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Risk related to return higher moments is usually perceived as substantial in the hedge fund industry. In this article, we relate the return co-skewness and co-kurtosis of hedge fund strategies to macroeconomic and financial shocks. We find that hedge funds’ return higher moments are quite sensitive to these shocks, and especially during the subprime crisis. Our robust IV-GMM estimation procedure reveals that output shocks are more important for explaining the co-kurtosis of most hedge fund strategies than shocks related to jumps in the VIX, while we find the opposite for co-skewness. This suggests that hedge funds better monitor their co-kurtosis. Our VAR experiments show that the bulk of the reaction of higher moment risk to shocks occurs during recessions. Furthermore, some directional strategies—like short-sellers and futures—successfully track shocks and deliver higher returns during periods of crisis, benefiting from market volatility analogously to lookback straddles. Our results also suggest that illiquidity factors are subject to endogeneity and may be viewed as indicators of return smoothing, especially during crises. Robustness checks show that the practice of return smoothing may lead to a serious underestimation of hedge fund higher moment systematic risk, and particularly fat-tail risk.

We study how downside risk taken by hedge fund strategies responds to macroeconomic and financial shocks. Using new empirical measures of systematic tail risk, we find that the impulse response functions of strategies’ multi-moment risk display an asymmetric behavior, being much more significant and nonlinear during the subprime crisis than during the recent expansion period. Our results indicate that managers of hedge fund strategies seek to monitor their tail risk during crises, in the sense that their portfolios seem to behave as puts, which are kurtosis reducers in times of turmoil. However, while some strategies (e.g., futures) succeed quite well in controlling their tail risk during crises, others (e.g., equity market neutral) have difficulties to do so. This is an important result for institutional investors who think that hedge funds are “hedged” and thus immunized against adverse macroeconomic shocks—especially market volatility shocks.

We investigate how macroeconomic and financial uncertainty impacts the behavior of hedge fund strategy higher moments—i.e., co-skewness and co-kurtosis—and their respective cross-sectional dispersions. Consistent with theoretical models, we find that strategy managers trade off these two higher moments when building optimal portfolios. Moreover, these trade-offs depend on the kind of strategy. Our experiments show that the VIX and its conditional variance are the most important factors affecting higher moment risk in the hedge fund industry. They also reveal that the behavior of hedge fund strategies is very asymmetric depending on the phase of the business cycle. In contrast to studies which rely on the mean-variance setting, we find that systemic risk—as measured by the cross-sectional dispersions of higher moments—tends to decrease in the low regime. The indicators of market volatility play a decisive role to explain this decline in systemic risk.

We investigate the new Fama-French (FF, 2015, 2016) five factors augmented with a well-known illiquidity measure (Pastor and Stambaugh, 2003), using an innovative GMM robust instrumental variables estimator casted in a panel data framework. When using OLS, the augmented FF model seems to have explanatory power regarding the FF 12-sector returns. However, our panel data framework suggests that the only consistently significant factor is the market risk factor. Nevertheless, depending on the technique we use, we find that measurement errors may be the cause of this result, thus providing some empirical evidence in support of the new FF five-factor approach. As robustness checks, we also experiment with other liquidity measures – like the Amihud (2002) ratio and the term-spread – and bond-oriented factors. Across our 12 portfolios, the results are largely unchanged. We also apply our extended model to managed portfolios – i.e., hedge fund portfolios. The returns of hedge fund strategies seem more responsive to the augmented FF five-factor model that includes illiquidity measures, especially when accounting for the subprime crisis. There is also evidence that the new FF factors embed illiquidity.

We study the asymmetric responses of hedge fund return moments—especially higher moments as measured by return co-skewness and co-kurtosis—to macroeconomic and financial shocks depending on the phase of the business cycle. Similarly to previous papers on hedge fund systematic market risk (beta), we find that hedge funds seem to monitor their return co-skewness and co-kurtosis. The response of their return moments to VIX shocks—our indicator of macroeconomic and financial uncertainty—is particularly important, hedge funds reducing their beta and co-kurtosis and increasing their co-skewness following a (positive) VIX shock. Overall, the representative hedge fund tends to behave as an insurance seller in economic expansion and as an insurance buyer in recession or crisis. Finally, VIX shocks contribute to increase systemic risk in the hedge fund industry.

