do alternative ucits deliver what they promise? a comparison of alternative ucits and hedge funds
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Do alternative UCITS deliver what they promise? Acomparison of alternative UCITS and hedge fundsMichael Busacka, Wolfgang Drobetzb & Jan Tillea
a Absolut Research GmbH, Grosse Elbstrasse 277a, 22767 Hamburg, Germanyb School of Business, University of Hamburg, 20146 Hamburg, GermanyPublished online: 28 May 2014.
To cite this article: Michael Busack, Wolfgang Drobetz & Jan Tille (2014) Do alternative UCITS deliver what theypromise? A comparison of alternative UCITS and hedge funds, Applied Financial Economics, 24:14, 949-965, DOI:10.1080/09603107.2014.916386
To link to this article: http://dx.doi.org/10.1080/09603107.2014.916386
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Do alternative UCITS deliver what
they promise? A comparison of
alternative UCITS and hedge funds
Michael Busacka, Wolfgang Drobetzb and Jan Tillea,*aAbsolut Research GmbH, Grosse Elbstrasse 277a, 22767 Hamburg, GermanybSchool of Business, University of Hamburg, 20146 Hamburg, Germany
We study the performance of alternative UCITS funds and account for potentialsurvivorship biases in our sample in the best possible manner. Alternative UCITSfunds offer similar raw returns but a lower volatility compared to offshore hedgefunds. Single-index models show that alternative UCITS funds provide onlymarginal exposure to variations in hedge fund returns. Multifactor models indi-cate that the most important risk factors for both alternative UCITS funds andtheir matched hedge funds strategies are related to stock market risks, but alter-native UCITS funds exhibit a lower exposure to these factors than hedge funds.Moreover, we find factor loadings on different risk factors, suggesting thatalternative UCITS and hedge funds pursue different strategies. Finally, we assessthe degree of the value added for an investor in terms of enhanced diversificationbenefits by implementing a spanning test and find that both groups are differentasset classes with time-varying diversification properties.
Keywords: alternative mutual funds; UCITS funds; hedge funds; performancemeasurement
JEL Classification: G11; G23
I. Introduction
UCITS funds are mutual funds regulated by pan-Europeanguidelines that can easily be distributed throughoutEurope. The regulatory framework allows investments inseveral asset classes and a variety of derivative productsthat can be used to implement strategies previously knownfrom hedge funds. The interest in alternative mutual fundsin general and alternative UCITS funds in particular hasgrown in the wake of the financial crisis. According toBusack and Tille (2010), investors are longing for moreliquid, more transparent and more regulated alternativeinvestment products. At the same time, fund managersare looking for new distribution channels to broadentheir investor base and bolster up their assets previously
lost due to high investor redemptions (Eschenbacher,2010). Therefore, it is a timely research question to exam-ine the performance of a comprehensive sample of alter-native UCITS funds and to compare them to offshorehedge funds.
Analyzing the empirical performance of UCITS fundsthat follow alternative investment strategies and compar-ing them with offshore hedge funds can reveal insightswhether the UCITS structure is suitable for investors whoseek to invest in hedge fund strategies. This question is ofutmost practical importance, given the survey results inAmenc and Sender (2010) that a substantial portion ofEuropean investors are tightly constrained to invest inoffshore hedge funds, either by outright quantitativerestrictions or unfavourable tax treatment. Therefore,
*Corresponding author. E-mail: [email protected]
Applied Financial Economics, 2014Vol. 24, No. 14, 949–965, http://dx.doi.org/10.1080/09603107.2014.916386
© 2014 Taylor & Francis 949
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UCITS funds may be a tool to accommodate investmentsin alternative strategies because of their relatively largedegrees of freedom with respect to the use of derivatives.However, one could also argue that the UCITS frameworkis not suitable for alternative strategies. The frameworkcomes with higher liquidity and diversification require-ments, short selling restrictions and additional costs whichmight lead to a dilution of (offshore) hedge fund returns.Moreover, there are regulatory concerns whether a tightlyregulated product targeting retail investors should beallowed to follow hedge fund strategies (Eschenbacher,2010).
Several recent papers study the performance of alter-native mutual funds. Agarwal et al. (2009) compare USalternative mutual funds to hedge funds, and Huang andWang (2013) analyze alternative US equity funds. Busackand Tille (2010, 2012), Tuchschmid et al. (2010) andTuchschmid and Wallerstein (2013) extend this researchto a sample of UCITS funds. We contribute to this strandof literature in three ways. First, we account for a survivor-ship bias in the best possible manner and – unlike priorstudies – provide an estimate of the size of the survivor-ship bias for alternative UCITS funds. Second, we use alarge set of alternative UCITS strategies and comparethem to their matched offshore counterparts. Agarwalet al. (2009) and Huang and Wang (2013) analyze muchsmaller samples of alternative mutual funds. Moreover,while Tuchschmid et al. (2010) and Tuchschmid andWallerstein (2013) analyze alternative UCITS mainly onan aggregated basis, Busack and Tille (2010, 2012) inves-tigate the performance of these funds in a simple anddescriptive manner. Third, we provide a more detailedempirical analysis of the risk-return-profile of alternativemutual funds and evaluate the suitability of the UCITSframework to accommodate different hedge fundstrategies.
Our results indicate that alternative UCITS funds offersimilar raw returns but a lower volatility than offshorehedge funds during our full sample period. Single-indexmodels reveal that alternative UCITS funds provide onlymarginal exposure to variation in hedge fund returns.Using multifactor models, our results show that the mostimportant risk factors for both alternative UCITS fundsand hedge funds are related to stock market risks.However, alternative UCITS funds exhibit a significantlylower exposure to these factors than hedge funds; thisfinding is similar to Tuchschmid et al. (2010) andTuchschmid and Wallerstein (2013). Furthermore, wedocument that matched strategies load on different riskfactors, indicating that alternative UCITS and hedge fundspursue different strategies. For the time period after thefinancial crisis, we find that the returns of alternativeUCITS funds have become more sensitive to systematicrisk factors. At the same time, however, the performanceof these funds deteriorated. To our knowledge, we are the
first who estimate the survivorship bias for a sample ofalternative mutual funds. Our estimated bias is in line withresults from the mutual funds literature and indicates thatneglecting this bias can induce misleading results. Finally,we test whether alternative mutual funds and hedge fundsconstitute different asset classes by assessing the degree ofvalue added for an investor in terms of enhanced diversi-fication benefits. Using a spanning test, our results suggestthat both groups represent different asset classes withtime-varying diversification properties.
The remainder is structured as follows: Section II pro-vides an overview of the UCITS framework. Section IIIdescribes the data and our methodology, and Section IVpresents descriptive statistics. Section V compares therisk-return-profiles of UCITS funds and hedge funds.Section VI shows the results from mean-variance span-ning test. Finally, Section VII concludes.
II. The UCITS Framework
Development of the UCITS directive
The term ‘UCITS’ stands for ‘Undertakings for CollectiveInvestments in Transferable Securities’. The specialty ofUCITS compliant funds is that they are regulated accord-ing to a pan-European standard to facilitate cross-bordermarketing within the European Union. Although mostfunds are traditional mutual funds, the development ofthe European regulation allows implementing hedgefund-like strategies within the UCITS framework, whichhas led to an increased interest of fund managers andinvestors in the aftermath of the 2008 financial crisis.The UCITS framework was initially established in 1985(85/611/EEC). It contains rules and obligations concern-ing the setup and operation of funds (i.e. investment guide-lines, transparency and liquidity), focusing on theprotection of fund investors. Structuring a hedge fund-like product was not possible from the start of theUCITS directive, and only in 2001, the so-called productdirective (2001/108/EC) allowed the use of derivatives.However, the proper use of derivatives was not defined,leading to the 2004/383/EC commission recommenda-tion, where Article 7.3 explicitly allows holding cash-settled derivatives without the underlying instruments asa cover, thus enabling the use of synthetic short-selling.The eligible assets guideline published in 2007 (CESR 07-044) and the classification of hedge fund indexes as finan-cial indexes (CESR 07-433) further broadened the invest-ment universe of UCITS funds, enabling exposure tootherwise ineligible asset classes such as commodities orhedge funds. Recently, the directive 2009/65/EC alsoamended the UCITS framework; coming into force in2011, it focuses on reducing existing barriers to cross-
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border marketing. Finally, in 2012, the regulator publishedguidelines concerning the appropriateness of some finan-cial indexes as an underlying for derivatives (2012/832ESMA).
