Comparing Hedge Funds: Highest Return Strategy

Tail risk of hedge funds: an extreme value application

The data dependency of empirical financial research is of common concern to both academics and practitioners. This is especially true for hedge funds because no single, commonly accepted database exists and because many of the databases may hold different sets of reporting managers. Each database uses current reporting managers as the basis for the construction of hedge fund indices, and these index returns reflect the characteristics of the funds reporting to the relevant database. Unlike historical returns derived from current databases, however, historical returns from most major hedge fund indices do not contain backfill or survivor bias. At the same time, performance characteristics may differ between indices because each index is constructed based on a different set of rules (e.g., equal weighted, asset weighted, and so on). In this article, the authors conduct a series of empirical tests similar to those previously conducted in academic studies. The authors use only those hedge fund indices that reflect the average returns of the entire set of reporting managers; that is, the indices representing overall industry returns. Results indicate that return-based style analyses, often used as a basis for hedge fund analysis, are impacted both by the period of analysis as well as the hedge fund index used. Moreover, results indicate that the addition of variables beyond those designed to capture underlying equity, interest rate, and credit risk have little impact on the explanatory power of these hedge fund universe indices beyond a very low level of statistical significance.

Hedge fund asset allocation can be a challenging endeavor given the dearth of tools available to deal with the unique statistical characteristics of long and short strategies. From a top-down perspective, the hedge fund industry is classified into several substyle categories including long/short equity, market neutral equity, convertible bond arbitrage, merger arbitrage, event driven, global macro, and managed futures. However, due to the non-correlated nature of rates of return in each style group, the problem of asset allocation appears overly simplistic. This article takes a different view of the hedge fund universe, classifying strategies as “convergent” or “divergent” in their orientation and thereby adding new meaning to the process of asset allocation. Convergent strategies tend to view the asset world as being mostly efficient, seeking to profit from small asset mispricings. Divergent strategies are based on the premise that from time to time, the market is inefficient, providing opportunities that can be exploited by using price series analysis and autocorrelations when pricing certain portfolio assets. Since convergent strategies tend to be “short volatility” and divergent strategies “long volatility,” using a top-down asset allocation policy that recognizes this asset dynamic can lead to a more efficiently allocated hedge fund portfolio. The results of this study show the time-varying validity of the divergent strategy and its potential benefits as a portfolio component. Since the divergent strategy experiences significantly higher performance during the periods of increasing market uncertainty, when it is combined with the convergent strategy, the portfolio experiences increased return and reduced risk with more favorable return distributions relative to the individual convergent strategies.

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.

<p>The purpose of this study is to identify the pre- and post-war impact of the Russia-Ukraine war on the interaction between cryptocurrencies, cryptocurrency hedge funds, and traditional hedge funds. This study provides a deeper understanding of how geopolitical events can affect the behavior of financial markets involving cryptocurrencies and hedge funds. In addition, this study also seeks to fill the knowledge gap that exists in the current literature, specifically with regards to hedge fund strategies during specific geopolitical conflicts. This study utilizes secondary data involving the cryptocurrency hedge fund index, global hedge fund index, and eight proposed hedge fund strategies. The study period runs from February 2018 to July 2023. Granger Causality Test and ARDL used in this study. The finding shows that there is a significant relationship between cryptocurrency hedge funds and conventional hedge funds. Statistical analysis revealed cointegration between cryptocurrency hedge funds and conventional hedge funds, indicating a significant long-term relationship. This study identified a significant impact of changes in market behavior before and after the Russia-Ukraine war on cryptocurrency hedge funds.</p>

Hedge Funds Versus Hedged Mutual Funds: An Examination of Long/Short Funds; A Performance Update

Prior literature documents that the Sharpe ratio (SR) generates biases in performance evaluation if returns distribution deviates from normal distribution because SR is derived under the mean-variance model with the strict assumption of either quadratic preferences or customarily distributed returns. When the return distributions deviate from normality, it may lead to unreasonable results. Therefore, this study examines which performance measurement approaches are efficient for non-normality on the distribution of asset returns. We collect monthly returns of 14 Credit Suisse (CS) hedges fund indexes from April 1994 to June 2021. The hedge fund index returns exhibit high negative skewness or high positive kurtosis, implying non-normal distribution. Then, we employ the Sharpe ratio (SR) and two performance measures, which extend the Sharpe ratio, the generalized Sharpe ratio (GSR), and the economic performance measure (EPM), to evaluate the performances of hedge funds. In addition, both the nonparametric and parametric estimation methods of the GSR and the EPM are utilized. Our findings indicate that the nonparametric GSR and the nonparametric EPM produce more similar rankings than the SR. Among the three parametric estimation methods of the GSR and EPM, only the method proposed by [1] produces similar rankings with the nonparametric GSR and the nonparametric EPM. Finally, our study contributes the practical approach for fund managers to evaluate their fund performance efficiently.

