (both long and short) in multiple asset classes and could be considered a separate management style. As hedge funds are usually treated as a separate part of the strategic investment portfolio many consider them to be a separate asset class. For a good overview of hedge fund strategies see Ineichen (2002). Sound Practices for Hedge Fund Managers (2000). A recent example of counterparty credit risk were the difficulties faced by investors, including some hedge funds, due to the failure of Refco to meet its obligations.

30

6. See e.g. Kat and Lu (2002) and Capocci et al. (2005) 7. Litterman (2005) introduced a somewhat related categorization not specifically for hedge funds but for investments in general. He divides the risk of an investment in CAPM beta, exotic beta and pure alpha from active management. He describes exotic betas as passive and therefore cheap to implement risk exposures with (currently) a positive expected return. These exotic betas have zero correlation with the CAPM market portfolio. Examples are commodities, CAT-bonds, selling volatility and investment in M&A. 8. Note that the numbers are generated using a return based approach and therefore always lags the actual exposures. Due to the lack of transparency of some funds a holdings based approach for the index is impossible. 9. In choosing the length of the regression window there is a clear trade off between accuracy, which decrease as the number of data points decreases, and timeliness, which increases when the number of data points drops. Although the statistics for a 36 month window are presented, this can be supplemented with less accurate, but timelier statistics of a 24 month and even 12 month window. Alternatively, one can resort to somewhat more sophisticated estimation methods, e.g. using a Kalman filter, as illustrated in Swinkels & van der Sluis, (2002). 10. Note that some of these risks are interrelated. 11. This is also related to the structural alpha part which is explained later on. 12. And deals might even break. When interest rates suddenly rise and/or equity markets fall, many announced transactions will get cancelled. What once appeared to be uncorrelated idiosyncratic deal risks have suddenly appeared to be highly correlated systematic risks. 13. All funds are selected randomly from an internal database and although their names are not disclosed these are real life examples. 14. While the value versus growth and the small versus large factors are usually much less volatile than overall equity markets these factors have been highly volatile during the analyzed period, most likely as a result of the ICT bubble during the late 90’s. 15. See e.g. Geltner (1991, 1993) and Asness et al (2001) on stale and managed pricing.

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Alternative Assets from a Liability Management Perspective Introduction Investments in alternative assets have the potential to substantially improve expected returns and downside risk of an investor. However, this potential is constrained by the need to manage the risk from asset and liability mismatches (“ALM Mismatch”). We have chosen to focus on four of the more mainstream alternative asset classes: commodities, property, private equity and hedge funds. We have selected these asset classes on the premise that they are widely accepted by the investor community to offer value. A brief discussion of each asset class is contained in Appendix A. In this article will examine the role of alternative assets in an investment portfolio of an institutional investor over a medium term investment horizon: 1. We explore the tension between two sources of risk reduction: diversification benefits provided by alternative asset classes and reducing ALM Mismatch. 2. We demonstrate an effective means of managing ALM Mismatch by using derivatives to exchange

ALM Mismatch risk for a funding risk. We use a Euribor funded portfolio as a simple proxy for an LDI strategy (liability driven investment). 3. We show how the effective management of ALM mismatch risk frees up risk budget to permit higher expected returns for less risk, and frees up cash assets for alternative investments to maximise returns. 4. We show the dramatic impact of the choice of two specific risk metrics on asset allocation. We contrast an absolute downside measure of risk with a dispersion measure. The difference between the risk measures demonstrates how the available risk budget for using alternative assets to enhance expected return is heavily constrained by the ALM Mismatch risk 5. Finally, we examine an optimal long-term asset allocation for a Euribor funded portfolio. The majority of discussions on alternative assets either focuses on each asset class on a stand-alone basis, or in terms of portfolio construction from an asset only perspective. However, the majority of

Patrick Lighaam (l), European Pensions Group, Morgan Stanley Global Capital Markets Mark Teeger (m), Asset Liability Management Group, Morgan Stanley Global Capital Markets Gwion Moore (r), Asset Liability Management Group, Morgan Stanley Global Capital Markets

31

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institutions invest, either directly or indirectly, to fund future liability commitments. We examine the impact of investment in alternative assets from the perspective of investors with different liabilities. We will consider four types of investors, who are managing assets against the following liabilities: 1. 2. 3. 4.

No liabilities (asset only) Nominal liabilities Inflation-linked or real liabilities Euribor liabilities, i.e. after hedging ALM Mismatch risk

We evaluate the impact of including alternative asset classes in portfolios previously constrained to more traditional asset classes, e.g. liquid equities, government bonds, corporate credit, inflation-linked bonds and cash. A conflict is demonstrated between ALM Mismatch risk and alternative asset diversification and we investigate how such a conflict can be managed by reducing ALM Mismatch risk. Over a 5-year horizon, we have generated 1000 capital markets scenarios describing the evolution of key market variables such as; inflation, nominal and real interest rate yield curves, GDP and asset returns. Using these scenarios, we test the performance of a range of asset allocations. For each expected return, we define the efficient frontier as the asset allocation with the minimum risk. In order to capture the full complexity of the interrelationship between different asset classes we simulate the asset and liability returns within a MonteCarlo framework. Morgan Stanley’s Asset Liability Management Group has developed a sophisticated economic cycle based “regime-switching” capital markets model. We use historic data to calibrate the model, which describes the behaviour of a wide range of asset classes across the economic cycle. The resulting values for returns, volatilities and correlations will vary over the economic cycle. The model is able to accommodate realistic asset behaviour, modelling asymmetric and fat tailed characteristics of return distributions that a more traditional Markowitz mean-variance approach to portfolio construction would neglect. Additional background information on the economic model is contained in Appendix B.