Possibilities and limitations for hedge fund strategiesunder the UCITS directive
The UCITS directive allows investments only in certainassets, which are specified in the product directive (2001/108/EC, 2002) and the eligible assets guidelines (CESR07-044 and CESR 07-433). In general, investments intransferable securities (e.g. stocks and bonds), moneymarket instruments, shares of other regulated funds,bank deposits and financial derivatives (listed and OTC)are eligible assets for UCITS funds. Using derivatives,funds can implement dynamic trading strategies, lever-aged positions and synthetic short sales. However, directphysical short-selling of securities is prohibited. Anotherdrawback of UCITS funds compared to hedge funds is thatoutright leverage by borrowing for investment purposes isnot allowed (85/611/EEC, Art. 36 No. 1). Therefore,leverage can only be obtained indirectly through deriva-tives’ margin deposits. This indirect leverage can becomequite large, especially if the so-called absolute VaRapproach is applied, thus mitigating the limitationsimposed by the prohibition of direct leverage.Nevertheless, indirect leverage is subject to certain limits,as specified in Art. 21 No. 3 85/611/EEC, 2004/282/ECand CESR 10-788, and it further needs to be incorporatedin the risk management process a UCITS fund needs toimplement.1
The applicability of financial indexes and the classifica-tion of hedge fund indexes as financial indexes (CESR 07-044 and CESR 07-433) allowed UCITS funds to invest inotherwise ineligible assets (e.g. single hedge funds as anunderlying for a derivative instrument) and opened up thepossibility to implement a variety of strategies that werethought to be implemented in offshore funds only. Theregulator tried to resolve the resulting misuse of tailoredindexes in ESMA guideline 2012/832, which sets strictrules for financial indexes. Most important, a financialindex must offer sufficient transparency to enable real-time tracking. In addition to these new guidelines, theimplementation of strategies within the UCITS frameworkis practically limited by liquidity constraints and diversi-fication requirements. UCITS funds have to provide
investors the possibility to redeem their shares at least infortnightly intervals (Simmonds, 2011). Moreover, in con-trast to hedge funds, UCITS funds are generally notallowed to use gates and side pockets. Article 22 of direc-tive 85/611/EEC specifies the diversification requirementsand limits position sizes and OTC counterparty exposures.In general, a UCITS fund is not allowed to invest morethan 5% of its assets into securities issued by the samebody, and the maximum OTC counterparty exposureamounts to 10% if the counterparty is a credit institution(Art. 22 No. 1 85/611/EEC).2
All these restrictive features (such as short selling,leverage, liquidity and diversification) arguably lead toinferior performance of UCITS funds compared to hedgefunds due to missing liquidity premiums, reduced bet sizeand increased frictions and costs, among other reasons.Overall, the regulatory framework for UCITS providesdifferent degrees of suitability to implement the varietyof hedge fund strategies. According to Simmonds (2011),equity strategies, global macro, managed futures, eventdriven (mainly merger and index arbitrage) or (liquid)credit strategies can be implemented within the UCITSframework. Amenc and Sender (2010) analyze whetherhedge funds are constrained by the required VaR-limitsand find that about 85% of all live hedge funds in theirsample remain inside theses limits. Summing up, theUCITS framework seems to be able to accommodatemost hedge fund strategies, and therefore, potentially pro-viding investors an access to hedge fund return profiles.
III. Data Selection and EmpiricalMethodology
Fund sample selection
Data have been collected from several sources. The mainsource for information on alternative UCITS funds is theAlternative Strategy Funds database from AbsolutResearch GmbH. This database was established in June2009 and focuses on institutional investor share classes.As one of the largest sources available on alternativeUCITS funds, it contains data on 1082 individual fundsbetween January 2002 and April 2012.3 Funds that ceasedto exist prior to 2009 are generally not included in thedatabase. However, after the inception of the database,
1 The limits of indirect leverage depend on the methodology chosen to calculate market exposure via derivatives. A manager can choosebetween three different methodologies, depending on the complexity of the strategy and derivatives used. Leverage can either becalculated using the ‘commitment approach’, the ‘relative VaR-approach’ or the ‘absolute VaR approach’ and is limited to either 200% ofthe funds’ NAV, twice the 99%-VaR of a derivative-free reference portfolio or 20%, calculated as the 20-day 99%-VaR, subject to theresults of stress tests.2 The former 5% limit can be increased to 10% if the sum of all positions larger than 5% does not exceed 40%.3 Individual fund means that only one share class per fund is included. It is common that fund companies launch different share classes fordifferent investor groups (e.g. retail and institutional).
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information on defunct funds was stored. We exclude sixshort-biased funds, five funds with missing data and all138 funds of funds, which left us with a sample of 933funds. Absolut Research only stores qualitative data (e.g.names, strategies, ISINs, etc.), but relies on third partydata vendors for the net asset values. Unlike for traditionallong-only mutual funds, there is no agreed upon definitionand categorization of alternative UCITS funds.4 To com-plement the data from Absolut Research and to collect afund sample as large as possible, we obtain a snapshotcopy from the Morningstar Direct Online Database (as of20 April 2011). The list includes 2346 live funds (withmultiple share classes) from the categories ‘EURAbsoluteReturn’, ‘Non-EUR Absolute Return’, ‘Long/Short’ and‘Other’ with information, among others, on namesand ISINs. As the snapshot was taken prior to the re-classification of Morningstar’s alternative and absolutereturn categories in May 2011, funds were manually reca-tegorized afterwards using the local Morningstar websitesto match them with the strategies from the AbsolutResearch database; 47 additional individual funds wereadded to the sample.
Data on alternative UCITS is prone to survivorshipbias, particularly for the years prior to 2009. Busack andTille (2010) note that their analysis might be positivelyinfluenced by a survivorship bias, although they includethe performance of 16 defunct funds. Tuchschmid et al.(2010) and Tuchschmid andWallerstein (2013) conjecturethat survivorship bias should not be of great importance asUCITS compliant funds are obliged to report returns andbecause their measured attrition rate is quite small (0.6%and 1.3%, respectively). With only 3 and 13 funds havingceased to exist, respectively, both studies simply excludethese funds from their analyses. In an attempt to providemore insight on survivorship issues prior to 2009, wesearch for defunct funds using the Bloomberg database.Our search criteria are similar to those chosen in Agarwalet al. (2009). In particular, we search for the keywordsneutral, absolute, short, regardless, independent, deriva-tive, futures, hedge and options for all funds labelled asUCITS compliant and available for sale in Germany.5
After validating the funds’ strategies, using names,descriptions and fund documents, we add another 34
funds to the funds already obtained from AbsolutResearch and Morningstar and obtain a total sample of1014 (= 933 + 47 + 33) funds, of which are 797 live and217 defunct by the end of April 2012.
Our sample includes rules-based funds that offer accessto long-short investment strategies; these funds’ invest-ment processes strictly adhere to a systematic process anddefines fully transparent trading rules.6 The AbsolutResearch database focuses on funds’ institutional shareclasses. However, this share class may not necessarily bethe one with the longest available track record. Bloombergstores information on inception dates of all related shareclasses, and we use this information to obtain the shareclass with the longest track record.7
Noneuro denominated share classes are converted intoEuros using end of the month exchange rates. We chosethe Euro as our denomination currency and analyze theperformance from the viewpoint of a European investorbecause it is not allowed to distribute UCITS funds to USretail investors. Moreover, by converting returns intoEuros we assume that investors do not hedge currencyrisks. Returns are computed as discrete returns from theend of month total return net asset values. Net asset valuesare reported after management fees and expenses, but theydisregard one-time fees such as entry or exit fees. Totalreturn net asset value data as well as exchange rates weretaken from the Bloomberg database. Our hedge fund indexdata are the Hedge Fund Research (HFR) indexes fromBloomberg.
There are two problems that may arise from our searchalgorithm. The first problem is that we obtain only currentfund names and investment strategies, but fund names andinvestment strategies can change over time. For example,Cooper et al. (2005) document that mutual funds changetheir name (and sometimes also their style) to increasefund flows by adopting a name that reflects an en vogueinvestment style. It is, therefore, possible that a fund has along track record, but that this track record is not repre-sentative for the fund’s current investment strategy. As weuse data prior to database inception, we screen all fundsfor name changes in Bloomberg and attempt to checkwhether a name change has been accompanied by a strat-egy change (using fund documents, press releases or direct
4 Morningstar started to introduce new categories for alternative investment strategies in May 2011. Prior to this date, alternative UCITSfunds were either categorized as long/short or absolute return funds. Lipper started to introduce a new range of alternative categories inJuly 2012 as a response to the growing universe of UCITS compliant funds with matching strategies. Although there are somesimilarities, category names differ between these two data providers.5 We limit this search to funds available for sale in Germany to facilitate data collection, as funds available for sale in Germany have toprovide fund documents in German language. Given that UCITS shall enhance cross-border marketing and that Germany is one of thelargest European markets for this type of regulated products, we are confident that our searches yield representative results.6 This is mainly done using a financial index, whose performance is swapped into the fund. However, we also include a few funds that tryto replicate the returns of hedge fund indexes using quantitative models.7 If multiple share classes had the same inception dates, we use the following procedure to select a share class: (i) we select the share classwhose net asset value is calculated using the fund’s base currency; (ii) if there are still multiple share classes left, we select the one with thelowest management fee. However, if a share class showed a management fee of 0%, the share class with the next lowest publishedmanagement fee was selected. This approach guarantees a fair performance comparison, as an investor only receives the net performance.
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contact to fund companies). Following this screening pro-cedure, we only include the relevant history where a fundfollowed an alternative investment strategy. We identify207 funds with changing names, and in 51 instances, thesename changes were accompanied by a strategy change.
Another potential problem may arise as funds canchange their legal structure, either by a re-domiciliationor a change of their investment guidelines to conform tothe UCITS regulation, but keep their acquired trackrecord. We identify 33 funds that changed their legalstructure and opted to conform to the UCITS guidelinesbut maintained their prior track record. Given the smallnumber of these cases, we include the prior performance toget time series that are as long as possible. One couldargue that the performance prior to adopting the UCITSguidelines is not representative for a fund’s current per-formance, possibly due to the tighter UCITS regulation(such as prohibition of physical short sales). However, theregulator allows funds to disclose past performance in thecase of re-domiciliation and changes of investment guide-lines only if the (voluntary) investment restrictions andstrategy prior to and after adopting the UCITS structure donot differ materially. Therefore, we expect potential biasesfrom including prior non-UCITS performance to be small.
Empirical methodology
Our analyses use both single-index models and a seven-factor model in the spirit of Fung and Hsieh (2004) forperformance measurement. Single-index models imple-ment regressions of equally weighted UCITS portfolioexcess returns on excess returns of matched hedge fundindexes. We use the HFR index description to match theUCITS categories with hedge fund indexes. UCITS cate-gories were taken from the Absolut Research fund classi-fication because there is no common industry standardhow to classify alternative UCITS funds. For EquityHedge or Event Driven, the matching is straightforward;however, in all other cases, we use the hedge fund indexclosest to an alternative mutual fund’s category. We useequally weighted portfolios to increase sample size asthere are a number of funds that do not report assets on amonthly basis to Bloomberg. The HFR indexes are alsoconstructed using equally weighted returns.
