Hedge Fund Strategies Research Articles

VIX Futures and options: A Case Study of Portfolio Diversification During the 2008 Financial Crisis

In 2008, the S&P 500 experienced a drawdown of about 50% from peak to trough. Many assets which are typically considered effective equity diversifiers also faced precipitous losses. Most hedge fund strategies and commodity indices were not immune from declining. For example, the HFRX Global Hedge Fund Index had a maximum drawdown of approximately 25% of its value in 2008, with some of its sub-indexes dropping almost 60%. The drop in commodities was even more significant. The S&P GSCI commodity index experienced a maximum drawdown of about 2/3 of its value in 2008. In stark contrast, volatility levels as measured by VIX experienced significant increases and in 2008 repeatedly set new highs not seen since the crash of 1987. This study assesses the impact of a long VIX investment as a diversifier for a typical institutional investment portfolio during the 2008 credit crisis.

The Rise and Demise of the Convertible Arbitrage Strategy

This article analyzes convertible arbitrage, one of the most successful hedge fund strategies. The aim of the strategy is to exploit underpricing of convertible bonds by taking a long position in a convertible and a short position in the underlying asset. The authors find that convertible bonds are underpriced at the issuance dates; at the same time, short sales of underlying equity increase significantly. Both effects are stronger and more persistent for equity-like convertibles than for debtlike convertibles. Furthermore, short-sale pressures negatively affect stock returns around the announcement and issuance dates of convertibles. All these factors have likely contributed to the shift toward issuing more debtlike convertibles in recent years, which, in turn, has substantially lowered the returns from convertible arbitrage.

Fund of hedge funds portfolio selection: A multiple-objective approach

This paper develops a technique for fund of hedge funds to allocate capital across different hedge fund strategies and traditional asset classes. Our adaptation of the polynomial goal programming optimisation method incorporates investor preferences for higher return moments, such as skewness and kurtosis, and provides computational advantages over rival methods. We show how optimal allocations depend on the interaction between strategies, as measured by covariance, co-skewness and co-kurtosis. We also demonstrate the importance of constructing ‘like for like’ representative portfolios that reflect the investment opportunities available to different-sized funds. Our empirical results reveal the importance of equity market neutral funds as volatility and kurtosis reducers and of global macro funds as portfolio skewness enhancers.

Hedge Funds for Retail Investors? An Examination of Hedged Mutual Funds

AbstractRecently, there has been rapid growth in the assets managed by “hedged mutual funds”—mutual funds mimicking hedge fund strategies. We examine the performance of these funds relative to hedge funds and traditional mutual funds. Despite using similar trading strategies, hedged mutual funds underperform hedge funds. We attribute this finding to hedge funds’ lighter regulation and better incentives. Conversely, hedged mutual funds outperform traditional mutual funds. Notably, this superior performance is driven by managers with experience implementing hedge fund strategies. Our findings have implications for investors seeking hedge-fund-like payoffs at a lower cost and within the comfort of a regulated environment.

Volatility Exposure of CTA Programs and Other Hedge Fund Strategies

<i>This article examines the dependence of trend-following CTA performance on underlying market volatility, both quantitatively and conceptually. While it is generally believed that CTAs have a long volatility exposure, tests conducted by the authors indicate that it is not quite true. The perception that CTA strategies are long volatility came from academic research and became widespread among traders. The notion of volatility exposure is sometimes confused with a dependence on volatility levels. If a CTA makes money in periods of high volatility and loses in periods of low volatility, its performance depends on the level of volatility. Volatility exposure, on the other hand, means that the CTA makes money when volatility rises and loses money when volatility falls. Both effects have comparable strength, are directly related, and thus are quite easy to confuse. By the same token, they need to be studied together, as is done in this article. The authors note that volatility exposure—i.e., dependence on volatility changes—should not be confused with dependence on volatility levels.</i> <b>TOPICS:</b>Real assets/alternative investments/private equity, commodities, futures and forward contracts, performance measurement

Fixed-Income Portfolio Allocation Including Hedge Fund Strategies: A Copula Opinion Pooling Approach

This paper adapts Meucci9s [2006a, 2006b] copula opinion pooling (COP) framework to examine whether fixed income hedge fund strategies enhance the risk-return spectrum of traditional bond portfolios. In contrast to the Black-Litterman setup, the COP approach does not rely on linear dependencies, and avoids the problems associated with the assumption of normally distributed asset returns. We analyze three scenarios that represent investor expectations about the performance of fixed income portfolios, and we add fixed income hedge fund strategies such as fixed income arbitrage, convertible bond arbitrage, and distressed securities, given expected shortfall constraints. Our results suggest that investor market expectations and attitudes toward potential losses are both important in determining the relative weight of hedge funds in the optimal portfolio. Depending on the model parameters, the allocation to hedge funds can vary greatly, from 0% to 85%.

