Conditional Properties of Hedge Funds: Evidence from Daily Returns*

Using daily returns on a set of hedge fund indices, we study (i) the properties of the indices' conditional density functions and (ii) the presence of asymmetries in conditional correlations between hedge fund indices and other investments and between hedge fund indices themselves. We use the SNP approach to obtain estimates of conditional densities of hedge fund returns and then proceed to examine their properties. In general, a nonparametric GARCH(1,1) model appears to provide the best fit for all strategies. We find that the conditional third and fourth moments are significantly affected by changes in the current volatility of returns on hedge fund indices. We examine changes in the conditional probability of tail events and report significant changes in the probability of extreme events when the conditioning information changes. These results have important implications for models of hedge fund risk that rely on probability of tail events. We formally test for the presence of asymmetries in conditional correlations to determine if there is contagion between hedge funds and other investments and between various hedge fund indices in extreme down markets versus extreme up markets. We generally do not find strong evidence in support of asymmetric correlations.     

Quantile regression analysis of hedge fund strategies

Extending previous work on hedge fund pricing, this paper introduces the idea of modelling the conditional quantiles of hedge fund returns using a set of risk factors. Quantile regression analysis provides a way of understanding how the relationship between hedge fund returns and risk factors changes across the distribution of conditional returns. We propose a Bayesian approach to model comparison which provides posterior probabilities for different risk factor models that can be used for model averaging. The most relevant risk factors are identified for different quantiles and compared with those obtained for the conditional expectation model. We find differences in factor effects across quantiles of returns, which suggest that the standard conditional mean regression method may not be adequate for uncovering the risk-return characteristics of hedge funds. We explore potential economic impacts of our approach by analysing hedge fund single strategy return series and by constructing style portfolios.     

Higher risk,lower returns: What hedge fund investors really earn

The returns of hedge fund investors depend not only on the returns of the funds they hold but also on the timing and magnitude of their capital flows in and out of these funds. We use dollar-weighted returns (a form of Internal Rate of Return (IRR)) to assess the properties of actual investor returns on hedge funds and compare them to buy-and-hold fund returns. Our main finding is that annualized dollar-weighted returns are on the magnitude of 3% to 7% lower than corresponding buy-and-hold fund returns. Using factor models of risk and the estimated dollar-weighted performance gap, we find that the real alpha of hedge fund investors is close to zero. In absolute terms, dollar-weighted returns are reliably lower than the return on the Standard & Poor's (S&P) 500 index, and are only marginally higher than the risk-free rate as of the end of 2008. The combined impression from these results is that the return experience of hedge fund investors is much worse than previously thought.     

Speculative capital and currency carry trades

In this paper, we study a two-country general equilibrium model with partially segmented financial markets, where hedge funds emerge endogenously. Empirically, we show that the hedge fund investment strategy predicted by our model, which we call the “risk-adjusted carry trade” strategy, explains more than 16% of the overall hedge fund index returns and more than 33% of the fixed income arbitrage sub-index returns. The flow of new money to hedge funds affects market interest rates, exchange rates, and both the hedge funds’ contemporaneous and expected future returns as predicted by the model.     

Zur Risikoanalyse von Hedgefonds: Ein datenanalytischer Ansatz

Hedge fund returns have a number of specific features compared to traditional investments which result in problems when applying traditional methods of risk analysis (Markowitz portfolio selection theory, Sharpe Ratio, value at risk calculation based on normal returns). These problems have to be considered adequately by insurance companies when constructing internal risk models and performing risk management for hedge funds in their investment.The present paper has its focus on the departure of hedge fund returns from the normality hypothesis, especially with respect to the statistical quantities skewness and kurtosis (fat tail problem). A statistical analysis of hedge fund index returns gives evidence that the majority of hedge fund returns show substantial departures from normality. In addition, the analysis shows that hedge fund returns are adequately represented by the family of GH-distributions developed in exploratory data analysis. Following this result a risk analysis of hedge fund strategies is performed on the basis of the GH-value at risk.     

Risk measurement distortion: an improved model of return smoothing

We examine the effects of smoothed hedge fund returns on standard deviation, skewness, and kurtosis of return and on correlation of returns using a MA(2)-GARCH(1,1)-skewed-t representation instead of the traditional MA(2) model employed in the literature. We present evidence that our proposed representation is more consistent with the behavior of hedge fund returns than the traditional MA(2) representation and that the traditional method tends to overstate the degree of smoothing observed in hedge fund returns. We examine methods for correcting the distortive effects of smoothing using our representation.     

A unique view of hedge fund derivatives usage: Safeguard or speculation?

We study the common equity and equity option positions of hedge fund investment advisors over the 1999–2006 period. We find that hedge funds' stock positions predict future returns and that option positions predict both volatility and returns on the underlying stock. A quarterly tracking portfolio of stocks based on publicly observable hedge fund option holdings earns abnormal returns of 1.55% through the end of the quarter. Net of fees, hedge funds using options deliver higher benchmark-adjusted portfolio returns and lower risk than nonusers. The results suggest that hedge fund positions reflect significant timing and selectivity skill.     