Extending the single-index framework, we further esti-mate a seven-factor model for UCITS funds and hedgefunds to compare their different factor exposures:
ri;t ¼ αi þ βi1W Mkti;t þ βi2W SMBi;t
þ βi3CE10Yi;t þ βi4CECSPREADi;t
þ βi5PTFSBDi;t þ βi6PTFSFXi;t
þ βi7PTFSCOMi;t þ εi;t
(1)
where ri;t denotes either the excess return on an equallyweighted UCITS portfolio or the excess return on a HFRindex. Our model differs from Fung and Hsieh (2004) inthat it uses the Fama and French (2012) global market(W_Mkt) and global size (W_SMB) factors instead of theexcess returns of the S&P 500 as well as the differencebetween the Russell 2000 Index and the S&P 500 Index.8
Moreover, we use European rather than US bond marketfactors. The two bond market variables are the change in10-year European government bond yields (CE10Y) andthe change in European credit spreads (CECSPREAD),which is the difference between the yield of the iBoxxEuro Corporate Bond 7–10 years Index and the 10-yearEuro government bond yield. Finally, we use the threetrend following variables from Fung and Hsieh (2001,2004) to account for dynamic trading strategies: thebond (PTFSBD), foreign exchange (PTFSFX) and com-modity (PTFSCOM) trend following factors.9 Finally, εi;tis a mean zero error term. All factor portfolio returns areconverted into Euros as the numeraire currency.
Both the single-index and seven-factor models useexcess returns on equally weighted portfolios as thedependent variable. We use the one-month Euribor ratefrom Bloomberg to calculate excess returns. The Famaand French (2012) market factor (W_Mkt) is recomputedaccordingly, as they use the one-month US Treasury billrate to calculate excess returns for their global marketfactor. Hedge fund index performance is converted intoEuros. We use both the investable as well as the non-investable hedge fund indexes from HFR in our compar-ison between hedge fund and alternative mutual fundperformance. Because investable hedge funds are openfor new investors, they resemble the closest investmentalternative for investors who want to invest in alternativemutual funds.10 Noninvestable hedge fund indexes con-tain all reporting funds and thus can be viewed as a bench-mark when assessing overall management skills. Fundswith superior performance might choose to limit theinflow of new capital to avoid distorting effects frombecoming too large.11
8 Data are provided on the website of Kenneth French (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).9 The trend following factors are taken from http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-FAC.xls.10 The investable HFR hedge fund indexes (HFRX series) is considered to be a financial index in the sense of the UCITS directive (2001/108/EC, 2002 and CESR 07-433). Therefore, the index performance could be obtained by using suitable derivatives. UBS launched arange of Exchange Traded Funds on several HFRX indexes in 2012.11 Naik et al. (2007) show that hedge fund strategies only provide outperformance up to a certain size and that increased inflows lead todeclining alphas over time.
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IV. Data Description and Data Biases
Descriptive statistics
We consider only funds with a performance history of atleast 12 months, reducing our initial sample from 1014 to900 funds. From these 900 funds, 710 were extant at theend of our sample period as of 30 April 2012. Although ithas been possible to set up alternative mutual funds sinceimplementation of the UCITS III directive, the trend tolaunch alternative mutual funds started to take off with theevolution of the recent financial crisis. Panel A of Table 1depicts the number of funds available at the end of eachyear. While there were a only moderate amount of fundsprior to 2007, the number of funds more than doubledbetween 2008 and 2010, arguably due to discussions oftighter hedge fund regulation (Amenc and Sender, 2010)or higher liquidity of UCITS (no gates and side pockets).Nonetheless, following this boom, it now seems that thereis a phase of consolidation as the total number of funds hasdecreased towards the end of our sample period. As
expected, the largest group are equity long/short fundsfollowed by equity market neutral funds (see Panel B),arguably because these strategies are perceived to be leastimpaired by UCITS regulation (Amenc and Sender,2010). Approximately 50% of the funds were domiciledin Luxembourg, emphasizing Luxembourg’s role asEuropean’s largest fund domicile (see Panel C).
Panel D of Table 1 provides an overview of the perfor-mance differences for live and defunct funds. During oursample period, 190 UCITS funds ceased to exist, a numberthat is much higher than the 3 and 13 defunct funds inTuchschmid et al. (2010) and Tuchschmid andWallerstein(2013), respectively, as well as the 16 defunct funds inBusack and Tille (2010). These funds were either liqui-dated, merged into other funds or stopped reporting NAVsto Bloomberg. Most likely, all of these actions are perfor-mance related. In fact, the average annualized return oflive funds is 1.6%. In contrast, defunct funds lost 2.5% peryear, on average, resulting in a difference of approxi-mately 4% per year (statistically significant at the 1%
Table 1. Number of funds, strategy breakdown, domiciles and performance of live and defunct funds
Panel A: growth of alternative UCITS Panel B: strategy breakdown (April 2012)
Year # funds Strategy # funds
December 2002 41 Equity long/short 188December 2003 69 Equity market neutral 69December 2004 90 Event driven 23December 2005 131 Fixed income long/short 64December 2006 203 Fixed income global macro 42December 2007 296 Fixed income multi-strategy 39December 2008 416 Multi-asset CTA 41December 2009 560 Multi-asset global macro 60December 2010 734 Multi-asset multi-strategy 59December 2011 758 Alternative beta 14April 2012 710 Volatility 32
Commodity 26Foreign exchange 53
Panel C: fund domiciles (April 2012) Panel D: live and defunct funds
Domicile # funds Descriptive statistics All Live Defunct Difference p-value
Luxembourg 340 # funds 900 710 190Ireland 116 Return (%, p.a.) 0.706 1.556 −2.471 4.027 0.000France 99 SD (%, p.a.) 7.897 7.769 8.375 −0.606 0.154Germany 70 Sharpe ratio 0.276 0.378 −0.105 0.483 0.000UK 39 Skewness −0.252 −0.215 −0.389 0.174 0.021Others 46 Excess kurtosis 1.954 1.689 2.945 −1.256 0.000
Alpha (%, p.a.) −1.570 −0.950 −3.888 2.938 0.000R-square 0.673 0.677 0.658 0.020 0.000
Notes: Panel A of Table 1 provides an overview of the number of funds that were extant at the end of each calendar year and at theend of the sample period in April 2012. Panels B and C report the numbers of funds according to fund strategies and country ofdomicile at the end of the sample period. Panel D shows the average annualized return, the average SD, the average Sharpe ratio,the average skewness and excess kurtosis, the seven-factor alpha (Fung and Hsieh, 2001) and the average adjusted R-square for liveand defunct funds in the seven-factor model over the entire live span of individual funds. The p-values for differences in means arecalculated from standard t-tests.
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level). Interestingly, dead funds do not exhibit signifi-cantly higher SDs, but investors are likely to face moreextreme losses as returns are more negatively skewed andexhibit higher excess kurtosis. In terms of Sharpe ratio,surviving funds significantly outperform nonsurvivingfunds. Based on their risk-adjusted returns (annualizedseven-factor alphas), alternative UCITS underperform by1.6% per year. While surviving funds also exhibit a nega-tive alpha, on average, they significantly outperform theirnonsurviving peers (−0.9% versus −3.9%).Panel A of Table 2 presents several risk and return
statistics for different portfolios of alternative mutual fundstrategies. Mutual fund portfolios are calculated based onequally weighted monthly returns. Column 2 showsannualized monthly raw returns between January 2002and April 2012. Similar to hedge funds, alternative mutualfunds as a group are quite heterogeneous. During the fullsample period, FX strategies provide the lowest returnswith only 0.47% per year, while macro-oriented fixedincome funds earn 5.6% per year. The SD of the overallalternative UCITS portfolio is low (with 2.7% per year),but in line with Busack and Tille (2010, 2012),Tuchschmid et al. (2010) and Tuchschmid andWallerstein (2013). Volatility itself shows a large variationacross different strategies. While fixed income long/shortfunds exhibit an annualized SD of only 1.6%, multi-assetCTA (managed futures) funds boast a SD of 8.4%.Although the SD of equity long/short funds is only 6%per annum, the maximum drawdown is as high as 15.7%.In general, funds’ downside deviations (i.e. the SD ofreturns below a certain threshold, which we set to zero)are comparable to the corresponding SDs, thus the Sharpeand Sortino ratios also do not differ materially. Moreover,Panel B of Table 2 contains the lower partial moment oforder one (LPM-1), indicating the expected return below acertain threshold, which we again set to zero. This measureranges from 0.1% for fixed income long/short and volati-lity strategies to 0.7% for managed futures. The last col-umn contains the p-value for a Jarque-Bera normality test.For the full sample of alternative mutual funds, returns donot significantly deviate from normality. On a strategylevel, this is also the case for equity long/short, equitymarket neutral and FX funds. All other alternative mutualfund strategies exhibit significant deviation from normality.
Panels B and C of Table 2 show descriptive statistics forinvestable and noninvestable hedge funds, respectively.Investable hedge funds lag behind noninvestable hedgefunds in terms of raw returns. While the HFRI CompositeIndex shows an average annual return of 2.55%, its invest-able counterpart lost 1.36% per year. As the SD is ofcomparable magnitude, investable hedge funds also lagbehind in terms of risk-adjusted performance. Like most
alternative mutual funds, hedge fund returns deviate sig-nificantly from a normal distribution.