Purpose The purpose of this paper is to test the new Fama and French (2015) five-factor model relying on a thorough sample of hedge fund strategies drawn from the Barclay’s Global hedge fund database. Design/methodology/approach The authors use a stepwise regression to identify the factors of the q-factor model which are relevant for the hedge fund strategy analysis. Doing so, the authors account for the Fung and Hsieh seven factors which prove very useful in the explanation of the hedge fund strategies. The authors introduce interaction terms to depict any interaction of the traditional Fama and French factors with the factors associated with the q-factor model. The authors also examine the dynamic dimensions of the risk-taking behavior of hedge funds using a BEKK procedure and the Kalman filter algorithm. Findings The results show that hedge funds seem to prefer stocks of firms with a high investment-to-assets ratio (low conservative minus aggressive (CMA)), on the one hand, and weak firms’ stocks (low robust minus weak (RMW)), on the other hand. This combination is not associated with the conventional properties of growth stocks – i.e., low high minus low (HML) stocks – which are related to firms which invest more (low CMA) and which are more profitable (high RMW). Finally, small minus big (SMB) interacts more with RMW while HML is more correlated with CMA. The conditional correlations between SMB and CMA, on the one hand, and HML and RMW, on the other hand, are less tight and may change sign over time. Originality/value To the best of the authors’ knowledge, the authors are the first to cast the new Fama and French five-factor model in a hedge fund setting which account for the Fung and Hsieh option-like trading strategies. This approach allows the authors to better understand hedge fund strategies because q-factors are useful to study the dynamic behavior of hedge funds.

We test the new Fama and French five-factor model on a sample of hedge fund strategies. This model embeds the q-factor asset pricing model which lies on the CMA and RMW factors. We find that the HML factor is not redundant for many strategies, as conjectured by Fama and French in their setting. HML seems to embed risk dimensions which are not included in the Fama and French new factors. In contrast to Fama and French, the α puzzle is robust to the addition of CMA and RMW. Furthermore, hedge funds seem to prefer, on the one hand, firms which invest a lot to firms which invest less, and, on the other hand, weak firms over robust ones. Finally, our results are not sensitive to the addition of the Fung and Hsieh seven-factor model. However, the explanatory power of the eleven-factor model is much higher for some hedge fund strategies involved in arbitrage.

We investigate how hedge funds’ strategies react, as a group, to macroeconomic risk and uncertainty. Adopting the methodology of Beaudry et al. (2001), we track the behavior of the cross-sectional dispersions of hedge fund strategies’ returns, market betas and alphas over the business cycle. The pattern of strategies’ betas supports Beaudry et al.’s conjecture: hedge funds reduce their risk-taking (betas) during times of macroeconomic uncertainty, which makes their strategies more homogeneous and thus contributes to increased systemic risk in the financial system. However, the cyclical behavior of the cross-sectional dispersions of strategies’ returns and strategies’ alphas is not in line with Beaudry et al.’s conjecture. These dispersions tend to increase during episodes of rising macroeconomic uncertainty, which suggests the prevalence of the Black’s (1976) leverage effect during financial turmoil and the fact that the exposure of hedge fund strategies to risk factors is quite different from each other. Finally, although remaining important, procyclicality seems to have declined through time in the hedge fund industry, which suggests that a learning process is at play.