Convertible Bond Arbitrage: Risk and Return

Abstract:  This paper specifies a simulated convertible bond arbitrage portfolio to characterise the risks in convertible bond arbitrage. For comparison the risk profile of convertible bond arbitrage hedge fund indices at both monthly and daily frequencies is also examined. Results indicate that convertible bond arbitrage is positively related to default and term structure risk factors. These risk factors are augmented with the simulated convertible bond arbitrage portfolio, mimicking a passive investment in convertible bond arbitrage, to assess the risk and return of individual hedge funds. We provide estimates of the performance of two hedge fund indices (an equally weighted and value weighted index) and a sample of convertible bond arbitrage hedge funds using a factor model methodology. Lagged and contemporaneous observations of the risk factors are specified, controlling for illiquidity in the securities held by funds. Our results cover two time periods. Initially we find evidence of abnormal risk adjusted returns in the individual hedge fund data and the equally weighted hedge fund index and no evidence of abnormal risk adjusted returns in the value weighted hedge fund index. When we examine performance during the credit crisis of 2007 and 2008 we find evidence of negative abnormal returns amongst individual hedge funds and the hedge fund indices.     

Does the choice of performance measure influence the evaluation of hedge funds?

The Sharpe ratio is adequate for evaluating investment funds when the returns of those funds are normally distributed and the investor intends to place all his risky assets into just one investment fund. Hedge fund returns differ significantly from a normal distribution. For this reason, other performance measures for hedge fund returns have been proposed in both the academic and practice-oriented literature. In conducting an empirical study based on return data of 2763 hedge funds, we compare the Sharpe ratio with 12 other performance measures. Despite significant deviations of hedge fund returns from a normal distribution, our comparison of the Sharpe ratio to the other performance measures results in virtually identical rank ordering across hedge funds.     

Not only skill but also scale: Evidence from the hedge funds industry

This paper empirically tests a two-levels model of decreasing returns to scale using a sample of hedge funds. The two-levels model assumes that a fund's gross alpha is a decreasing function of both the fund scale and the style scale measured by the aggregate size of peers in the hedge fund style. We find that a fund-level model underestimates the impact of diseconomies of scale on the gross alpha by 55 basis points. The results indicate that managers should consider the constraints imposed by the style scale when optimizing their portfolio sizes. We also provide evidence that hedge funds did not invest at their optimal amount and confirm that skill and the ability to resist decreasing returns to scale are two important components of selecting hedge fund performers.     

The role of derivatives in hedge fund activism

Using a hand-collected sample of hedge fund activist engagements from 1994 to 2014, this study analysed the role of derivatives in the hedge fund activism. Evidence shows abnormal returns of targets of hedge fund activists who did not use derivatives exceeded the abnormal returns of targets of hedge fund activists who employed derivatives around the activist engagement disclosure period. We also find that idiosyncratic volatility of the targets of hedge fund activists who did not use derivatives was more reduced than that of the targets of hedge fund activists who used derivatives. Finally, the probability of takeovers increases for hedge fund activists who did not use derivatives.     

Do Hot Hands Exist among Hedge Fund Managers? An Empirical Evaluation

In measuring performance persistence, we use hedge fund style benchmarks. This allows us to identify managers with valuable skills, and also to control for option-like features inherent in returns from hedge fund strategies. We take into account the possibility that reported asset values may be based on stale prices. We develop a statistical model that relates a hedge fund's performance to its decision to liquidate or close in order to infer the performance of a hedge fund that left the database. Although we find significant performance persistence among superior funds, we find little evidence of persistence among inferior funds.     

Hedge fund return predictability; To combine forecasts or combine information?

While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance.     

Macroeconomic risk and hedge fund returns

This paper estimates hedge fund and mutual fund exposure to newly proposed measures of macroeconomic risk that are interpreted as measures of economic uncertainty. We find that the resulting uncertainty betas explain a significant proportion of the cross-sectional dispersion in hedge fund returns. However, the same is not true for mutual funds, for which there is no significant relationship. After controlling for a large set of fund characteristics and risk factors, the positive relation between uncertainty betas and future hedge fund returns remains economically and statistically significant. Hence, we argue that macroeconomic risk is a powerful determinant of cross-sectional differences in hedge fund returns.     

Emerging Market Exposures and the Predictability of Hedge Fund Returns

We examine emerging market and global macro hedge funds and find a significant positive relation between hedge funds’ future returns and their exposure to both emerging market equities and emerging market currencies. We present evidence that the strong predictive power of emerging market betas is related to the superior market‐timing ability of these fund managers. Results are robust after controlling for commonly used hedge fund factors, the emerging market equity index, lagged fund returns, liquidity risk, and fund characteristics. Our results suggest that hedge funds can earn positive excess returns by timing their exposure to emerging market securities.     