Comparing Panel A with Panels B and C, it becomesapparent that alternative mutual funds exhibit comparablereturns to noninvestable hedge funds and outperforminvestable hedge funds on a raw return basis during thesample period. While the average annualized return of theglobal alternative mutual fund portfolio is 2.58%, invest-able hedge funds lost 1.36% per year, resulting in anannual return gap of roughly 4%. Hedge funds displayhigher SDs, and they also suffer from more severe lossesthan alternative UCITS funds, as indicated by the max-imum drawdown statistics. Hedge funds expose investorsto more severe tail risks, i.e. higher expected tail losses andlower Omega ratios. Hedge funds also exhibit significantdeviations from normality. Therefore, considering thelower risk measures of alternative UCITS funds, it is notunlikely that the outperformance of UCITS funds issample-specific and driven by risk reduction. We calculatethe same statistics for the period from January 2009 toApril 2012 and find that at least noninvestable hedgefunds exhibit, much higher returns than alternativeUCITS funds, on average (not shown). While the HFRIComposite has an annual return of 10.2% and a SDof 10.2% during these later sample years, the correspond-ing moments for UCITS are only 2.7% and 2.6%,respectively.
Survivorship bias in alternative UCITS
The most prominent database biases are survivorship bias,selection bias and backfilling bias (Fung and Hsieh, 2000).The survivorship bias is possibly the best documented biasin the mutual and hedge fund literature.12 AbsolutResearch started to collect data for alternative UCITSfunds only from June 2009 onwards. As we incorporateperformance prior to this date in our analysis, we presum-ably introduce some survivorship bias. Our search algo-rithm limits this bias. Resulting in larger samplescompared to Agarwal et al. (2009), Busack and Tille(2010, 2012), Tuchschmid et al. (2010) and Tuchschmidand Wallerstein (2013), it further mitigates the selectionbias. Although we are not able to fully avoid or provide anestimate of the magnitude of this bias, it should be ofminor importance. UCITS is a retail format, thus fundmanagers have an incentive to appear in a commercialdatabase to reduce investors’ search costs.
Another bias that Fung and Hsieh (2000) discuss forhedge funds and Evans (2010) documents for mutualfunds is the instant history, backfilling or incubationbias. Again, we are not able to estimate the magnitude ofthis bias. A possibility would be to eliminate some early
12 For example, see Brown and Goetzmann (1995), Malkiel (1995), Elton et al. (1996), Griese and Kempf (2003), Ackerman et al.(1999), Brown et al. (1999) and Fung and Hsieh (2000), among others.
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sample months, which comes at the cost of losing valuabledata points. Fung and Hsieh (2009) argue that such acorrection would not only be costly in terms of samplesize but most likely also introduce other forms of dataerror. Evans (2010) shows that the effect of incubation bias
on aggregate fund returns is quite small. The value-(equal-) weighted difference in four-factor alphas between1996 and 2005 is only 0.11% (0.84%) per year.
Following Griese and Kempf (2003), we construct buy-and-hold portfolios of surviving funds as well as all funds
Table 2. Times series descriptive statistics
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Return SD SkewnessExcesskurtosis
Sharperatio
Sortinoratio
Omegaratio
Downsidedeviation
Maximumdrawdown LPM-1
JBp-value
Panel A: alternative mutual fundsAlternative UCITS 2.580 2.694 −0.407 −0.281 0.958 0.988 1.960 2.612 −6.450 0.224 0.148Equity long/short 4.166 5.944 −0.175 0.044 0.701 0.741 1.671 5.622 −15.738 0.518 0.726Equity market neutral 2.266 2.890 −0.056 0.871 0.784 0.824 1.824 2.749 −6.892 0.229 0.136Event driven 2.547 2.368 −1.547 4.227 1.075 0.842 2.212 3.023 −7.062 0.175 0.000Fixed income long/short 2.265 1.553 0.808 2.243 1.459 1.895 3.255 1.195 −1.193 0.084 0.000Fixed income globalmacro
5.568 3.877 0.482 1.318 1.436 1.743 3.155 3.193 −4.298 0.215 0.001
Fixed incomemulti-strategy
1.924 2.711 −0.494 1.183 0.710 0.664 1.720 2.896 −8.305 0.223 0.002
Multi-asset CTA 3.496 8.420 0.823 3.488 0.415 0.482 1.413 7.254 −9.894 0.705 0.000Multi-asset global macro 2.578 5.560 −0.623 3.184 0.464 0.420 1.444 6.144 −14.513 0.484 0.000Multi-asset multi-strategy 1.567 4.069 −1.597 11.087 0.385 0.297 1.422 5.272 −14.012 0.309 0.000Alternative beta −1.432 6.565 −0.943 2.083 −0.218 −0.187 0.844 7.662 −22.618 0.764 0.000Volatility 2.673 1.990 −1.853 11.986 1.344 0.983 3.450 2.719 −3.389 0.091 0.000Commodity 0.636 8.694 −1.540 4.039 0.073 0.059 1.062 10.809 −16.710 0.860 0.000Foreign exchange 0.472 2.556 0.060 −0.647 0.185 0.193 1.139 2.443 −6.013 0.282 0.327
Panel B: investable hedge fundsHFRX global hedge fundindex
−1.357 9.992 0.053 1.530 −0.136 −0.138 0.902 9.815 −33.296 1.151 0.002
HFRX equity hedgeindex
−2.528 10.436 −0.150 0.917 −0.242 −0.238 0.833 10.637 −35.609 1.263 0.090
HFRX equity marketneutral index
−3.380 11.349 0.569 2.416 −0.298 −0.314 0.790 10.761 −41.565 1.340 0.000
HFRX event driven index −0.139 10.579 −0.076 1.456 −0.013 −0.013 0.990 10.747 −29.229 1.160 0.004HFRX relative valuearbitrage index
−0.955 11.358 −0.070 1.635 −0.084 −0.080 0.938 12.003 −48.042 1.279 0.001
HFRX macro/CTA index −0.264 11.605 0.216 0.611 −0.023 −0.023 0.983 11.500 −24.418 1.292 0.235
Panel C: noninvestable hedge fundsHFRI fund weightedcomposite index
2.550 9.860 −0.065 0.994 0.259 0.266 1.216 9.603 −23.316 0.983 0.074
HFRI equity hedge index 1.575 10.506 −0.119 0.702 0.150 0.151 1.120 10.415 −27.291 1.095 0.242HFRI equity marketneutral index
−1.057 10.595 0.204 2.261 −0.100 −0.106 0.923 9.982 −32.202 1.145 0.000
HFRI event driven index 3.471 10.561 −0.224 1.481 0.329 0.337 1.285 10.315 −25.947 1.015 0.002HFRI relative valuearbitrage index
2.915 10.316 0.249 1.568 0.283 0.298 1.242 9.790 −23.338 1.002 0.001
HFRI macro/CTA index 3.285 9.968 0.669 2.749 0.330 0.387 1.304 8.484 −14.985 0.902 0.000HFRI macro systematicdiversified index
4.208 12.271 0.652 3.536 0.343 0.362 1.314 11.610 −25.489 1.118 0.000
Notes: The table contains descriptive time series statistics for alternative mutual funds (Panel A) as well as investable and noninvestablehedge funds (Panels B and C). All statistics are calculated using monthly data. Statistics for alternative mutual funds are calculated usingequally weighted monthly fund returns. Statistics for hedge funds are calculated using the respective HFR indexes. All returns areconverted into Euro returns. Column 1 contains annualized monthly raw returns (in %), Column 2 the annualized SD (in %). Columns 3and 4 show the values for skewness and excess kurtosis. Sharpe ratio (Column 5), Sortino ratio (Column 6), Omega ratio (Column 7),annualized downside deviation (Column 8, in %) and LPM-1 (Column 9, in %) are calculated using a return threshold of zero. Maximumdrawdown (Column 10, in %) is the maximum peak to trough loss. Column 11 contains p-values of the Jarque-Bera test for normality inmonthly returns. Statistics for Alternative Beta and commodity funds are calculated from April 2007 to April 2012 and March 2008 toApril 2012, respectively, due to the availability of funds following these strategies.
956 M. Busack et al.
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in our sample (surviving and defunct) available at the startof different investment periods. Assuming that investorshold equally weighted portfolios of all funds available atthe start of an investment period and allocate the moneyfrom terminated funds equally among surviving funds, thereturn difference is interpreted as survivorship bias. Ourfirst investment period begins in January 2002, the last inJanuary 2012.13 Table 3 shows the annualized return dif-ferences between the two buy-and-hold portfolios. Forexample, the value of 0.95% (Column 5, Row 3) impliesthat an investor who analyzes an equally weighted buy-and-hold portfolio of all surviving funds from 2003 untilthe end of 2005 overestimates his return by 0.95% per yearcompared to a buy-and-hold portfolio that contains allfunds active at the beginning of 2003. The differencebetween both portfolios is positive for every investmentperiod in our sample. There are only six evaluation periodsfor which the difference is not statistically significant; inall other cases, the difference is significant at least at the5% level.
Over the full sample period, the average annualizedperformance difference is 0.65%. Considering only theyears from 2009 to 2012, the period after AbsolutResearch started collecting and maintaining data; thisvalue does not change much, but is slightly lower withan average value of 0.55%. This is an indication that oursearch algorithm works well in detecting nonsurvivingfunds prior to collecting data. Our values for the survivor-ship bias are closer to the values found in the mutual fundsliterature than to those reported in the hedge fund litera-ture. It is close to the 0.80% in Brown and Goetzman’s(1995) for equally weighted mutual fund portfolios, and itis also of comparable magnitude to Elton et al.’s (1996)estimates that vary between 0.60% and 0.77% when theyapply a reinvestment assumption. Our estimate is slightlyhigher than the average value of 0.40% reported in Grieseand Kempf (2003) for their sample of German equityfunds.