32

Risk Measurement The choice of risk metrics is of crucial importance in assessing the impact of alternative asset classes in portfolio construction. Whilst dispersion risk measures, such as standard deviation, tend to be the most used in practice, we believe that absolute downside risk measures often relate better to investors risk preferences. For example, the risk of achieving a low return is often closer to investors primary risk concerns than return volatility. Downside risk measures take into account the impact of a higher expected return on reducing downside risk. We should therefore expect to find higher expected returns from portfolios selected using downside risk compared with dispersion risk, even for risk averse investors. Out of the 1000 generated scenarios, we can observe the 50 scenarios with the worst outcome for asset returns, or funding ratio. Taking the average of these 50 worst outcomes, we get the “Mean of the worst 5% of outcomes”. We focus on two types of risk metrics based on this measure of portfolio tail risk: 1.

Downside risk: “Mean of the worst 5% of outcomes” This risk metric is a measure of downside risk to the portfolio. As such, it provides a better assessment of the economic risk exposure of the investor to low returns than a dispersion or volatility measure.

2. Dispersion risk: ‘Mean of all outcomes’ minus ‘Mean of the worst 5% of outcomes’ This risk metric is similar to that of standard deviation as it measures the dispersion of returns around the expected return. However, we focus only on asymmetric downside tail performance, thereby ensuring that high return scenarios are not regarded as risky. In order to provide a reference assessment of the impact of alternative asset classes in portfolio construction, we will consider the allocation of these alternative asset classes from two different points of view:

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A. Minimum dispersion risk portfolio This is the portfolio that is closest to a “liability matching portfolio” that can be achieved given the available selection of assets. These portfolios represent an asset mix that will minimise the uncertainty to the funding ratio or total return. The asset allocation breakdown of the minimum dispersion risk portfolio is contained in Appendix C. B. Reference portfolio Most investors are not solely seeking to minimise risk. Provided the associated extra risk is reasonable, many investors prefer to seek excess return. The reference portfolio’s asset mix has a one-for-one trade-off between the improvement in expected funding ratio or returns and the increase in dispersion/downside risk, i.e. the reference portfolio is the point whereby an improvement of the expected return equals an increase in the risk measure: Expected return ‘Mean of the Worst 5% of outcomes’

=1

(‘Mean’ – ‘Mean of the worst 5%’)

=1

Neither of these portfolios are suggested as a recommendation to the investor, but rather they are identified as aids to the discussion. For example, the

Expected return over 5 years (annualised)

Diagram 1a: Asset only dispersion risk optimisation. The red frontier contains only traditional asset classes, while the blue frontier has both traditional and alternative asset classes. The red and blue dots indicate the reference portfolios Dispersion risk Asset only efficient frontier

10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% 0%

5%

10%

15%

Mean – Mean of 95% downside (annualised)

Portfolio Optimisation We perform the optimisations in the following sections over a 5-year investment horizon, which has been chosen as a medium-term investment horizon. A shorter time horizon such as a year will fail to capture the risk premia that tends to develop over time; eg credit spread in excess of cumulative default. Throughout the discussion, we make comparisons between efficient frontiers for optimisations including traditional asset classes only and optimisations that include both traditional and alternative asset classes. The traditional asset classes are taken to be: cash; government bonds (bond index with 6 year duration); corporate credit (single A average rated bond index with 6 year duration); inflationlinked bonds (bond index with 6 year duration); and equities. We use proprietary numerical portfolio optimisation techniques to derive the efficient frontier. Numerical techniques are required since the capital markets model used here is a sophisticated Monte Carlo simulation process for which no closed form optimisation techniques apply.

or Expected return

more the reference portfolio’s asset mix resembles that of the minimum dispersion risk, the more ALM Mismatch risk is dominating alternative asset diversification risk.

20%

We explore how the optimal asset allocation varies against a range of liability profiles and observe achievable levels of risk and return.

Asset Only Investor From an asset only perspective, the benefits of investment in alternative assets are clear. Diagrams 1a and 1b show the relative impact of including alternative assets from both a dispersion risk and downside risk perspective. ‘Return’ is defined here as the compound annual return over a 5 year horizon. Diagram 1a shows the asset allocation efficient frontiers under the dispersion risk measure, with and without alternative assets. Alternative assets do improve risk-return characteristics. Both with and without alternative assets the minimum dispersion risk portfolio is mostly cash, with some inflation-linked bonds (see Appendix C). The proportional

33

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Expected return over 5 years (annualised)

Diagram 1b. Asset only downside risk optimisation. The red frontier contains only traditional asset classes, while the blue frontier has both traditional and alternative asset classes. The red and blue dots indicate the reference portfolios Downside risk Asset only efficient frontier 10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0%

is possible to increase expected return while maintaining a level of downside risk that is comparable to the minimum downside risk for optimisation without alternative assets. The reference downside portfolio has 65% alternative assets, again primarily split between property and hedge funds.

Asset and Liability Investors

4%

2%

0%

-2%

-4%

-6%

-8%

-10%

Moving away from ‘asset only’, towards an asset and liability investor, the ALM Mismatch will often be the primary source of risk to the portfolio value. For an investor such as a pension fund or an insurer, we explore the extent to which the ALM Mismatch risk dominates over the diversification benefits of investing in alternative assets.

Mean of worst 5% downside (annualised)

benefit of alternative assets increases the higher the level of risk the investor is prepared to take. The reference dispersion portfolio has a 23% allocation to alternative assets, primarily split between property and hedge funds. Diagram 1b shows the asset allocation efficient frontiers under the downside risk measure, with and without alternative assets. From the perspective of downside risk, the case for holding a high proportion of alternative assets becomes extremely strong. With allocations of up to 40% alternative assets, it

Table 1 Asset Only: Reference portfolios asset mix for diagrams 1a and 2a Dispersion risk

Downside risk

Without Alternative Assets

With Alternative Assets



Cash

66%

48%

59%

–

Government Bonds

–

–

–

–

Corporate Credit

13%

20%

32%

21%

Equity

3%

4%

9%

14%

Inflation Linked Bonds

19%

6%

–

–

Commodities

–

3%

–

11%

Private Equity

–

1%

–

3%

Property

–

11%

–

24%

Hedge Funds

–

8%

–

27%

Asset Only

34

We assume an initial funding ratio of 120%, both for the case of the nominal liability and the real liability investor and examine investors with a) nominal, b) real or c) Euribor swapped liabilities. We then examine the risk to the funding ratio over a fiveyear investment horizon. Nominal or real liabilities are modelled as constant maturity 15-year duration zero-coupon bonds. Euribor liabilities are modelled as AAA rolling 3-month cash.

