Commonality in Hedge Fund Returns: Driving Factors and Implications

Tail risk of hedge funds: an extreme value application

Can We Really 'Clone' Hedge Fund Returns? Further Evidence M.Kooli & S. Sharma Hedge Fund Replication: Does Model Combination Help? J. Teiletche Factor-Based Hedge Fund Replication with Risk Constraints R.D.F.Harris & M. Mazibas Takeover Probabilities and the Opportunities for Hedge Funds and Hedge Fund Replication to Produce Abnormal Gains A.Ravi , P.Mayall & J. Simpson Benchmarking of Replicated Hedge Funds M.D.Wiethuechter & L. Nemeth Insight - Distributional Hedge Fund Replication via State Contingent Stochastic Dominance C.H. Glaffig Non-Parametric Hedge Funds and Replication Indices Performance Analysis: A Robust Directional Application L. Germain , N. Nalpas & A. Vanhems Hedge Fund Cloning through State Space Models R. Savona Hedge Fund Return Replication via Learning Models R. McFall Lamm, Jr Linear Model for Passive Hedge Fund Replication G. Barone-Adesi & S. Siragusa Can Hedge Fund-like Returns be Replicated in a Regulated Environment? I. Markov & N. Tuchschmid A Factor-based Application to Hedge Fund Replication M. Rossi & S.L. Rodriguez

Macroeconomic Risk and Hedge Fund Returns

A Total Risk Measurement Framework for Hedge Funds and Funds of Funds

This paper deals with a unique form of an alternative investment: hedge funds. Specifically, the paper researches the influence of certain factors on hedge fund returns. Specifically, four factors are tested: (1) inclusion of funds into market hedge fund index, (2) possibility to close to new investors, (3) type and level of charged fees and (4) reporting delay/concealment. The data are compiled for more than 4000 funds for 23 years and cover all types and statuses of hedge funds. The most important results found no significant additional yield for increased remuneration for fund administration. Additionally, the regression confirmed the hypothesis of the time return decay in most hedge funds (so-called backfill bias). The other two hypotheses of market inclusion and openness to new capital have not been validated on a significant level. Throughout the whole text, the paper describes typical problems that are connected with hedge funds and their respective data.

Many hedge funds claim to provide significant diversification for traditional portfolios, besides attractive returns. The authors provide empirical evidence regarding the return and diversification benefits of hedge fund investing using the CSFB/Tremont hedge fund indexes over 1994–2000. Like many others, they find that simple regressions of monthly hedge fund excess returns on monthly S&P 500 excess returns seem to support the claims about the benefits of hedge funds. The regressions show only modest market exposure and positive added value. This type of analysis can produce misleading results, however. Many hedge funds hold, to various degrees and combinations, illiquid exchange–traded securities or difficult–to–price over–the–counter securities. For the purposes of monthly reporting, hedge funds often price these securities using either the last available traded prices or estimates of current market prices. These practices can lead to reported monthly hedge fund returns that are not perfectly synchronous with monthly S&P 500 returns. Non–synchronous return data can lead to understated estimates of actual market exposure. When the authors apply standard techniques that account for this problem, they find that hedge funds in the aggregate have significantly more market exposure than simple estimates indicate. Furthermore, after accounting for this increased market exposure, they find that taken as a whole the broad universe of hedge funds does not add value over this period.

A persistent problem for hedge fund researchers presents itself in the form of inconsistent and diverse style classifications within and across database providers. In this article, single-manager hedge funds from the Hedge Fund Research (HFR) and Hedgefund.Net (HFN) databases are classified on the basis of a common factor extracted using the factor axis methodology. It is assumed that the returns of all sample hedge funds are attributable to a common factor that is shared across hedge funds within one classification, and a specific factor that is unique to a particular hedge fund. In contrast to earlier research and the application of principal component analysis (Fung and Hsieh [1997, 2001]), factor axis seeks to determine how much of the covariance in the dataset is attributable to common factors (commonality). Factor axis largely ignores the diagonal elements of the covariance matrix, and orthogonal factor rotation maximizes the covariance between hedge fund return series. In an iterative approach, common factors are extracted until all return series are described by one common and one specific factor. Prior to factor extraction, the series are tested for autoregressive moving-average processes, and the residuals of such models are used in further analysis to improve upon squared correlations as initial factor estimates. The methodology is applied to the July 1995 to June 2010 timeframe. The results indicate that the number of distinct style classifications is reduced in comparison with the arbitrary self-select classifications of the databases.