In addition to the return differences, Table 3 also indi-cates the number of funds available at the beginning andend of each investment period. Apparently, fund failurerates increase during the sample period. Funds which wereactive at the beginning of 2002 show an average failurerate of 1.8% per year. In general, the yearly attrition ratefor funds is around 2% until the end of 2008, on average.This value increased to 4% for funds that were alive at thebeginning of 2007 and even climbed to 7% for funds thatwere active at the beginning of 2009. This development ispartly attributable to the market turmoil following thefinancial crisis. Therefore, the survivorship bias hasbecome more important in recent years. Our results are
in contrast to those in Tuchschmid et al. (2010) andTuchschmid and Wallerstein (2013), who document anattrition rate of only 0.6% for the time span of June 2009until May 2010 and 1.6% over the period June 2009 untilFebruary 2012 (or 0.6% per year). The differences areattributable to our more extensive search algorithm fordeceased funds. All in all, our results indicate that neglect-ing nonsurviving funds leads to biased results.
V. Performance of Alternative UCITS andHedge Fund Portfolios
Single-index model results
A concern of an investor who considers investing in alter-native mutual funds is whether they effectively provideaccess to the corresponding hedge fund strategies andsimilar risk-return-profiles. Because of regulatory restric-tions (see Section II), it is possible that alternative UCITSfunds ultimately do not deliver what they promise. Toanswer this question, we regress monthly excess returnsof alternative UCITS portfolios on the excess returns ofthe matched HFR indexes. If both categories are closesubstitutes, regression coefficients and R-squares shouldbe in the vicinity of one.While sensitivity coefficients nearone but with low R-squares indicate that there are differentsources of risk in alternative UCITS funds, low coeffi-cients with high R-squares imply a reduced exposuretowards the respective hedge fund strategy. Low andinsignificant coefficients together with low R-squareswould suggest that alternative UCITS are not able at allto deliver exposure to traditional hedge fund strategies.14
The regression results from single-index models areshown in Table 4. Panel A contains results where perfor-mance is measured against the respective investable hedgefund index, while in Panel B regressions are repeatedusing the noninvestable hedge fund indexes as regressors.
Over the full sample period, most of the alternativeUCITS strategies fail to provide a substantial exposure tothe respective hedge fund strategies, both investable andnoninvestable. In particular, the estimated coefficientsrange from zero for fixed income long/short funds toonly 0.4 for equity long/short strategies. Considering allfunds as a group, the estimated exposure towards hedgefunds is below 0.2 (Column 3 in Panels A and B).Interestingly, alternative UCITS are slightly more exposedto noninvestable hedge fund returns than to investableones. Although slope coefficients are generally low, theyare statistically significant in most cases. The generally
13 To work with a sample as large as possible, we do not require funds to have a minimum track record of 12 months for this analysis.14 Alternatively, low R-squares might also indicate that the selected benchmark indexes are not representative for measuring theperformance of UCITS fund portfolios.
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Tab
le3.
Survivorship
bias
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2002 (38)
1.74
1%**
*(38)
1.59
1%**
*(38)
1.38
9%**
*(38)
1.22
0%**
*(37)
1.07
2%**
*(37)
0.91
6%**
*(37)
0.68
6%**
*(36)
0.71
7%**
*(36)
0.71
9%**
*(35)
0.60
4%**
*(32)
0.58
5%**
*(31)
2003 (42)
1.30
7%**
*(42)
1.07
0%**
*(42)
0.95
4%**
*(41)
0.81
9%**
*(41)
0.71
7%**
*(41)
0.47
9%**
(40)
0.53
5%**
(40)
0.55
2%**
*(39)
0.44
1%**
(36)
0.42
7%**
(35)
2004 (69)
0.63
6%(69)
0.75
9%**
*(68)
0.61
2%**
(68)
0.54
9%**
*(66)
0.44
9%**
(64)
0.47
0%**
*(63)
0.46
8%**
*(62)
0.36
7%**
*(59)
0.35
4%**
(57)
2005 (95)
0.60
7%**
*(94)
0.45
8%**
*(93)
0.51
2%**
*(90)
0.53
0%**
*(87)
0.54
9%**
*(83)
0.51
5%**
*(82)
0.41
9%**
*(79)
0.39
9%**
*(76)
2006 (137
)0.49
9%(135
)0.51
3%**
*(130
)0.54
1%**
(126
)0.60
1%**
*(118
)0.57
0%**
*(114
)0.47
4%**
*(108
)0.44
6%**
*(104
)20
07 (21 1)
0.52
2%**
(207
)0.45
7%(202
)0.54
6%**
(190
)0.47
6%**
(183
)0.40
6%**
(173
)0.37
5%**
(164
)20
08 (314
)0.30
2%(308
)0.71
4%(284
)0.58
9%**
(266
)0.45
8%**
(243
)0.41
6%**
(232
)20
09 (427
)1.63
8%**
*(401
)1.111%
***
(369
)0.77
6%**
(334
)0.71
0%**
(313
)20
10 (579
)0.62
8%**
*(542
)0.55
7%**
*(492
)0.48
5%**
*(458
)20
11 (762
)0.41
4%**
*(700
)0.35
2%**
*(653
)20
12 (824
)0.10
8%(789
)
Notes:T
hetabledisplays
annu
alized
return
differences(%
)betweenbu
y-and-ho
ldpo
rtfolio
sof
survivingfund
sandallfun
dthatwereactiv
eatthestartofan
investmentp
eriod.The
first
columnshow
sthestartofaninvestmentperiodandthefirstrow
theendof
aninvestmentperiod.**
/***
indicatessign
ificanceatthe5%
/1%
level.New
ey–W
estS
Esareused
tocalculate
p-values
ofaveragemon
thly
return
differences.Num
bersin
bracketsindicatethenu
mberof
fund
savailablein
therespectiv
eyear.
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low R-squares indicate that alternative UCITS returns aredriven by additional or other risk factors than traditionalhedge fund returns. Equity long/short is the only groupthat is able to deliver notable, although still relatively low,hedge fund exposure.
In terms of risk-adjusted performance, most alternativeUCITS portfolios exhibit positive, but insignificantalphas. The small outperformance corresponds to our ear-lier results in Table 2, indicating higher raw returns andSharpe ratios for several alternative UCITS portfolios.However, it is in contrast to Agarwal et al. (2009) whofind a risk-adjusted outperformance of hedge funds.Tuchschmid et al. (2010) and Tuchschmid andWallerstein (2013) also do not report evidence of signifi-cant underperformance. Given stricter regulation andprobably higher associated costs of UCITS, this
observation is clearly surprising. Therefore, we checkwhether our findings are influenced by the choice of theEuro as the numeraire currency for computing returns.Re-estimating all regression models, our results remainqualitatively unchanged (not shown).
Seven-factor model results
A lack of hedge fund exposure does not automaticallymake alternative UCITS funds obsolete. If they load ondifferent risk factors, they can still add value to an inves-tor’s portfolio through diversification. As single-indexregressions only capture a small part of the entire returnvariation of alternative UCITS funds, we proceed by usingour seven-factor model and analyze whether additionalsources of risk drive the variation in alternative UCITS
Table 4. Single-index model
(1) (2) (3) (4) (5) (6)
αi t(αi) βi t(βi) Adj. R2 Matched index
Panel A: UCITS versus strategy matched investable hedge fundsAll 0.001 1.100 0.173*** 9.227 0.388 HFRX global hedge fundEquity long/short 0.003* 1.779 0.345*** 7.816 0.356 HFRX equity hedgeEquity market neutral 0.000 0.288 0.059** 2.584 0.044 HFRX equity market neutralEvent driven 0.000 0.459 0.068** 2.384 0.077 HFRX event drivenFixed income long/short 0.000 0.029 0.013 0.715 0.000 HFRX relative value arbitrageFixed income global macro 0.003** 2.460 0.121*** 3.666 0.122 HFRX macro/CTAFixed income multi-strategy 0.000 0.082 0.132*** 5.030 0.282 HFRX relative value arbitrageMulti-asset CTA 0.001 0.842 0.150*** 2.787 0.035 HFRX macro/CTAMulti-asset global macro 0.000 0.261 0.095* 1.909 0.030 HFRX macro/CTAMulti-asset multi-strategy 0.000 −0.247 0.126*** 2.899 0.086 HFRX global hedge fundAlternative beta −0.002 −0.781 0.341*** 6.586 0.354 HFRX global hedge fundVolatility 0.000 0.839 0.045*** 2.784 0.057 HFRX relative value arbitrageCommodity 0.000 −0.048 0.238** 2.610 0.128 HFRX macro/CTAForeign exchange −0.001* −1.948 0.129*** 6.899 0.326 HFRX macro/CTA
Panel B: UCITS versus strategy matched noninvestable hedge fundsAll 0.000 0.308 0.195*** 11.627 0.479 HFRI fund weighted compositeEquity long/short 0.002 1.302 0.410*** 10.568 0.516 HFRI equity hedgeEquity market neutral 0.000 0.265 0.090*** 4.147 0.099 HFRI equity market neutralEvent driven 0.000 0.183 0.075*** 2.797 0.098 HFRI event drivenFixed income long/short 0.000 −0.055 0.006 0.312 −0.007 HFRI relative value arbitrageFixed income global macro 0.003** 2.114 0.158*** 3.807 0.157 HFRI macro/CTAFixed income multi-strategy 0.000 −0.486 0.122*** 3.998 0.197 HFRI relative value arbitrageMulti-asset CTA 0.001 0.516 0.073 0.769 0.003 HFRI macro systematic divers.Multi-asset global macro 0.000 0.077 0.137 1.629 0.051 HFRI macro/CTAMulti-asset multi-strategy −0.001 −0.750 0.171*** 2.968 0.163 HFRI fund weighted compositeAlternative beta −0.003 −1.410 0.372*** 5.867 0.361 HFRI fund weighted compositeVolatility 0.000 0.542 0.022* 1.891 0.004 HFRI relative value arbitrageCommodity −0.001 −0.388 0.170* 1.705 0.045 HFRI macro/CTAForeign exchange −0.002*** −2.867 0.160*** 4.991 0.378 HFRI macro/CTA
Notes: The table contains results of single-index regressions of alternative UCITS fund portfolios against the matched hedge fund index(shown in Column 6). Results in Panels A and B are measured over the entire sample period (January 2002–April 2012), except forcommodity and alternative beta funds; for these funds, performance is measured betweenMarch 2008 and April 2012 and April 2007 andApril 2012, respectively, due to availability of funds following the respective strategies. Column 1 shows the estimated monthly alphas.Column 2 contains the t-statistics of two-sided tests of alpha, where the null hypothesis is that alpha is zero. Column 3 shows theestimated beta against the respective hedge fund index, while Column 4 contains the t-statistics of the test that beta in respect to the hedgefund index is zero. SEs are calculated using heteroscedasticity- and autocorrelation-consistent (HAC) SEs following Newey and West(1987). Column 6 displays adjusted R-squares.