Nominal Liabilities For the case of an investor with nominal liabilities, the minimum dispersion risk portfolio is mostly government bonds with some corporate credit (see Appendix C). As the target funding ratio is increased above the minimum risk portfolio, the proportion of government bonds declines and the proportion of corporate credit increases. Government bonds give way to corporate credit due to the higher yield expected on corporate credit. Alternative assets constitute a much lower proportion of the dispersion risk reference portfolio than for the asset only investor. This is due to a combination of two factors. Firstly, the size of the ALM Mismatch risk reduces available risk budget for diversification. Secondly, the yield pick-up from corporate credit allows an additional return over government bonds with some liability hedging properties, limiting the relative utility of alternative assets.

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Diagram 2a: Asset and nominal liability dispersion risk optimisation. The red frontier contains only traditional asset classes, while the blue frontier has both traditional and alternative asset classes. The red and blue dots indicate the reference portfolios. In this case, they are coincident.

Diagram 2b: Asset and nominal liability downside risk optimisation. The red frontier contains only traditional asset classes, while the blue frontier has both traditional and alternative asset classes. Downside risk Asset and nominal liability efficient frontier

Dispersion risk Asset and nominal liability efficient frontier

160% Expected funding ratio

Expected funding ratio

160% 150% 140% 130% 120% 110%

150% 140% 130% 120% 110% 100% 100%

100% 0%

20% 40% 60% 80% Mean – Mean of worst 5% downside funding ratio

100%

Along the efficient frontier, as we explore portfolios with expected return above the reference portfolio, alternative assets provide some risk-return improvement over traditional assets alone. However, the improvement is small as compared to the benefit provided to the asset only investor. From a downside risk perspective, the impact of alternative investments is more pronounced. The higher risk adjusted returns provided by alternative assets compete with corporate credit to raise the

90% 80% 70% Mean of worst 5% downside funding ratio

60%

expected funding level, while minimising total ALM Mismatch risk. Here 44% of the assets in the reference portfolio is dedicated to alternative assets. Hedge funds feature prominently due to their high risk adjusted returns. Commodities also feature strongly due to the diversification benefits they provide. Property features weakly due to the positive relationship between rental income and the level of inflation and nominal rates, meaning that property will on average produce low returns in a low yield environment when nominal liabilities have a high value.

Table 2 Asset and nominal liability: Asset mix for Reference portfolios Dispersion risk

Downside risk

Nominal Liability Benchmark





Cash

–

–

–

–

Government Bonds

20%

19%

–

–

Corporate Credit 76%

77%

76%

37%

Equity

4%

3%

24%

19%


–

–

–

–

Commodities

–

–

–

11%

Private Equity

–

1%

–

9%

Property

–

–

–

1%

Hedge Funds

–

–

–

23%

Inflation-linked Liabilities For the case of the investor with inflation-linked liabilities, the minimum dispersion risk portfolio, both with and without alternative assets, is split between inflation-linked and government bonds (see Appendix C). These bond assets provide a hedge for the duration and inflation sensitivity of the liabilities. However due to the substantial ALM Mismatch they are only able to provide a partial hedge of liabilities, leaving considerable residual risk, and then only at the cost of committing to a low return investment strategy. This is particularly the case for inflationlinked bonds, which will typically yield less than government bonds.

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Diagram 3a: Asset and real liability dispersion risk optimisation. The red frontier contains only traditional asset classes, while the blue frontier has both traditional and alternative asset classes. The red and blue dots indicate the reference portfolios

Diagram 3b: Asset and nominal liability downside risk optimisation. The red frontier contains only traditional asset classes, while the blue frontier has both traditional and alternative asset classes. The red and blue dots indicate the reference portfolios

Dispersion risk Asset and real liability efficient frontier

Downside risk Asset and real liability efficient frontier 160%

150%



160%

140% 130% 120% 110% 100% 0%


100%

There are two major differences between the nominal and real dispersion risk optimisations. There are two major differences between the nominal and real dispersion risk optimizations. Firstly, high inflation expectation scenarios tend to be associated with higher rather than lower nominal rates. Based on this observation, the inflation component within the total real liabilities will on average show an increase in value with rising nominal rates, the increase in the nominal discount factor damps the impact of the increase in inflation expectations. Therefore the resulting ALM Mismatch risk is lower for real liability

Table 3 Asset and real liability: Asset mix for Reference portfolios Dispersion risk

Downside risk

Real Liability Benchmark



Cash

–



–

–

–

Government Bonds 16%

15%

–

–

Corporate Credit

51%

60%

76%

34%

Equity

6%

7%

24%

15%


27%

–

–

–

Commodities

–

3%

–

8%

Private Equity

–

2%

–

10%

Property

–

4%

–

8%

Hedge Funds

–

9%

–

26%

36

150% 140% 130% 120% 110% 100% 120%

110% 100% 90% 80% 70% Mean of worst 5% downside funding ratio

60%

investors than for nominal liability investor, all else being equal. Secondly, the absence of an inflation-linked credit asset in this analysis means that real ALM matching with bonds reduces expected returns considerably. (In practice, we can use derivatives to create synthetic inflation-linked credit assets.) As a result, alternative assets displace inflationlinked bonds from the reference portfolio. This is because, by first improving the asset-side risk-return profile, alternative assets also provide a more effective approach to total portfolio risk reduction than an incomplete hedge of ALM Mismatch risk with low yielding inflation-linked bonds. Alternative assets make up 18% of the dispersion risk reference portfolio. From a downside risk perspective, the impact of alternative investments is more pronounced. The higher risk adjusted returns provided by alternative assets compete with corporate credit to raise the expected funding level while minimising total portfolio risk. For the reference portfolio, 52% of the asset portfolio is dedicated to alternative assets. As with the nominal liabilities, hedge funds feature prominently. Property also makes up 8% of the reference portfolio; this differs from the nominal downside risk optimisation. The greater proportion of property is a

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It is interesting to compare the level of private equity in the downside risk reference portfolio from the asset-only optimisation with that of the optimisation for investors with nominal and real liabilities. The proportion of private equity is very small (3%) for an asset only optimisation, while higher (between 9-10%) for the nominal and real investor. The higher proportion of private equity is a reflection of the higher level of risk for the portfolio as a whole, making the marginal contribution of the risky private equity to the total portfolio risk smaller, and thus more easily accommodated.