Using an updated database that extends after the subprime crisis, we revisit the asymmetries of hedge fund behavior in recession compared with economic expansion. In this respect, we study the time-varying α' sa ndβ's associated with strategy returns using an inno- vative framework based on the Kalman filter and the multivariate GARCH. We find that hedge fund managers reduce drastically their risk exposure during financial crises while their behavior is much smoother in normal times. We also find that hedge funds continue to provide good prospects for investors in terms of risk-adjusted returns. Actually, the procyclicality of hedge fund strategies' returns seems to decrease through time. Moreover, the strategies' behavior in terms of α and β tends to become more heterogeneous in times of crisis. The strategy exposure to adverse shocks seems to recede even after accounting for the subprime crisis. Finally, many hedge fund strategies benefit from an increase in the volatility of stock market returns. Hedge fund strategies may thus constitute a way to offset the lower expected returns observed in the conventional financial markets and may contribute to portfolio diversification.

We revisit the factors incorporated in asset pricing models following the recent developments in financial markets – i.e., the rise of shadow banking and the change in the transmission channel of monetary policy. We propose two versions of the Fung and Hsieh (2004) hedge fund return model, especially an augmented market model which accounts for the new dynamics of financial markets and the procyclicality of hedge fund returns. We run these models with an innovative Hausman procedure, tackling the measurement errors embedded in the models factor loadings. Our empirical method also allows for confronting the drawbacks of the instruments used to estimate hedge fund asset pricing models.

Traditional financial institutions as banks follow procyclical risk strategies, that is, they increase their leverage in economic expansions and reduce it in contractions, which leads to a procyclical behaviour for their betas and other risk and financial performance measures. We study the cyclical aspects of hedge fund strategies, an issue quite overlooked in the literature. We rely on two procedures: conditional modelling and Kalman filtering of hedge funds’ alpha and beta. We find that hedge fund betas are usually procyclical. Our results also show that the alpha is often high at the beginning of a market upside cycle but, as the demand pressure increases, it progressively shrinks, which suggests that the alpha puzzle ought to be analysed in a dynamic setting.

In this article, we propose a new empirical version of the Fama and French (F&F) model based on a new version of the Hausman (1978) specification test. This empirical model incorporates correction factors for risk exposure. These factors take into account the problem of errors-in-variables. Our model also features another innovation. It uses higher moments of the four factors of the augmented F&F model as instruments to tackle the problem of errors-in-variables. This new method reflects the nonlinear character of risk. We highlight the link between our new version of the Hausman artificial regression and the standard two-stage least-squares method. Our results suggest that it is preferable to account for errors-in-variables and more generally specification errors when estimating the F&F model because it might cause serious biases of important parameters like alpha and beta.

The findings presented in this article improve the existing methods for estimating financial models of returns and especially for estimating the parameters that are relevant for portfolio managers, such as the Jensen alpha, a popular measure for stock selection, and beta, a well-known systemic risk measure. The authors focus on the presence of measurement errors in these models. For instance, the risk factors in the Fama and French models or the market model are biased by measurement, or specification, errors. These measurement errors are related to the use of proxies for measuring risk factors, such as the risk premium, and other risk factors, which are approximated by mimicking portfolios. To tackle these specification problems, the authors propose new Hausman-based estimators lying on cumulants optimal instruments. Using these newly generated strong instruments in a generalized method of moments (GMM) setting, the authors obtain new GMM estimators that they call GMM-C and its homologue GMM-hm. They also extend the methodology to the standard two-stage least squares (TSLS) framework using new optimally generated instruments. These procedures improve the existing method of moments for estimating, or calibrating, the parameters of a financial model and, more generally, for treating the problem of endogeneity often encountered in financial studies. Moreover, this study leads to a new indicator that signals the presence of specification errors in financial models. The authors apply a battery of tests and estimators to a sample of 22 HFR hedge fund indices observed monthly over the period 1990–2005. Their tests reveal that specification errors bias parameter estimation of financial models of returns and that the ranking of hedge funds is very sensitive to the choice of estimators.