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returns and whether these sources differ between hedgefunds and alternative UCITS funds. Agarwal et al. (2009)show that the factor exposures of hedgedmutual funds andtraditional hedge funds do not differ significantly for mostfactors except for the size factor. Tuchschmid et al. (2010)find that a global index of UCITS hedge funds is signifi-cantly less exposed to equity market risk and credit spreadrisk compared to hedge funds. Our analysis not only con-siders a global fund portfolio, but rather a broad set ofdiverse alternative mutual fund strategies. Therefore, weare able to analyze risk exposures in a more detailed way(and on a strategy level). The seven-factor model inEquation (1) is estimated using the Seemingly UnrelatedRegression (SUR) technique.15 Table 5 shows the resultsfor the seven-factor model using the alternative UCITSfund portfolios (Panel A) and hedge fund indexes (PanelsB and C). The table presents the estimated betas with SEsin parentheses as well as adjusted R-squares for eachestimated equation. Moreover, in Panel A, the last row ofeach equation reports the p-values from Wald coefficienttests, testing for differences between the estimated sensi-tivity coefficients for alternative UCITS and the matchedhedge fund indexes.
Over the full sample period, factor exposures of alter-native UCITS funds vary across the different strategies,but are largely as expected (Panel A). Equity-orientedstrategies load significantly on the equity risk factors,while fixed income strategies tend to load on the bondand credit factors. Managed futures show significant expo-sure to two of the three trend following factors. The signsof the estimated factor coefficients are as expected, that ispositive for equity market risks and negative for bondmarket and spread risks as converging spreads and fallingyields will lead to higher prices of the respective securi-ties. The results for equity long/short UCITS and for theaggregated portfolio of UCITS funds indicate that equitylong/short funds dominate the sample as the results arequite similar.
Panels B and C reveal that different hedge fundstrategies exhibit similar factor exposures. In particular,while the estimated coefficients on market and size arealmost always significant, the coefficients on the otherfactors are generally insignificant across the differenthedge fund strategies. This is clearly a surprising resultgiven hedge funds’ diverse investment strategies as wellas the different underlying markets/assets they areassumed to invest in (with global macro being anexception). In terms of risk-adjusted performance(alpha), alternative UCITS neither significantly under-nor outperform, which is similarly the case for most ofthe noninvestable hedge fund indexes. In contrast,
investable hedge funds largely underperform, exhibitingsignificantly negative alphas of 30 basis points permonth for the global index and varying between 0(managed futures) and 50 for Equity Market Neutralfunds. The observable underperformance of investablehedge fund indexes may indicate that superior fundmanagers close their funds to new investors, as pro-posed by Naik et al. (2007).
The regression model delivers R-squares between 0.30and 0.75 for the different alternative UCITS strategies,which is similar to the explanatory power reported inAgarwal et al. (2009), Tuchschmid et al. (2010) andTuchschmid and Wallerstein (2013). The R-squares forhedge fund indexes are generally higher (ranging from0.6 to 0.9) than for alternative UCITS funds. Therefore,it seems that alternative UCITS funds are less exposed tothe standard sources of hedge fund risk. As a robustnesscheck, we run regressions of excess returns of the equityhedge strategies (market neutral and long/short) onexcess returns of the 10 MSCI World sector indexes.In results not shown, we find that alternative UCITS andoffshore hedge funds load on different sector indexes.This observation provides another piece of evidence thatalternative UCITS and offshore funds pursue differentinvestment strategies.
Size and statistical significance of the coefficientsalone do not provide an indication whether alternativeUCITS funds and hedge funds are really different.Therefore, we implement Wald tests where each indivi-dual coefficient of a given UCITS strategy is compared tothe coefficient of its matched hedge fund index. Over thefull sample period and over all funds, there are severalsignificant differences between alternative UCITS fundsand hedge funds. In fact, alternative UCITS funds sig-nificantly outperform investable hedge funds and are sig-nificantly less exposed to equity markets and small capstocks, which makes them potentially better diversifiersfor an investor’s traditional equity risk. For example, theestimated coefficient on the size factor is around 0.1 foralternative UCITS, while it is around 0.6 for hedge funds.Small cap stocks are more illiquid and riskier than largecap stocks (Amihud, 2002); thus, they may be inappropri-ate for alternative UCITS funds. Furthermore, the expo-sure of UCITS funds against the credit spread factordiffers from hedge funds for most strategies. For UCITSfunds, the estimated coefficient is mostly negative (longspread risks), while it is mostly positive for hedge fundindexes (short spread risks). With respect to the trend-following factors, there are usually no significant differ-ences, except for macro-related UCITS strategies and thebond as well as commodity trend-following factors. The
15 As the right-hand side is equal for all equations, this approach is equivalent to OLS estimation equation by equation (Zellner, 1962).Using SUR has the main advantage that we obtain a combined coefficient covariance matrix that can be used for Wald coefficient tests todirectly compare individual coefficients of UCITS portfolios with the coefficients of matched indexes.
960 M. Busack et al.
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Tab
le5.
Seven-factormod
el (1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
αW_M
ktW_S
MB
CE10Y
CSP
READ
PTFSB
DPTFSF
XPTFSC
OM
Adj.R
2
Pan
elA:alternativeUCIT
Sfunds
All
0.00
0(0.000
)0.00/0.96
0.135*
**(0.010
)0.00
/0.00
0.066*
**(0.011)
0.00
/0.00
−0.003(0.002
)0.29/0.16
−0.006*
**(0.001
)0.11/0.00
−0.001
(0.003
)0.63/0.81
0.00
3(0.002
)0.10/0.43
0.001(0.003
)0.64
/0.85
0.748
Equ
itylong
/sho
rt0.00
1(0.001
)0.00/0.04
0.315*
**(0.023
)0.17
/0.00
0.077*
**(0.025
)0.00
/0.00
0.000(0.005
)0.47/0.25
−0.007*
*(0.003
)0.09/0.01
−0.008
(0.007
)0.26/0.63
0.00
3(0.005
)0.40/0.75
0.007(0.007
)0.29
/0.17
0.725
Equity
marketn
eutral
0.000(0.001)
0.01/0.07
0.073*
**(0.018
)0.66
/0.10
0.080*
**(0.020
)0.00
/0.00
0.001(0.004
)0.07/0.27
−0.001(0.002
)0.00/0.00
−0.004
(0.005
)0.85/0.87
0.00
2(0.004
)0.82/0.47
−0.001(0.005
)0.52
/0.56
0.251
Event
driven
0.00
0(0.000
)0.04/0.64
0.093*
**(0.011)
0.00
/0.00
−0.002(0.013
)0.00
/0.00
−0.005*
(0.003
)0.40/0.36
−0.007*
**(0.001
)0.02/0.01
−0.005
(0.003
)0.85/0.69
0.00
3(0.002
)0.21/0.56
−0.004(0.003
)0.66
/0.64
0.587
Fixed
incomelong
/sho
rt0.00
0(0.000
)0.02/0.98
0.007(0.009
)0.00
/0.00
0.006(0.010
)0.00
/0.00
−0.010*
**(0.002
)0.25/0.31
−0.006*
**(0.001
)0.07/0.11
0.01
0***
(0.003
)0.11/0.33
0.00
3(0.002
)0.15/0.03
−0.005*
(0.003
)0.51
/0.62
0.338
Fixed
incomeglob
almacro
0.00
2**(0.001
)0.17/0.72
0.049*
*(0.023
)0.09
/0.00
0.161*
**(0.026
)0.00
/0.00
−0.015*
**(0.005
)0.01/0.03
−0.008*
**(0.003
)0.00/0.00
−0.009
(0.007
)0.01/0.01
0.00
2(0.005
)0.75/0.13
−0.006(0.007
)0.01
/0.00
0.321
Fixed
incomemulti-strategy
−0.00
1(0.001
)0.05/0.39
0.008(0.016
)0.00
/0.00
0.106*
**(0.017
)0.00
/0.00
−0.016*
**(0.004
)0.05/0.05
−0.012*
**(0.002
)0.47/0.00
−0.004
(0.005
)0.60/0.62
0.00
4(0.003
)0.08/0.01
−0.006(0.005
)0.51
/0.66
0.400
Multi-assetC
TA0.003(0.002)
0.15/0.99
−0.041(0.052
)0.01
/0.00
−0.019(0.057
)0.00
/0.00
0.008(0.012
)0.69/0.97
−0.015*
*(0.006
)0.00/0.00
0.04
4***
(0.015
)0.53/0.27
0.04
8***
(0.011)
0.00/0.01
0.019(0.015
)0.46
/0.14
0.264
Multi-assetg
lobalm
acro
0.000(0.001)
0.79/0.22
0.265*
**(0.027
)0.02
/0.00
0.054*
(0.030
)0.00
/0.00