Euribor Liabilities As we have seen, alternative assets make a positive diversification contribution to investors with both real and nominal liabilities. However, we have seen that the benefits appeared more striking for an asset only investor. In order to maximise the benefits from alternative investments, the ALM Mismatch should be reduced. For an investor like a Dutch pension fund the liabilities are either long-dated nominal or real cashflows. Since the universe of nominal and real bonds is most-

Table 4 Asset and Euribor liability: Asset mix for Reference portfolio. We show the minimum dispersion risk portfolio in the first column as no one-for-one reference portfolio exists. Dispersion risk

Downside risk

Euribor Liability Benchmark





Cash

100%

4%

-

-

Government Bonds

-

3%

5%

-

Corporate Credit

-

58%

78%

-

Equity

-

6%

17%

18%


-

-

-

-

Commodities

-

4%

-

15%

Private Equity

-

1%

-

10%

Property

-

12%

-

18%

Hedge Funds

-

11%

-

38%

ly of short to medium duration, it is cumbersome to eliminate ALM Mismatch using physical assets, and even a partial hedge of liabilities will require accepting a significantly reduced expected return on the asset portfolio. Hedging with derivative overlays, such as interest rate and inflation swaps, allows the investor to swap a significant proportion of their ALM Mismatch risk to a floating rate risk. This approach is taken as a theoretical exercise rather than as a portfolio management recommendation, considering that there are several other instruments and strategies available to reduce the ALM Mismatch risk which do not necessarily transform your pension liabilities to a Euribor benchmark In this section, we examine a portfolio that has had all of the ALM Mismatch risk transformed to Euribor. A floating rate benchmark such as Euribor makes the problem of portfolio construction closer to that of an asset only investor. This is because using derivative overlays to manage ALM Mismatch has the additional advantage of freeing up cash assets for investment in assets with higher expected returns. Diagrams 4a and 4b show the efficient frontiers for a Euribor benchmarked liability. Cash and floating rate

Diagram 4a: Asset and cash liability dispersion risk optimisation. The red frontier contains only traditional asset classes, while the blue frontier has both traditional and alternative asset classes. The red dot indicates the reference portfolio for the frontier with alternative assets. There is no reference portfolio for the optimisation with traditional asset only. Dispersion risk Asset and Euribor liability efficient frontier 160% Expected funding ratio

reflection of the link between rental income growth and inflation.

150% 140% 130% 120% 0%

37


100%

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Diagram 4b: Asset and cash liability downside risk optimisation. The red frontier contains only traditional asset classes, while the blue frontier has both traditional and alternative asset classes. The red and blue dots indicate the reference portfolios

We have explored the role of four alternative asset classes in efficient portfolio construction for different types of investors, finding that: •

Downside risk Asset and Euribor liability efficient frontier


160%

•

150% 140% 130%

• 120% 130%

120%

110% 100% 90% 80% 70% 60% Mean of worst 5% downside funding ratio

credit provide a highly effective hedge of the liabilities. (Corporate credit for the Euribor liability optimisation is modelled as floating rate notes, or FRNs.) This allows the investor to adjust asset allocation and risk to target a spread over Euribor commensurate with their funding objectives. The proportion of alternative assets in the downside risk reference portfolio for Euribor liabilities is higher than for any of the other three categories of investors examined, with 81% of the portfolio dedicated to alternative assets. This is a reflection of a combination of two factors. Firstly, hedge funds and real estate returns are broadly linked to the general level of interest rates/inflation and hedge funds will often have “cash plus” return targets. In this regard, economic environments with rising rates causing a greater burden from Euribor liabilities are likely to be accompanied by good performances from hedge funds and real estate. Secondly, the reference point occurs at the funding level where cash and FRNs have just been removed from the portfolio in favour of the higher returning assets.

Conclusion The available risk budget for using alternative assets to enhance expected return can be heavily constrained by ALM Mismatch risk. By contrast, the use of a derivative overlay to control ALM Mismatch risk frees up risk budget to permit higher expected returns for less risk, and frees up cash assets for investment to maximise returns.

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•

For the asset only investor the diversification benefits of alternative assets are considerable, particularly from a downside risk perspective. For the nominal liability investor the need to manage ALM Mismatch risk is greater than the benefits provided by alternative asset diversification. As a result, the diversification benefits of alternative assets are much smaller than for an asset only investor. Since high inflation scenarios tend to be associated with higher rather than lower nominal rates, ALM Mismatch risk is lower for real liability investors. This leaves greater capacity for alternative assets to contribute to portfolio risk diversification than for a nominal liability investor. For the case of an investor that has used a derivative overlay to swap nominal or real liabilities to Euribor, a Euribor benchmark provides more freedom in asset portfolio construction. The fact that many asset class returns have a positive relationship to the level of nominal interest rates and inflation means that the potential for alternative assets to improve portfolio performance is very strong.

We have also seen how using a downside risk measure allows for the fact that higher expected returns result in lower downside risks, given the same dispersion risk. Therefore, even for risk averse investors, we find that portfolios selected using downside risk measures have higher expected returns than those selected using dispersion risk measures.