Hedge fund allocation: Evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques

We propose a model for constructing Asian funds of hedge funds. We compare the accuracy of forecasts of hedge fund returns using an ordinary least squares (OLS) regression model, a nonparametric regression model, and a nonlinear nonparametric model. We backtest to assess these forecasts using three different portfolio construction processes: an “optimized” portfolio, an equally-weighted portfolio, and the Kelly criterion-based portfolio. We find that the Kelly criterion is a reasonable method for constructing a fund of hedge funds, producing better results than a basic optimization or an equally-weighted portfolio construction method. Our backtests also indicate that the nonparametric forecasts and the OLS forecasts produce similar performance at the hedge fund index level. At the individual fund level, our analysis indicates that the OLS forecasts produce higher directional accuracy than the nonparametric methods but the nonparametric methods produce more accurate forecasts than OLS. In backtests, the highest information ratio to predict hedge fund returns is obtained from a combination of the OLS regression with the Fung–Hsieh eight-factor variables as predictors using the Kelly criterion portfolio construction method. Similarly, the highest information ratio using forecasts generated from a combination of the nonparametric regression using the Fung–Hsieh eight-factor model variables is achieved using the Kelly criterion portfolio construction method. Simulations using risk-adjusted total returns indicate that the nonparametric regression model generates superior information ratios than the analogous backtest results using the OLS. However, the benefits of diversification plateau with portfolios of more than 20 hedge funds. These results generally hold with portfolio implementation lags up to 12 months.     

HEDGE FUNDS: WHAT DO WE KNOW?

Hedge funds are attracting increased attention because of their reputation for earning superior (risk-adjusted) returns. Hedge Fund Research Inc. estimates that in 2001 there were about 7,000 hedge funds with investor capital of about $600 billion. And yet the diversity of hedge funds, combined with a general lack of transparency, makes the hedge fund industry something of a "black box."
This article provides an overview of the legal structure of hedge funds, the various fund investment strategies, and the existing research on overall hedge fund performance. Without uniform and comprehensive reporting requirements, it is difficult to ascertain the size and scope of hedge fund investments. Nonetheless, current research provides persuasive evidence that hedge funds earn positive risk-adjusted returns, on average, in contrast to their counterparts in the mutual fund industry. In an attempt to explain these higher returns, the authors begin by noting that hedge funds are subject to considerably less regulation than other investment institutions because their client base is limited to wealthy individuals and institutions. Hedge funds can thus employ investment strategies that mutual funds and pension funds are prohibited from pursuing, such as short selling, high leverage, derivatives, concentrated holdings, and limited redemptions. As a result, the funds may be able to earn excess returns by operating in illiquid and specialized markets where there is a shortage of arbitrage capital. At the same time, and perhaps even more important, hedge funds are in a better position than conventional mutual funds to attract skilled managers because of their use of performance-based incentive fee structures.     

Systematic risk and the cross section of hedge fund returns

This paper investigates the extent to which market risk, residual risk, and tail risk explain the cross-sectional dispersion in hedge fund returns. The paper introduces a comprehensive measure of systematic risk (SR) for individual hedge funds by breaking up total risk into systematic and fund-specific or residual risk components. Contrary to the popular understanding that hedge funds are market neutral, we find that systematic risk is a highly significant factor explaining the dispersion of cross-sectional returns while at the same time measures of residual risk and tail risk seem to have little explanatory power. Funds in the highest SR quintile generate 6% more average annual returns compared with funds in the lowest SR quintile. After controlling for a large set of fund characteristics and risk factors, systematic risk remains positive and highly significant, whereas the relation between residual risk and future fund returns continues to be insignificant. Hence, systematic risk is a powerful determinant of the cross-sectional differences in hedge fund returns.     

The risk in hedge fund strategies: theory and evidence from trend followers

Hedge fund strategies typically generate option-like returns.Linear-factor models using benchmark asset indices have difficultyexplaining them. Following the suggestions in Glosten and Jagannathan(1994), this article shows how to model hedge fund returns byfocusing on the popular 'trend-following' strategy. We use lookbackstraddles to model trend-following strategies, and show thatthey can explain trend-following funds' returns better thanstandard asset indices. Though standard straddles lead to similarempirical results, lookback straddles are theoretically closerto the concept of trend following. Our model should be usefulin the design of performance benchmarks for trend-followingfunds.     

Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

This paper analyzes the contribution of hedge funds to optimal asset allocations between 1993 and 2010. The preferences of specific institutional investors are captured by implementing a Bayesian asset allocation framework that incorporates heterogeneous expectations regarding hedge fund alpha. Mean-variance spanning tests are used to infer the ability of hedge funds to significantly enhance the mean-variance efficient frontier. Further, a novel democratic variance decomposition procedure sheds light on the dynamics in the co-movement of hedge fund returns with a set of common benchmark assets. The empirical findings indicate that portfolio benefits of hedge funds are time-varying and strongly depend on investor optimism regarding hedge funds’ ability to generate alpha. In general, allocations to hedge funds improve the global minimum variance portfolio even after controlling for short-selling restrictions and minimum diversification constraints. However, due to dynamics underlying the composition of the aggregate hedge fund universe, the factor structure of hedge fund returns has become more similar to the benchmark assets over time.