Estimation of financial models of hedge fund returns often gives rise to abnormally high alphas. This phenomenon may be due to mis-specified models, but recently the introduction of volatility factors in hedge fund returns models (Kuenzi and Xu, 2007) has been viewed as a solution to solve the alpha puzzle. This paper shows that modelling the volatility of the innovation term of hedge fund return models might be another way to explain the alpha puzzle. The model proposed in this paper is a factor model that incorporates an 'alternative' factor, which is the return of a short put on the Standard & Poor's 500 whose volatility is the VIX. This paper takes an overall view to analyse the problem of specification errors in financial models. To account for specification errors, it proposes a new estimator based on the generalised method of moments (GMM) whose instruments are the higher moments of returns, the GMM-hm. Some transformations of the basic factor model, which are recommended in the financial literature to improve the estimation of the alpha, are considered the n-CAPM and the conditional model. Some GARCH and EGARCH specifications of our basic model of returns are also estimated. Our results show that modelling the volatility of the innovation is the best way to lower the alpha. The n-CAPM has also had some success in reducing the alpha. The alpha is not the only garbage bag of a returns model (Jaeger and Wagner, 2005), but the alpha and the innovation both constitute the garbage bag.

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.

This paper proposes new Hausman-based estimators lying on higher moments and cumulants. Our study gives way to a new indicator signalling the presence of specification errors in financial models. We apply our battery of tests to a sample of 21 HFR hedge funds strategies over the period 1990–2005. Our tests reveal that it is much more preferable to account for specification errors when estimating financial models of returns. Actually, the performance ranking of hedge funds strategies may change significantly when accounting for specification errors.

The authors consider the dynamic market strategies of hedgefunds by using the Kalman filter. There are many studies onthe behavior of conditional alphas and betas of hedge funds,but the dynamics of these coefficients is studied within thetraditional regression framework: The resulting conditionalalphas and betas are hence for a great part arbitrary becausethey do not result from a dynamic optimization process. Inthis article, the authors try to correct this problem in partby writing transition equations for the alpha and the betawhose explanatory variables are market financial variables.The alphas of hedge fund indices appear quite difficult tocontrol, a result in line with the market efficiency hypoth-esis. Besides, the betas are much more controlable, their reac-tion to market variables being significant.

There is yet an unsolved beta puzzle in the financial literature. While studying the returns of mutual funds, Ferson and Schadt1 observed that these funds had a tendency to increase their conditional beta when the market risk premium decreases. According to them, the behaviour of the typical mutual fund is perverse and that gives way to the beta puzzle. Ferson and Schadt tried to explain this puzzle by the negative correlation between changes in conditional betas and change in net new money flows. Yet, cash holdings are positively correlated with net new money flows. Thus, when cash holdings are high, beta is low, a normal relation. Here, there is an endo-geneity econometric problem that must be dealt with a robust instrumental variable (IV) estimator.

This article uses a new set of instruments based on higher statistical moments to discard the specification errors that might be present in the Fama and French model. It shows that the usual instruments perform quite poorly in comparison to higher moments. It estimates the Fama and French model on a sample of 22 HFR hedge funds indices and 111 HFR individual hedge funds over the period 1990-2005. To do so, it compares many instrumental variables methods as the two-stage least squares and the generalized method of moments. The results show that there are few problems of specification errors on the side of the indices but this level of aggregation hides the errors. Indeed, the estimations of the sample of 111 funds reveal specification errors for the loadings of the market premium and the factor SMB which seem understated. The message to retain is that the individual investor must consider specification errors when modelling the returns of hedge funds as he does not buy the indices but the stocks of individual hedge funds. This article shows an investor how to correct the traditional measures of performance, which are alphas and factor loadings, to guide him towards a better decision process.

Many studies demonstrate that the high level of hedge fund alpha is due to specification errors in the return models used to analyze their performance. Using a new estimator based on Generalized Method of Moments relying on higher moments of returns as instruments, we find that this method might help solve the puzzle. Modeling returns volatility also reduces the bias related to alpha. Surprisingly, Asian hedge funds display similar behavior as global hedge funds. Finally, the subprime crisis substantially impacted the risk-taking appetite of global and Asian hedge funds. In that respect, their exposures to the Fama and French risk factors greatly decreased.

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