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Appendix A – A survey of the selected alternative assets Professional investors are faced with a wide and rapidly growing selection of new asset classes and financial products. Recently, increased focus has been on asset classes such as infrastructure, structured credit and volatility related products. We have chosen to focus this article on four of the more mainstream alternative assets, namely: commodities; property; private equity and hedge funds.

Commodities Based on their historic performance, commodity futures have shown behaviour that might be desirable for many investors. Commodities have exhibited risk and returns comparable to equity, whilst benefiting from having returns either negatively or weakly correlated with returns on equities and bonds. Additionally, over longer time periods they have shown a positive correlation with inflation. Portfolio construction is of key importance when investing in commodities. The desired risk characteristics of each individual commodity might be a reason to alter the individual commodity weights versus standard commodity indices. For example, the desired low or negative correlation with equities and bonds and higher correlation with inflation is mostly found within the energy related commodities rather than the (precious) metals and agriculture related commodities.

Property When discussing investment in property, care must be taken to distinguish between the different types of investments opportunities that are available, since risk, return and liquidity characteristics can be very different. For example, direct investments in property have shown returns that show low volatility and are weakly correlated to bond and equity markets, but more strongly correlated to inflation. Indirect investment via non-listed property funds combines these characteristics with greater liquidity and lower transaction cost. However, valuation still presents a problem. Investments in the public listed property funds are liquid, with transparent pricing, but have a higher correlation to equities, and so a reduced diversification benefit.

There is strong evidence of correlation between rental revenue and inflation, although the relationship may occur with a delay depending on the structure of the lease agreements. The link to inflation becomes most reliably apparent over long investment horizons.

Private Equity Private equity makes itself attractive to investors seeking to enhance return. A well-diversified private equity portfolio may be expected to return in the order of at least 2% per annum over public equity. This is in part compensation for the poor liquidity of private equity investments. However, higher expected returns are accompanied by higher risks. The degree of correlation with the performance of other asset classes is unclear, and a desire for prudent portfolio construction would suggest a high correlation to public equity as a modelling assumption. Private equity has a long investment horizon, and accurate assessment of returns can only be made once the investments have either been realised or have become publicly traded. Prior to this, returns are calculated on an internal rate-of-return basis. The distributions of private equity returns have “fat tails”, with some investments falling to zero value, while a few record significantly higher returns.

Hedge Funds Hedge funds as an asset class have delivered high risk adjust returns with remarkable consistency. However, the idiosyncratic nature of hedge funds makes their classification difficult. Hedge fund managers pursue a wide range of trading strategies such as Long/Short Equity, Credit, Event Driven, Macro, Market Neutral, Systematic, Multi-Strategy and Arbitrage. The risk and return characteristics of different strategies and correlations with existing asset classes therefore differ. It is the diversity of hedge funds that provides much of their risk advantages. The unique risk advantage for a hedge fund investor is the ability to diversify across trading strategies as well as asset classes. For example, a well-diversified fund of hedge funds can be expected to recorded returns slightly lower than equity, but with substantially lower volatility.

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Appendix B – Morgan Stanley’s Cyclical Capital Markets Model The analysis of asset diversification based on downside assets risk measures relies on the ability to credibly assess expected returns as well as the risk distribution around return expectations. Morgan Stanley’s capital markets approach is particularly targeted at the distribution of excess returns and the relative attractiveness of asset classes and has the following unique characteristics: 1. Morgan Stanley’s capital markets modelling approach is to simulate a real world evolution of key financial and economic variables with scenarios designed to reflect dynamic market behaviour across the economic cycle: 2. Driving capital market scenarios by the economic cycle promotes the creation of economically rational scenarios. For example 10 year versus 3 month yield curve inversions often happen near recessions but cannot persist for 10 years, whereas the naïve use of a simple derivative pricing model for simulation could easily contain scenarios with 10 years of inverted 10 year – 3 month yields 3. Calibrated against historic data, the model is able to accommodate realistic asset behaviour, modelling asymmetric and fat tailed return distributions that would be missed within more traditional Markowitz mean-variance approaches. 4. Economic cycles are a natural way to build mean reversion into interest rate models, avoiding com-

40

mon model calibration problems associated with the parameterisation of mean reversion speed. 5. Stochastic credit transition matrices populate tails of credit risk distributions by allowing high probabilities of downgrade and default, and low recovery rates. 6. The capital markets specification is deliberately not arbitrage free, so we can investigate reward and risk dimensions to investment strategy decisions. Arbitrage free models are inappropriate for investment strategy purposes in that by definition they have no return dimension. For example, an arbitrage free approach always assumes credit is fairly priced and therefore there is no difference in expected return when credit spreads move. In our approach we separately analyse income (current and projected spreads for reinvestment) compared with losses (cumulative defaults including transitions, net of recoveries) to explore realistic value offered by different credit classes. 7. A focus on modelling fat tails in equity returns – arbitrage free models can underestimate equity risk in periods of low equity volatility and high forward rates. 8. Modelling economic cycle and inflation linkages to rental income and yield curve linkage to property valuation.

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Appendix C – Minimum dispersion risk portfolios Asset Only