Dynamic Investment Strategies of Hedge Funds

Capacity constraints, investor information, and hedge fund returns

The main purpose of this chapter is to estimate a model for hedge fund returns that will endogenously generate failure probabilities using panel data where sample attrition due to fund failures is a dominant feature. We use the Lipper (TASS) hedge fund database, which includes all live and defunct hedge funds over the period January 1994 through March 2009, to estimate failure probabilities for hedge funds. Our results show that hedge fund failure prediction can be substantially improved by accounting for selectivity bias caused by censoring in the sample. After controlling for failure risk, we find that capital flow, lockup period, redemption notice period, and fund age are significant factors in explaining hedge fund returns. We also show that for an average hedge fund, failure risk increases substantially with age. Surprisingly, a 5-year-old fund on average has only a 65% survival rate.

Betas, Characteristics and the Cross-Section of Hedge Fund Returns

Return Smoothing and its Implications for Performance Analysis of Hedge Funds

Many studies on hedge fund returns are formulated in a static framework, resorting to a multifactor model using mimicking portfolios or benchmarks accounting for the style of different hedge fund strategies. While classical linear regressions represent a parsimonious approach to the modeling of hedge fund strategies, time-invariant factor loadings might be misleading (Berkelaar, Kobor, and Kouwenberg, The VaR Modeling Handbook, 2009). Actually, hedge fund strategies are essentially dynamic processes. Since conditional models appear promising to explain portfolio managers dynamic strategies, we study in this paper how conditional alphas and betas react to financial market variables such as market risk premium, interest rate, market volatility, or macroeconomic variables related to the business cycle (Racicot and Theoret, Journal of Wealth Management, 2007). An interesting assumption tested in the literature (Swinkels and van der Sluis, Working Paper, Tilburg University, 2001; Bollen and Whaley, Working Paper, Vanderbilt University, 2006; Racicot and Theoret, Journal of Wealth Management, 2008) is the conjecture that the conditional alpha and beta follow a random walk. Actually, those parameters might also react to conditioning financial market or macroeconomic news. Using the Kalman filter setting, our study explores the reaction of HFR indices conditional alpha and beta to financial market variables, especially interest rates, market risk premia, and squared market risk premia, a proxy for volatility. Our results show that the alphas of most of the indices computed over strategies are not very sensitive to financial market variables. A pure recursive process seems, therefore, more relevant for the alpha. The data generating process of the alpha is thus more similar to a random walk process than to a conditional model, Atl Econ J (2010) 38:377–378 DOI 10.1007/s11293-010-9230-6

Do hedge funds' exposures to risk factors predict their future returns?

Hedge fund returns are generally considered to be little correlated with market returns. Skills and dynamic strategies are claimed to generate more complex risk exposures that yield superior performance (alpha) or complementary sources of risk premium (alternative beta) through bear and bull markets by using a broad range of instruments, such as derivatives, leverage, short selling, and arbitrage across markets. This market neutrality feature of hedge funds would suggest that investing in hedge funds, either directly or through funds of hedge funds, could be an effective tool of portfolio diversification, hence making it appealing for a large range of institutional investors and high-wealth individuals.1 However, hedge funds (1) provide limited liquidity, as resources are usually “locked up” for 1–3 years; (2) impose high management fees (up to 5 percent a year); and (3) offer poor transparency.KeywordsHedge FundFinancial DistressCredit SpreadHedge Fund ManagerHedge Fund ReturnThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.