Polynomial Goal Programming and the Implicit Higher Moment Preferences of U.S. Institutional Investors in Hedge Funds

Polynomial goal programming (PGP) has been successfully introduced in the hedge fund allocation literature. The method is intuitive, and flexible enough to incorporate investor preferences in higher moments of the return distribution. However, until now, PGP could not be used to its full effectiveness because it requires quantification of real preference parameters toward those moments. Previously, those parameters were chosen without elaborate research. Our goal in this paper is to identify implied sets of preference parameters by using investors' choice of and the importance they attribute to risk and performance measures. We use three groups of institutional investors, pension funds, insurance companies, and endowments, and we derive implied sets of preference parameters in the context of a hedge fund portfolio optimization. To extract implied investor preference parameters, we determine optimal hedge fund portfolios according to the criteria set by those fund of hedge fund sponsors. We then fit implied preference parameters so that the PGP optimal portfolio is identical to the desired hedge fund portfolio. We can thus determine real preferences for the higher moments of the portfolio return distribution. With the obtained economically justified sets of preference parameters, we can use the well established PGP framework more efficiently to derive allocations that satisfy institutional investor expectations for hedge fund investments. Furthermore, the implied preference parameters enable fund of hedge fund managers and other managers to combine different hedge fund strategies optimally according to investor expectations. Finally, we can assess the importance of individual moments, as well as their marginal rates of substitution.

Does Hedge Fund Performance Persist? Overview and New Empirical Evidence

Abstract The contribution of this paper is to provide an overview and new empirical evidence on hedge fund performance persistence, which has been a controversial issue in the academic literature during the last several years. In the first step, we review recent studies and put them into a joint evaluation of hedge fund performance persistence. In the second step, the methodological framework developed in the overview is used to present new empirical evidence. We find different levels of performance persistence depending on the statistical methodology and the hedge fund strategy employed. In our study, performance persistence cannot be explained by the use of option‐like strategies, but it can be partially explained by survivorship and backfilling bias. Differences among hedge fund strategies might be explained by return smoothing. Finally, we develop a rationale for choosing between different methodologies to measure performance persistence and conclude that the multi‐period Kolmogorov‐Smirnov test is the most useful for evaluating performance persistence of hedge funds.

Hedgeové fondy a jejich výkonnost v období 1994-2008

Přispěvek představuje zakladni milniky vývoje a soucasný stav hedgeových fondů, vymezuje pojem hedgeový fond a přibližuje hlavni investicni strategie hedgeových fondů. V casti o problemech spojených s měřenim výkonnosti rozebira hlavni zkresleni indexů hedgeových fondů. Na zakladě průměrných rocnich výnosů, alfa faktoru modelu CAPM, Sharpova a Sortinova indexu analyzuje výkonnost hedgeových fondů v letech 1994-2008 v porovnani s indexy S&P500, MSCI World a Russell 2000. Hlavnim zjistěnim je skutecnost, že ackoliv některe strategie dosahly v analyzovanem obdobi vyssich výnosů v porovnani s vybranými tržnimi indexy hedgeove fondy jako celek nedocilily při zohledněni zkresleni výkonnosti specificke pro indexy hedgeových fondů vyssich výnosů oproti tržnim indexům. Potencial hedgeových fondů vsak spociva ve flexibilitě a schopnosti pružněji reagovat na extremni tržni podminky. Uvedenou skutecnost dokazuje jednak nižsi volatilita výnosů v analyzovanem obdobi a jednak nadprůměrna výkonnost hedgeových fondů v obdobi krizi na kapitalových trzich vzhledem ke zvoleným tržnim indexům.