Euribor Liability

Minimum dispersion-risk portfolio





Cash

69%

67%

100%

100%

Government Bonds

7%

9%

–

–

Corporate Credit

–

–

–

–

Equity

–

–

–

–


23%

22%

-

-

Commodities

–

–

–

–

Private Equity

–

–

–

–

Property

–

2%

–

–

Hedge Funds

–

–

–

–

Nominal Liability

Real Liability

Minimum dispersion-risk portfolio

Without Alternative With Alternative Assets Assets

Without Alternative With Alternative Assets Assets

Cash

–

–

–

–

Government Bonds

96%

96%

45%

45%

Corporate Credit

4%

4%

10%

10%

Equity

–

–

–

–


–

–

45%

45%

Commodities

–

–

–

–

Private Equity

–

–

–

–

Property

–

–

–

–

Hedge Funds

–

–

–

–

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Tier One Asset Manager Our client is a new company based on the philosophy and processes of an existing successful pension fund management organisation. Its objective is to provide investment advice and asset management services to pension funds associated with a large multinational sponsor worldwide. Total assets under management will exceed EUR 30 billion by the end of 2005, and are expected to grow to around EUR 40 billion in the years to come. The new company will be an agile, internationally oriented and dynamic organisation employing approximately 80 professionals in total. Continuous innovation in financial products and changes in pension regulations make it a professionally challenging environment to work in. Within the global investment management community our client has an outstanding reputation for being both professional and pioneering. Organisational Structure: The company has a flat organisational structure with short communication lines and a high degree of delegation. The atmosphere is professional yet informal. Currently, 20 professionals in the Investment Department manage the total asset portfolio; this number is expected to grow to around 30. They pursue investment strategies covering areas such as high-yield, emerging market bonds and equities, equities arbitrage, index management, tactical asset allocation, hedge funds, private equity and other alternative assets. A team-based approach is applied throughout the entire organisation.

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Global Value Added/ Opportunistic Real Estate Real estate as an asset class has long been an essential component of the overall portfolio mix for institutional investors. Diversification features and favorable risk/return metrics in general have been the most important drivers for including this asset category in investment portfolios.. Institutional investors operating in large local markets have typically held property investments directly in their local markets, while investors with smaller local markets have a longer history of cross border real estate investing. As real estate investors are faced with a more efficient market due to capital overhang and a shift out of the current low yielding bonds, would this be the right time to further diversify the real estate allocation by including global unlisted opportunistic and value added real estate funds? The starting point of this article is the debate whether to invest locally or internationally. Subsequently the different approaches to obtain international real estate exposure are described. In addition an overview will be given of the different investment styles within the unlisted real estate investment spectrum. As the last item we will address return/risk aspects which encompass more qualitative features rather than just financial viewpoints.

By Børge Tangeraas and Mark Kouters, Composition Capital Partners

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Cross-Border versus Local Real Estate Investing Historically, real estate portfolios of institutional investors were predominantly composed of domestic direct holdings. As a result of higher sophistication in other asset classes investors started increasingly to seek opportunities not only abroad but also through alternative approaches and structures to build their real estate allocations. Simultaneously, investors became aware of the fact that better geographical diversification within their real estate portfolio would lead to better risk/return characteristics. In addition, by broadening their horizon investors could access a larger pool of talented players in this segment of the market. Extensive research has been published with respect to the diversification benefits of cross-border investing. Most of these studies conclude that cross-border real estate investing offers investors attractive diversification benefits. A global real estate portfolio has a low correlation to other traditional asset classes as well as among the individual regional real estate markets. Also, property offers a relatively high and stable income return, a low correlation with other asset classes and, last but not least, an effective inflation hedge1. While strategies employed by real estate investors are generally similar on a world-wide basis, global real estate investors benefit from the increased investment opportunity set to redevelop property which is still owned by companies and governments, particularly in Continental Europe and Asia. Also, markets function at different levels of maturity. For instance, established markets show stretched pricing which have been carried upwards by market momentum. At the same time new inefficient markets can offer tremendous opportunities for capable managers who take on risk while buying and developing property in these newer markets. Real estate is essentially a local business and a global universe allows investors to gain access to markets experiencing the best fundamentals at a specific point in time (see Hudson-Wilson, Fabozzi and Gordon).

Methods of Cross-Border Real Estate Investing Once investors have made the decision to make cross-border real estate allocations, it should be determined whether an investment should be made

in publicly listed real estate securities, or alternatively, in (“private equity”) real estate funds and other non-listed vehicles such as joint ventures. Some investors have chosen to organize listed and nonlisted forms of real estate investment activities in one single department. The benefits of investing in indirect real estate companies compared to making investments in direct real estate are: •

Investors no longer needs internal resources to manage the in-house property portfolio; lower information and monitoring costs related to the inefficiency of real estate markets; the investor’s ability to focus on asset allocation and top-down strategies within his portfolio context; the investor gets access to specialist management (also in fields where indirect public equity is not present).

• •

•

Private Real Estate Investing Since 1996, when the second wave of private equity real estate funds emerged in the US that spread across Europe and Asia within a few years, the number of funds has gone through a dramatic growth phase. INREV, the European industry platform for non-listed real estate funds, manages a database which in its latest quarterly report in 2005 showed over 440 distinct vehicles, representing a gross asset value of €290 billion. In Asia growth is lagging behind Europe; however, it is catching up rapidly as managers in both Japan, China and India respond to the increasing cross-border real estate allocations to their countries. Already over 90 different real estate vehicles were counted in Asia by December 2005 (Source: Composition Capital Partners Database).

Investment Styles Property markets in the U.S., Europe and Asia move within different stages in their respective real estate phases due to asynchronic economic cycles. Property managers with proprietary access to information can realize superior returns benefiting from inefficient information flows in the markets. In today’s environment a myriad of types of investors (typical institutional investors, family offices and high net worth individuals) employ capital globally through numerous publicly listed and non-listed vehicles.

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Depending on their liability profile and risk appetite these investors adopt various investment styles ranging from Income-Driven (Core) to Value-Added and Opportunistic strategies (see Table 1). “Relative return” investors generally rely on a few markets where appropriate benchmarks are available (U.S., UK, Australia and some Continental European markets) while “absolute return” investors in the above mentioned markets focus on local managers executing more inventive transactions by repositioning or redevelopment of the property assets. They generally excel in deal sourcing and have a demonstrated background and distinct ability to add value. More methodical market research in less developed countries and emerging markets may be undertaken by absolute return investors to gauge local market sensitivities (country risk and liquidity risk). Obviously, risk and return go hand in hand with acquisition price levels generally being low, outlook for increasing rents, and high yields.