−0.010(0.006
)0.05/0.15
−0.008*
*(0.003
)0.00/0.00
0.00
1(0.008
)0.05/0.12
0.00
1(0.006
)0.82/0.13
0.003(0.008
)0.05
/0.05
0.557
Multi-assetm
ulti-strategy
−0.00
1(0.001
)0.04/0.56
0.214*
**(0.021
)0.14
/0.00
−0.039*
(0.023
)0.00
/0.00
0.003(0.005
)0.89/0.86
0.002(0.002
)0.64/0.34
0.00
3(0.006
)0.99/0.75
−0.00
1(0.004
)0.47/0.87
0.00
3(0.006
)0.60
/0.76
0.504
Volatility
0.000(0.000)
0.02/0.90
0.006(0.012
)0.00
/0.00
0.004(0.013
)0.00
/0.00
−0.002(0.003
)0.73/0.95
−0.009*
**(0.001
)0.17/0.02
−0.001
(0.003
)0.48/0.83
0.00
2(0.003
)0.15/0.03
−0.008*
*(0.003
)0.64
/0.83
0.320
Foreign
exchange
−0.00
2***
(0.000
)0.55/0.00
0.040*
**(0.013
)0.05
/0.00
0.138*
**(0.015
)0.00
/0.00
0.000(0.003
)0.23/0.80
−0.002(0.002
)0.01/0.00
−0.002
(0.004
)0.02/0.03
−0.00
1(0.003
)0.97/0.03
0.000(0.004
)0.02
/0.01
0.483
Pan
elB:investab
lehedge
funds
HFRXglob
alhedgefund
−0.00
3***
(0.001
)0.265*
**(0.028
)0.656*
**(0.032
)0.004(0.006
)0.000(0.003
)0.00
3(0.008
)−0.00
7(0.006
)−0.002(0.008
)0.844
HFRXequity
hedge
−0.00
4***
(0.001
)0.366*
**(0.032
)0.597*
**(0.036
)0.006(0.007
)0.001(0.004
)0.00
4(0.009
)−0.00
4(0.007
)−0.004(0.009
)0.819
HFRXequity
marketn
eutral
−0.00
5***
(0.001
)0.052(0.040
)0.771*
**(0.044
)0.021*
*(0.009
)0.020*
**(0.005
)−0
.002
(0.011)
0.00
0(0.008
)−0.009(0.011)
0.764
HFRXeventd
riven
−0.00
3**(0.001
)0.319*
**(0.029
)0.677*
**(0.033
)0.001(0.007
)0.002(0.003
)−0
.007
(0.009
)−0.00
6(0.006
)−0.008(0.009
)0.853
HFRXrelativ
evaluearbitrage
−0.00
4**(0.002
)0.229*
**(0.042
)0.710*
**(0.047
)0.001(0.010
)−0.015*
**(0.005
)−0
.010
(0.012
)−0.011(0.009
)−0.013(0.012
)0.733
HFRXmacro/CTA
−0.00
1(0.002
)0.138*
**(0.052
)0.617*
**(0.058
)0.014(0.012
)0.014*
*(0.006
)0.03
2**(0.015
)−0.00
2(0.011)
0.033*
*(0.015
)0.609
Pan
elC:non
investab
lehedge
funds
HFR
Ifund
weightedcomposite
0.00
0(0.001
)0.370*
**(0.022
)0.598*
**(0.024
)0.004(0.005
)0.005*
(0.003
)0.00
0(0.006
)0.00
0(0.005
)0.000(0.006
)0.908
HFRIequity
hedg
e−0.00
1(0.001
)0.484*
**(0.021
)0.537*
**(0.023
)0.007(0.005
)0.002(0.002
)−0
.004
(0.006
)0.00
1(0.004
)−0.00
4(0.006
)0.924
HFRIequity
marketn
eutral
−0.00
3**(0.001
)0.136*
**(0.033
)0.737*
**(0.036
)0.011(0.007
)0.017*
**(0.004
)−0
.002
(0.009
)−0.00
4(0.007
)−0.007(0.009
)0.817
HFRIeventd
riven
0.00
0(0.001
)0.381*
**(0.024
)0.650*
**(0.027
)0.001(0.006
)0.002(0.003
)−0
.008
(0.007
)−0.00
1(0.005
)−0.008(0.007
)0.899
HFRIrelativ
evaluearbitrage
0.00
0(0.001
)0.205*
**(0.032
)0.716*
**(0.035
)−0.003(0.007
)0.000(0.004
)0.00
1(0.009
)−0.01
3*(0.007
)−0.009(0.009
)0.821
HFRImacro/CTA
0.00
2(0.001
)0.150*
**(0.032
)0.626*
**(0.036
)0.002(0.007
)0.011*
**(0.004
)0.01
7*(0.009
)0.01
3*(0.007
)0.024*
*(0.009
)0.795
HFRImacro
system
atic
diversified
0.00
3(0.002
)0.276*
**(0.051
)0.593*
**(0.056
)0.008(0.012
)0.027*
**(0.006
)0.02
1(0.015
)0.00
5(0.011)
0.050*
**(0.015
)0.666
Notes:The
tablecontains
theestim
ated
coefficientsusingtheSURapproach,S
Es(inparentheses)andtheadjusted
R-squares
foraseven-factor
modelfollo
wingFu
ngandHsieh
(2004).
W_M
ktistheFam
aandFrench(2012)
excess
return
ofaglobalstockmarketportfolio
.W_SMBistheexcess
return
ofaportfolio
ofglobalsm
allcapstocks
over
globallargecapstocks.
CE10Yisthechange
inthe10-yearE
uropeangovernmentbondyield.CSP
READisthechange
ofthedifference
betweentheyieldof
theiBoxxEuroCorporateBond7–10
yearindexandthe
10-yearEuropeangovernmentbond
yield.
PTFSB
D,P
TFSF
XandPTFSC
OM
aretheFungandHsieh
(2001)
bond,foreign
exchange
andcommodity
trendfollo
wingfactors.Colum
n9
show
stheadjusted
R-squares.PanelAcontains
theresults
forequallyweightedalternativeUCITSfund
portfolio
sandPanelBforthe
matched
hedgefund
indexes.InPanelA
,the
lastrowfor
each
regression
displays
p-values
forWaldcoefficient
testswhether
theestim
ated
coefficient
ofthealternativeUCITSportfolio
differsfrom
therespectiv
ematched
HFRX/HFRIindex.
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corresponding coefficients are insignificant for UCITSfunds, but significant for macro hedge funds. The differ-ences for the commodity trend-following factor are likelyto be explained by the fact that UCITS funds are notallowed to invest in physical commodities and commod-ity futures. In addition, commodity swaps and certificateson commodities were not readily available during theentire sample period.
Subsample results
Our sample period is heterogeneous; in fact, there is a bullmarket between 2003 and mid-2007, followed by marketdisruptions during the recent financial crisis and theiraftermath. Presumably, hedge funds and alternativeUCITS funds will have time-varying factor exposuresand performance differences over time. Furthermore, asillustrated in Panel D of Table 1, the growth of alternativeUCITS funds really took-off after the collapse of LehmanBrothers and the Madoff fraud, and it has been since thenwhen more and more hedge fund managers have recog-nized the potential of the UCITS framework to set up retailhedge funds. As more funds became available, it is morelikely that a convergence between hedge funds and retailfunds has occurred, enabling investors to better accesshedge fund-like payoff profiles. As a robustness check,we therefore re-estimate the prior two models for thepeculiar subsample period January 2009 until April2012. For the sake of brevity, the results are not tabulated.
In the single-index model, the estimated coefficientsduring the 2009–2012 subperiod do not change notablyand remain by and large of the same magnitude. In termsof convergence, as measured by the R-square, we findmixed evidence. The R-square for the aggregated portfo-lio, which includes all UCITS funds, does not increase andthus indicates that no convergence has taken place. TheR-square measured against the HFRX index actuallydeclines from 0.4 to roughly 0.3, while it remains stablewhen the HFRI index is used (0.45 versus 0.48). In con-trast, on a strategy level, we find several substantialincreases in explanatory power. This is particularly thecase for fixed income strategies, equity market neutraland managed futures. On the other hand, the R-squarefor equity long/short UCITS funds declines, which is asurprising result given that equity long/short hedge fundstrategies are generally believed to be most suitable forreplication (Amenc and Senders, 2010).
Using the seven-factor model, we find that the R-squareincreases from 0.75 to 0.81 for the global alternativeUCITS portfolio. Hedge funds’ R-squares also increasefrom 0.85 to 0.89 (HFRX Global) and from 0.9 to 0.93(HFRI Composite). Standard hedge fund risk factors havebecome more important at the end of our sample period,both for alternative UCITS and hedge funds. Moreover,the R-square of UCITS funds is now closer to the R-square
of hedge fund indexes. Combining this observation withthe decreased and lower R-squares in the single-indexregressions, we conclude that alternative UCITS fundsand hedge funds share the same underlying pool of stra-tegies and markets (high seven-factor model R-squares),but invest differently at least to some extent. On a strategylevel, we find only one strategy whose R-square has notincreased (managed futures). Consistent with the single-index results, the most notable increases in R-square areobservable for fixed income and equity market neutralUCITS funds. One reason why the R-squares for UCITShave increased is that equity risks and spread risks havebecome more important in terms of statistical significance.Different alternative UCITS strategies now tend to load inthe same way on these risk factors, which implies that theyhave become more similar to each other over time. Theother risk factors are usually insignificant, except bondyields for fixed income funds and some trend followingfactors for macro-related UCITS funds. The increase inthe significance of systematic risk factors likely explainsthe decrease in estimated alphas. Although alphas remaininsignificant for most UCITS strategies, they are lowercompared to the estimated alphas during the full period.Huang and Wang (2013) similarly document reducedalphas during times of crisis for a sample of US hedgefund-like mutual funds.