Hedge Funds: Strategy, Risk and Performance Evaluation

The study presents an application of multivariate regime switching copula models, in order to model the joint distributions of selected hedge fund classes/strategies and traditional capital markets. Our empirical interest focuses on testing for the presence of any asymmetric dependence structures between these sample hedge fund classes and benchmark capital market returns. A number of different specifications of canonical vine copulas are incorporated into a Markov switching two-regime framework to capture hedge fund reactions in bear and bull market phases. We apply this empirical framework on core and complementary hedge fund strategies as well as on traditional asset classes and produce substantial evidence of asymmetric dependence structures between hedge fund strategies and conventional capital markets. Furthermore, our empirical results reveal a high dependence regime with high estimated correlations and a low dependence regime with lower estimated correlations.

Private Placement Life Insurance as a Tax-Managed Wealth Accumulation and Transfer Strategy

In recent years, there has been heightened interest on the part of high-net-worth individuals to increase their investments in alternative assets, and in hedge fund strategies in particular. But many hedge funds, particularly those focused on absolute return strategies, have a significant tax burden. In an attempt to offer a tax-managed investment solution to their high-net-worth clients, many wealth advisors are directing their clients to consider investing in alternative assets through private placement life insurance (PPLI) structures. In this article, the author defines the structure, provides some historical perspective on how it has developed, and evaluates its potential as a wealth management solution, pointing to both advantages and obstacles. <b>TOPICS:</b>Wealth management, real assets/alternative investments/private equity

A comparison of alternative approaches for determining the downside risk of hedge fund strategies

AbstractIn this study, we compare a number of different approaches for determining the Value at Risk (VaR) and Expected Shortfall (ES) of hedge fund investment strategies. We compute VaR and ES through both model‐free and mean/variance and distribution model‐based methods. Certain specifications of the models that we considered can technically address the typical characteristics of hedge fund returns such as autocorrelation, asymmetry, fat tails, and time‐varying variances. We find that conditional mean/variance models coupled with appropriate assumptions on the empirical distribution can improve the prediction accuracy of VaR. In particular, we observed the highest prediction accuracy for the predictions of 1% VaR. We also find that the goodness of ES prediction models is primarily influenced by the distribution model rather than the mean/variance specification. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:244–269, 2009

The Rise and Demise of the Convertible Arbitrage Strategy

This paper analyzes convertible arbitrage, one of the most successful hedge fund strategies. The aim of the strategy is to exploit underpricing of convertible bonds by taking a long position in a convertible and a short position in the underlying asset. We find that convertible bonds are underpriced at the issuance dates. At the same time, short sales of underlying equity significantly increase. Both effects are stronger and more persistent for equity-like than for debt-like convertibles. Furthermore, we find that short sales pressures negatively affect stock returns around announcement and issuance dates of convertibles. In our opinion, this contributed to the shift towards issuing more debt-like convertibles in recent years, which in turn substantially lowered the returns from convertible arbitrage.

Portfolio Optimization of Hedge Funds in a Downside Risk Framework (Optimisation de Portefeuille des Hedge Funds dans le cadre du Downside Risk)

This article examines the portfolio optimization of hedge funds in the downside risk framework in order to take into account the asymmetry of returns and the behavior of investors towards the risk which are not captured by the mean-variance model. By using the Credit Suisse/Tremont Hedge Fund database, the results showed that the downside risk measures have an impact on the composition of optimal portfolios and on the efficient frontier. The results also showed that the mean-variance model overestimates the risk because of the use of the variance as a risk measure, but the mean- semivariance model of Harlow (1991) underestimates the risk because of the cosemivariances measures used in this model. Likewise, the results obtained proved that the new mean-semivariance model provides a better optimization of hedge funds portfolios because it makes it possible to capture the non-normality of hedge funds strategies and the risk perception of investors which are not taken into account by the mean-variance model and also makes it possible to overcome the problem of inequality of the cosemivariances measures in the mean-semivariance model of Harlow (1991).

Liquidity shocks, size and the relative performance of hedge fund strategies

We examine whether the increase in the flow of capital to hedge funds over the period 1994–2005 had a negative impact on performance. More specifically, we study the relative performance of small versus large funds for each of the hedge fund strategies. Our results indicate that on an absolute return basis, small funds outperform large funds. On a risk-adjusted return basis, however, we find that large funds outperform small funds, and that large funds are also shown to hold less liquid assets and take on less systematic and idiosyncratic risk than small funds. Further, funds that experience positive liquidity shocks generally outperform those that experience negative liquidity shocks. We also find evidence that hedge fund managers that are aggressive in dealing with liquidity shocks perform better than hedge fund managers that are conservative in dealing with liquidity shocks.