Value Added and Opportunistic Real Estate Risk Adjusted Returns Measuring global real estate value added and opportunity funds’ true volatility is far from a science

due to the asset class’ current limited liquidity and disclosure of returns. With no market valuation for specific funds during its lifetime, investors are left with internal rate of returns based on realized net cash flow throughout the closed end life of the fund. Data collected by Pension Consulting Alliance for the ten year period from 1991 to 2001 based on the performance of 43 managers and 110 funds suggests that opportunity funds as an asset class have produced an average gross annual return for the period of 20.66% (15.44% net of fees) with a low in 1999 of 10.45% and a high in 2001 of 30.95%. In contrast core real estate, which has historically generated an average gross annual return of roughly between 8 and 10%. Furthermore, opportunistic real estate managers who attempt to add value to property assets, can document fairly consistent performance over subsequent funds launched by that same manager. This is a phenomenon that is well documented also in the private equity working field, an industry which shows similar characteristics. Hence, managers who have demonstrated the ability to add value to assets and buy and sell their properties in a disciplined way may generally be able to generate

Table 1: Real Estate Investment Styles Core

Value-Added

Opportunistic

Sectors

Major property types

Major property types plus All, including non traditional other retail, hospitality, senior living, storage

Property Classification

A, B+

B+, B

B-, C

Location

Institutional primary market

Institutional primary market, some secondary markets

Primarily secondary and tertiary

Life Cycle

Operating

Operating, leasing, re-leasing Development, redevelopment

Occupancy

High with low rollover concentration and little near term rollover

Moderately to well leased w/ Low or w/ high rollover some rollover concentration concentration or near term and near term rollover, subrollover stantially pre-leased development

Leverage

Low

Moderate

High

Required Capital Expenditures Minimal

Moderate improvements, sig- Significant, full development nificant renovations

Anticipated Return (IRR)

5-10%

10 – 15%

Primary Source of Return

Current income

Current income and apprecia- Apprecation tion

Source: Professioneel beleggen 10, Vastgoedbeleggingen deel B, Roosen, R, 2004

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>15%

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alpha to subsequent funds while acting with the necessary risk-consciousness. But there is no free lunch:, investors need to understand the history and the way managers produce their returns in context to the underlying real estate markets.

Measuring Market Risk Constraints, opportunities, and risks associated with investing in foreign capital markets can be analyzed to assess the overall risk environment. We believe the following factors are important to evaluate risk with respect to value added and opportunistic international real estate investing: •

•

•

•

Political and fiscal stability –governmental stability, the quality of socio-economic conditions, an established and reliable legal and tax jurisdiction Market transparency – variables analyzed to assess the transparency of the local markets include the availability of data and benchmarks to conduct due diligence at fund level and property level. Liquidity and size – the overall market capitalization and the liquidity of the local markets is important to assess the overall local market volatility. Macro economic and real estate specific volatility – volatility in terms of the standard deviation of historical time series data can be calculated on variables such as realized returns, rental levels, local interest rates and foreign exchange rates.

Measuring Manager Risk The manager’s investment strategy and ability to add value in order to generate excellent absolute (and relative) returns are generally measured by: •

•

Track record analysis – the consistency and quality of the manager’s track record with regard to the strategy and vintage year. Investment Strategy – value added and opportunity funds rely heavily on the expertise of the manager to generate returns by taking on prudent market risk or by taking on development, refurbishment and/or lease-up risk. The manager’s track record and return expectations have to be viewed in relation to their past and future

investment strategy, management’s dedication, motivation, and investment process. After adjusting for the risks addressed above it is fair to argue that global value added and opportunistic real estate strategies do indeed contribute measurable risk-adjusted return benefits to well diversified portfolios. However, opportunistic real estate returns should be adjusted for leverage, fees and strategy risk.

Conclusion The rapid increasing depth of the global non-listed real estate industry allows investors to implement efficient value added and opportunistic strategies. With established property markets becoming more efficient while not delivering the required return for the level of risk it makes sense to explore alternative strategies for real estate investing. Supported by the emergence of strong industry platforms such as INREV in Europe, additional transparency and increased classification of the current and future investment universe will take place. Indirect real estate industry platform initiatives are also being established in the U.S. and Asia. Since its start INREV has been making great progress in working towards further professionalism of the non-listed industry and has installed several working committees that take on issues like corporate governance, best practices, liquidity and benchmarks. The design and implementation of benchmarks for stabilized core private funds will be useful for income producing investors. Value added and opportunistic investors, we believe, will benefit from an objective peer group benchmark. Following the trends in the private equity environment, in which peer group benchmarks have become well established, similar standards may also be initiated by the value added and opportunistic real estate investment industry. To summarize, a more professionalized industry creates opportunities for sophisticated investors that want to build up, expand or restructure their real estate exposure. Alternative strategies such as opportunistic and value-added types of investments have delivered good returns when executed by skilled and experienced managers. A selective and in-depth analysis should ultimately contribute to top-tier risk adjusted returns.

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Note 1.

Numerous studies and papers have been written on indirect international real estate investments. For a detailed review of literature see Sirmans and Worzela, Urban Studies, 2003.

References Hudson-Wilson, S, F. Fabozzi and J. Gordon, Why Real Estate?, Journal of Portfolio Management, Special Real Estate Issue 2003, pp. 12-25. Hahn, T., D. Geltner, and N. Gerardo Lietz, Real Estate Opportunity Funds, Journal of Portfolio Management, Special Real Estate Issue 2005, pp. 143-153.