The exposures and R-squares of hedge funds remainlargely unchanged during the 2009–2012 subsample per-iod. Market and size risks are still the dominant risk factorsfor hedge funds. However, the coefficients on market riskslightly decreased, while the coefficients on the size factorslightly increased. The coefficients on the other factors arealmost always insignificant. Contrary to UCITS funds,hedge funds now exhibit better performance. In fact, theunderperformance of investable hedge funds vanished,and noninvestable hedge funds even outperformed alter-native UCITS funds. Wald coefficient tests reveal thatdifferences between hedge funds and UCITS funds remainsignificant, again indicating that they follow different stra-tegies. An example is spread risks, which is important foralternative UCITS but less important for hedge funds.Moreover, UCITS funds are still significantly less exposedto equity market risk factors, particularly when comparedto noninvestable hedge funds. Macro-related UCITSfunds are also less exposed to the returns from trend-following strategies as the corresponding coefficients aresignificantly lower than those of macro hedge funds.
Summing up, our findings underline that hedge fundsand alternative UCITS funds represent quite distinctinvestment products that adopt different strategies. Froman investor’s point of view, who wants to invest in hedgefunds but is constrained to invest in onshore funds by theregulatory authorities, this result may be disappointing. Itis even more so as the growing supply of funds did notchange this basic results.
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VI. Mean-Variance Spanning Tests
Our results so far indicate that even strategies that havevery close matching hedge fund indexes (equity long/short, equity market neutral, managed futures or eventdriven) do not necessarily share the same risk character-istics. This leads to our final question whether alternativeUCITS funds and hedge funds really constitute differentasset classes. Therefore, we implement mean-variancespanning tests following Gibbons et al. (1989). TheirGRS-test tests the joint hypothesis that all regressionintercepts for a portfolio of test assets regressed on abenchmark portfolio are equal to zero. This test is eco-nomically equivalent to testing whether the Sharpe ratio ofa portfolio consisting of the benchmark assets signifi-cantly changes by adding the test assets.16 We use bothinvestable and noninvestable hedge funds as benchmarkassets and alternative UCITS funds as test assets. The testsare implemented for the full sample period and two sub-periods (January 2002–December 2008 and January2009–April 2012) to see whether diversification proper-ties changed over time. Table 6 summarizes the results ofour spanning tests, whereW denotes the test statistic of theGRS-test.
The first two columns in Table 6 corroborate our con-jecture that alternative UCITS funds and hedge funds areseparate asset classes, as the GRS-test rejects the spanningnull hypothesis for almost every case. An exception is thelast subperiod, where investable hedge funds form thebenchmark portfolio. This result again suggests that alter-native UCITS fund do not deliver what they promise.Because alternative strategies are usually no stand-aloneinvestments, we further test whether they are able toimprove the Sharpe ratio of a traditional stock-bond port-folio (denotes as SB). Taking the position of a Europeaninvestor, we use the MSCI Europe Total Return Index asour stock portfolio and the iBoxx Eurozone SovereignsTotal Return Index as our bond portfolio. We then testwhether UCITS funds are able to shift the efficient frontierof a portfolio that, in addition to stocks and bonds, alsoincludes hedge funds. Over the full sample period, bothalternative UCITS and noninvestable hedge funds are ableto significantly shift the Sharpe ratio of an investor’sstock-bond portfolio, implying that they serve as diversi-fiers for traditional investments. Investable hedge funds,however, are not able to generate a significant shift of thetraditional portfolio’s efficient frontier (p-value of 0.365).Most important, an inclusion of alternative UCITS funds
Table 6. Mean-variance spanning test
Period
(1) (2) (3) (4) (5) (6) (7)
UCITSversus HFRX
UCITSversus HFRI
UCITSversus SB
HFRXversus SB
HFRIversus SB
UCITS versusSB + HFRX
UCITS versusSB + HFRI
2002‒2012W 2.487 2.294 3.137 1.102 3.418 2.761 2.620p(W) 0.008 0.015 0.001 0.365 0.002 0.003 0.005|a| 0.001 0.001 0.001 0.003 0.001 0.001 0.001R2 0.376 0.476 0.263 0.069 0.086 0.459 0.516
2002–2008W 2.143 1.814 2.766 1.571 4.509 2.543 1.786p(W) 0.029 0.069 0.005 0.167 0.000 0.010 0.075|a| 0.001 0.001 0.001 0.005 0.002 0.001 0.001R2 0.379 0.462 0.255 0.186 0.183 0.478 0.501
2009–2012W 0.613 2.308 0.936 1.461 4.375 0.559 1.800p(W) 0.817 0.046 0.533 0.223 0.002 0.856 0.122|a| 0.001 0.002 0.001 0.005 0.007 0.001 0.001R2 0.597 0.621 0.197 0.020 0.017 0.656 0.684
Notes: The table reports the results frommean-variance spanning tests.W is the normalized GRS-test statistic from Gibbons et al. (1989);it tests whether the estimated intercepts in the regressions are simultaneously equal to zero. p(W) is the p-value of the test statisticW. |a| isthe average absolute value of the regression intercept (monthly alpha). R2 is the average R-square. All returns are excess returns (using theone-month Euribor) and denominated in Euro. The models in Columns 1 and 2 regress UCITS strategy portfolios (test assets) on hedgefunds indexes (benchmark assets). In the models in Columns 3–5, fund portfolio returns are regressed on a portfolio of traditional assetsconsisting of stocks and bonds (labelled SB; MSCI Europe TR Index and iBoxx Eurozone Sovereigns TR). The last two models inColumns 6 and 7 show the results for regressions of UCITS portfolios on a benchmark portfolio including traditional assets as well asinvestable and noninvestable hedge funds, respectively.
16 Implicit in this interpretation is the assumption that there exists a risk-free rate, which transforms the efficient frontier into a straight line(Gibbons et al., 1989).
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into a portfolio consisting of stocks, bonds and a set ofhedge fund strategies leads to a significant shift of theefficient frontier and thus an improvement of an investor’sreturn-risk spectrum. Therefore, we again conclude thatalternative UCITS funds and hedge funds are distinct assetclasses.
Analyzing the two subperiods, we observe that theresult for alternative UCITS funds is driven by the resultsfrom the first subperiod (2002–2008). In the later 2009–2012 period, alternative UCITS strategies were able tochange the Sharpe ratio neither of a portfolio consistingof stocks, bonds and hedge funds nor of a traditionalstock-bond portfolio. During this period, governmentbonds exhibit strong returns due to drastic declines ininterest rates in the wake of central banks’ quantitativeeasing. In contrast, the spanning test results for hedgefunds suggest that their inclusion into a traditional stock-bond portfolio significantly shifts the Sharpe ratio duringthe 2009–2012 time period; the test rejects for noninvest-able hedge funds, albeit not for investable ones. Thisresult is most likely driven by the strong performanceof relative value arbitrage hedge funds that profited froma sharp tightening of credit spreads beginning in 2009and extending through 2012. Overall, alternative UCITSand hedge funds seem to constitute different assetclasses. While alternative UCITS possess time-varyingdifferences in their diversification benefits towards tradi-tional assets, at least noninvestable hedge funds wereable to continuously act as a diversifying asset class.Considering the wider investment opportunities of off-shore hedge funds, these results confirm our ex anteexpectations.
VII. Conclusion
Alternative UCITS funds have gained popularity in theyears following the recent financial crisis. UCITS fundsare pan-European-regulated mutual funds that try toimplement strategies previously known from the hedgefund arena. Given the tendency for increased regulation ofalternative offshore funds in Europe, these funds are, atleast theoretically, a viable alternative to a variety of off-shore hedge fund strategies. We examine a comprehensivesample of alternative UCITS funds and also provide anestimate of the survivorship bias in the best possiblemanner. In terms of raw returns, alternative UCITS fundsdeliver comparable results to noninvestable hedge fundsand outperform investable hedge funds, at least over thefull sample period. Furthermore, they have lower SDs andsmaller tail risks. We further show a significant differencebetween live and defunct alternative UCITS funds.According to our estimates, the survivorship bias amountsto 0.5% per year.
We also compare the risk profiles of a comprehensiveset of alternative UCITS strategies and matching off-shore strategies. During the full sample period, alterna-tive UCITS funds deliver slightly better risk-adjustedreturns. However, towards the end of our sample per-iod, hedge funds, especially noninvestable funds, tendto outperform alternative UCITS funds. Furthermore,we show that the risk profiles of alternative UCITSfunds differ significantly from offshore hedge fundsand that alternative UCITS funds are not able to pro-vide an adequate exposure against offshore hedge fundstrategies. These results may be partly explained bydifferences in regulation and stricter risk limits of alter-native UCITS funds. However, we also find significantdifferences in common risk factor exposures betweenalternative UCITS funds and matched offshore funds,indicating that both are pursuing different strategies.Finally, we test whether alternative UCITS funds andhedge funds constitute distinct asset classes, followingthe test procedure by Gibbons et al. (1989). We docu-ment that alternative strategies can shift the efficientfrontier and significantly enhance the return-risk spec-trum of a traditional stock-bond portfolio, although thediversification benefits of alternative UCITS funds varyover time. As a result, a prudent investor should notgenerally rule out investments in one or the other assetclass.
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