Hedge Fund Structure, Skewness and Liquidity

The paper develops a model of the hedge fund industry in which the different fees charged (management fees and redemption fees) are determined endogenously in a competitive market setting. The heterogeneity in managerial skills, the wide variety of the hedge fund strategies and the existence of investors with heterogeneous liquidity needs, give rise to a variety of hedge fund structures that differ in the expected returns delivered to investors and in the statistical properties of returns. Hedge funds try to dissuade redemptions by imposing restrictions on withdrawals through the terms of contracts (redemption fees, initial lock-up period, minimum investment, notice period.) These restrictions would allow managers to work on more long term horizons and to invest in more illiquid assets (or illiquid strategies) which command higher returns. Offering higher profiles of return-risk than other investment vehicles (when considered in the mean-variance paradigm) hedge funds also exhibit significant skewness and kurtosis. We explore in this paper, among other empirical implications of the model, the fact that this deteriorated skewness is caused by investing in illiquid assets. This would be consistent with the findings that liquid stocks tend to have superior performance in illiquid times, and that illiquid assets perform worse during liquidity crisis, and are more likely to be subject to liquidity shocks.

Replication and Benchmarking of Hedge Funds

The growth in the hedge fund industry during the last two decades has been accompanied by a general decline in the performance of hedge funds. Academic research on hedge funds has led to the development of a number of replication strategies that in some cases can explain a significant portion of returns generated by various hedge fund strategies. This article presents a replication methodology that is particularly suited for performance evaluation. The methodology develops two replicating portfolios such that the distribution properties of the target are matched. The first portfolio delivers the statistical properties at the lowest possible cost while the other is more expensive but delivers the same set of properties as the target. The portfolios are used to evaluate a set of managers reporting to the CISDM Database.

Does Hedge Fund Performance Persist? Overview and New Empirical Evidence

The contribution of this paper is to provide an overview and new empirical evidence on hedge fund performance persistence, which has been a controversial issue in the academic literature during the last several years. In the first step, we review recent studies and put them into a joint evaluation of hedge fund performance persistence. In the second step, the methodological framework developed in the overview is used to present new empirical evidence. We find different levels of performance persistence depending on the statistical methodology and the hedge fund strategy employed. In our study, performance persistence cannot be explained by the use of option-like strategies, but it can be partially explained by survivorship and backfilling bias. Differences among hedge fund strategies might be explained by return smoothing. Finally, we develop a rationale for choosing between different methodologies to measure performance persistence and conclude that the multi-period Kolmogorov-Smirnov test is the most useful for evaluating performance persistence of hedge funds.

Conditional Financial Models and the Alpha Puzzle

This article proposes new estimations of conditional versions of the Fama-French model in order to deal with the alpha puzzle that emerges from hedge fund studies. Its originality lies in resorting to a new form of GMM, the GMM-C, which uses the cumulants of the explanatory variables as instruments in doing the estimations of each hedge fund strategy in panel. The estimations reveal that the new estimator is robust and is preferable to the usual OLS estimation of conditional models, which does not account for specification errors as does the GMM-C. This article also shows that the success of a conditional model in solving the alpha puzzle is related to a judicious choice of the conditioning information.TOPICS: Real assets/alternative investments/private equity, factor-based models, statistical methods, performance measurement

Detecting Structural Breaks in Multivariate Financial Time Series: Evidence from Hedge Fund Investment Strategies

This paper extends the class of asset-based style factor models with multiple structural breaks to the multivariate setting. We propose a model that allows for the presence of common breaks in a system of factor models for individual hedge fund investment strategies, which share common investment characteristics. We develop a Bayesian approach to inference for the unknown number and positions of the structural breaks, based on a set of filtering recursions similar to those of the forward-backward algorithm. Furthermore, we identify relevant risk factors, common among the series of hedge funds, using a Bayesian model comparison approach. We apply our method to a set of correlated hedge fund strategies, which are mainly characterized by equity related bets. Multiple common breaks are identified, consistent with well-known market events, which reveal evidence for structural changes in the risk exposures as well as in the correlation structure of the analyzed series.