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Auteurs instructie Het VBA Journaal is een wetenschappelijk blad met een praktijkgerichte lezerskring Redactie adres: VBA Herengracht 479 1017 BS Amsterdam telefoon: 020-618 28 12 e-mail: [email protected]

Het VBA Journaal heeft een wetenschappelijke D-status. De redactie hecht aan deze status en stelt daarom eisen aan de inhoud en de stijl van te plaatsen artikelen. De lezers van het blad zijn de leden van de VBA-Beroepsvereniging van Beleggingsdeskundigen en andere professionals in de financiële wereld. Goed opgeleide beroepsbeoefenaren met een scherp oog voor de praktische aspecten van de geboden materie. Zowel de lezer met een meer theoretische inslag als de op toepassing gerichte lezer moeten aan hun trekken komen. Dit stelt hoge eisen aan toegankelijkheid en leesbaarheid van de artikelen. De toegankelijkheid van artikelen is gebaat bij een aantal zaken: • actief taalgebruik en korte zinnen; • een korte, prikkelende titel die de lading dekt, eventueel met subtitel; • een stimulerende samenvatting die de lezer vertelt waarom dit artikel voor hem relevant is en wat er in het artikel ruwweg mag worden verwacht. • regelmatig gebruik van tussenkopjes ter ondersteuning van de structuur; • vergelijkingen alleen als ze een toegevoegde waarde hebben; • duidelijke conclusies; • het gebruik van grafieken en tabellen vergroot de aantrekkelijkheid van een artikel. Maximale inzichtelijkheid wordt bereikt wanneer deze onafhankelijk van de tekst kunnen worden gelezen. Streef naar minimaal 1 grafiek of tabel per pagina; • een beperkt gebruik van het aantal noten. Beoordelingsprocedure Een artikel wordt na ontvangst ter beoordeling toegezonden aan twee referenten. Komt het artikel voor plaatsing in aanmerking, dan kunnen, als de beoordelaars het nodig achten, enige ronden met vragen en suggesties ter verbetering volgen. Is het eindresultaat positief, dan wordt het artikel geplaatst. Na iedere redactievergadering – ongeveer een maand voor de verschijning van het Journaal – wordt u op de hoogte gebracht van de fase waarin uw artikel zich bevindt. De redactie streeft ernaar de periode tussen ontvangst en plaatsing van het artikel zo kort mogelijk te houden. Praktische zaken • De optimale omvang van een artikel is ongeveer 3000 woorden. Het maximale aantal woorden is 4000 Inclusief inleiding, tabellen en grafieken betekent dit, dat een artikel maximaal 5 pagina’s in het VBA Journaal beslaat. • Gebruik de spelling uit de Woordenlijst der Nederlandse Taal (het ‘groene boekje’). Neem literatuurverwijzingen als volgt op in de tekst: Nillissen (1995, p. 13). In de literatuurlijst: Nillissen, T. (1995), Towards a new Journal for Dutch Investment Professionals, VBA Journaal, elfde jaargang nr. 1, maart 1995, pp. 12-15. • Geef noten aan met cijfers. Neem de noten aan het eind van de tekst, maar voor de literatuurlijst op. • Indien u bij een revisie van een artikel hebt geprofiteerd van de opmerkingen van de (anonieme) referent(en), geef dit dan in een noot aan. Voor uitgebreidere richtlijnen over het schrijven van toegankelijke artikelen hierbij enige literatuurverwijzingen: • Grubben, E. en Vriens, J (1995), Professioneel Schrijven, handleiding bij het voorbereiden en schrijven van creatieve en effectieve teksten. Academic Service, Schoonhoven (najaar 1995, daarvoor via de redactie te verkrijgen). • McCloskey, Donald, (1985), Economical Writing. Economic Enquiry, Vol. XXIV, April 1985.

Commissies/voorzitters Bestuur van de Vereniging Dr. R.Th.H. Willemsen RBA, voorzitter Mevr. S.H.C.M. van de Kamp-Vergeer RBA, secretaris Mevr. Drs. A.M. Munnik RBA, penningmeester Drs. M. de Berg RBA Drs. J.K.H. van Dam RBA Drs. M.B.A. Sanders RBA Drs. C.M.H.G. de Vaan RBA Mevr. R. van Wijk-Russchen RBA Ballotagecommissie Mevr. Drs. W.E. Nieuwenhuizen, voorzitter Tuchtcommissie Mr. G.St. Panjer, voorzitter Kascommissie Drs. R.G. van Boeijen RBA, voorzitter Programmacommissie Drs. O.J.S. Rabeling RBA, voorzitter Commissie Regelgeving Mr. G.St. Panjer, voorzitter Accountingcommissie Drs. J.S.A. van Scheijndel, voorzitter Commissie Asset & Liability Management Drs. C.E. Kortleve, voorzitter Commissie Beleggingsproces Drs. H.J. van Hoos RBA, voorzitter Commissie Duurzaam Beleggen Drs. M. de Berg RBA, voorzitter Commissie Alternatieve Beleggingsvormen en -strategieën Drs. W.B. ten Brinke, voorzitter Commissie Tactische Asset Allocatie Drs. P.P.A. Bronger RBA, voorzitter Commissie Vastgoed Drs. R.J.E. Satumalaij RBA, voorzitter Commissie Technische Analyse Dhr. J.B. de Kempenaer, voorzitter Commissie Investment Perfomance Measurement Drs. C.A.G.M. Reniers RBA, voorzitter VBA vertegenwoordiging in European Investment Performance Council Drs. C.A.G.M. Reniers RBA Dhr. R.A.F. van Eeuwijk MA, CFA Dutch Commission on Bonds (DCB) en European Bond Commission (EBC) Drs. R.P.J.M. ter Horst RBA, voorzitter Coördinator van bovenstaande 11 commissies Dhr. D. Wenting, AAG RBA Curatorium pd. Opleiding tot Financieel en Beleggingsanalist (VU) Prof.Dr. R.A.H. van de Meer, voorzitter Effas Board in Frankfurt Dr. R.Th.H. Willemsen RBA VBA vertegenwoordiging in de Raad voor de Jaarverslaggeving Drs. W. Heineken RBA VBA vertegenwoordiging in DSI Dr. R.Th.H. Willemsen RBA Seniorenconvent Drs. R. Snoeker, voorzitter Bovenstaande personen zijn bereikbaar via het secretariaat van de VBA. Telefoon: 020 - 618 28 12

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VBA-Beroepsvereniging van Beleggingsdeskundigen Herengracht 479 1017 BS Amsterdam telefoon: 020 - 618 28 12

vba journaal 22e jaargang voorjaar 2006 The long-term risk and return effects of investing in private equity 12

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