In this article we test the ability of the stochastic differential model proposed by Fatone et al. [Maximum likelihood estimation of the parameters of a system of stochastic differential equations that models the returns of the index of some classes of hedge funds, J. Inv. Ill-Posed Probl. 15 (2007), pp. 329–362] of forecasting the returns of a long-short equity hedge fund index and of a market index, that is, of the Hedge Fund Research performance Index (HFRI)-Equity index and of the S&P 500 (Standard & Poor 500 New York Stock Exchange) index, respectively. The model is based on the assumptions that the value of the variation of the log-return of the hedge fund index (HFRI-Equity) is proportional up to an additive stochastic error to the value of the variation of the log-return of a market index (S&P 500) and that the log-return of the market index can be satisfactorily modelled using the Heston stochastic volatility model. The model consists of a system of three stochastic differential equations, two of them are the Heston stochastic volatility model and the third one is the equation that models the behaviour of the hedge fund index and its relation with the market index. The model is calibrated on observed data using a method based on filtering and maximum likelihood proposed by Mariani et al. [Maximum likelihood estimation of the Heston stochastic volatility model using asset and option prices: An application of nonlinear filtering theory, Opt. Lett., 2 (2008), pp. 177–222] and further developed in Fatone et al. [Maximum likelihood estimation of the parameters of a system of stochastic differential equations that models the returns of the index of some classes of hedge funds, J. Inv. Ill-Posed Probl. 15 (2007), pp. 329–362; The calibration of the Heston stochastic volatility model using filtering and maximum likelihood methodsin Proceedings of Dynamic Systems and Applications, Vol. 5, G.S. Ladde, N.G. Medhin, C. Peng, and M. Sambandham, eds., Dynamic Publishers, Atlanta, USA, 2008, pp. 170–181]. That is, an inverse problem for the stochastic dynamical system representing the model is solved using the calibration procedure. The data analysed is from January 1990 to June 2007, and are monthly data. For each observation time, they consist of the value at the observation time of the log-returns of the HFRI-Equity and of the S&P 500 indices. The calibration procedure uses appropriate subsets of data, that is the data observed in a 6 months time period. The 6 months data time period used in the calibration is rolled through the time series generating a sequence of calibration problems. The values of the HFRI-Equity and S&P 500 indices forecasted using the calibrated models are compared to the values of the indices observed. The result of the comparison is very satisfactory. The website http://www.econ.univpm.it/recchioni/finance/w8 contains some auxiliary material including some animations that helps the understanding of this article. A more general reference to the work of some of the authors and of their coauthors in mathematical finance is the website: http://www.econ.univpm.it/recchioni/finance.

Diversification in the hedge fund industry

Hedge funds increasingly are becoming a focus of investors and U.S. regulatory agencies including the U.S. Securities and Exchange Commission (the “SEC”), the U.S. Treasury Department, the National Association of Securities Dealers, and the Commodity Futures Trading Commission as enhanced regulatory attention is forcing the hedge fund industry for the first time since the near‐collapse of Long‐Term Capital Management, L.P. in late 1998 to examine itself more closely. In light of the dismal performance of U.S. stock market indices over the past several years, investors are turning to “absolute” return vehicles, such as hedge funds, that are able to generate current positive returns in a market that has returned consistently laggard results. Investors’ ability to invest in hedge funds has been further enhanced by the increase of hedge funds‐of‐funds registered under the U.S. Investment Company Act of 1940, as amended (the “1940 Act”). Simultaneously, over the past 18 months, the SEC has become increasingly concerned with a multitude of industry occurrences including (i) the widening access of investors to hedge funds, (ii) the unregulated nature of hedge funds, potentially leading to fraud or conflicts of interest with their investors, and (iii) the impact of hedge funds on the markets, especially in relation to their use of short selling. As U.S. regulatory attention remains focused on the hedge fund industry and industry practice and the themes of potential new regulations become apparent, it has become a cautionary period for hedge fund sponsors and investment managers.

Hedge Fund Performance 1990-2000: Do the Money Machines Really Add Value?

The objective of this study is to examine whether the available data on individual hedge funds (HFs) and funds-of-hedge funds (FOHFs) can reveal the risk-return trade-off and, if so, to find an appropriate risk measure that captures the cross-sectional variation in HF and FOHF returns and compare the risk-return relationship in HFs and FOHFs. Using the “live funds” and the “dead funds” datasets provided by Hedge Fund Research Inc. (HFR), we concentrate on alternative risk measures such as semi-deviation, VaR, expected shortfall and tail risk and compare them with standard deviation in terms of their ability to describe the cross-sectional variation in expected returns of HFs and FOHFs. Firstly, the risk measures are analysed at the portfolio level of HFs and FOHFs by adopting the Fama and French (1992) approach. Secondly, the various estimated risk measures are compared at the individual HF and FOHF levels by using univariate and multivariate cross-sectional regressions. The results show that the available data on HFs and FOHFs exhibits different risk-return trade-offs. The Cornish-Fisher expected shortfall or Cornish-Fisher tail risk could be an appropriate risk measure for HF return. Although appropriate alternative risk measures for the HFs are found, it is difficult to determine the risk measures that best capture the cross-sectional variation in FOHF returns.

This chapter focuses on new trends in the hedge fund industry. The chapter begins by creating some historical context for the current perception and state of hedge funds. The remainder of this chapter focuses on the following trends and their potential impact on the industry: (1) growth in all areas of the industry, especially in terms of long-term capital flows from institutional investors; (2) uncertainty about growth in the short term; (3) ways hedge funds approach growth; (4) the need for more diversity among hedge fund managers, including more minorities and women; (5) diverging long-term objectives for larger and smaller hedge funds; (6) future cost savings to investors; (7) development of new investment options to address liquidity concerns for investors; (8) new regulations; and (9) the future role of technology in the hedge fund industry.

This article analyzes the risk characteristics for various hedge fund strategies specializing in fixed income instruments. Because some fixed income hedge fund strategies have exceptionally high autocorrelations in reported returns and this is taken as evidence of return smoothing, we first develop a method to completely eliminate any order of serial correlation across a wide array of time series processes. Once this is complete, we determine the underlying risk factors to the adjusted hedge fund returns and examine the incremental benefit attained from using nonlinear payoffs relative to the more traditional linear factors. The hedge fund indices have a very strong exposure to high-yield credit. In general, we find a marginal benefit to using the nonlinear risk factors in terms of the ability to explain reported returns. Finally, we examine the benefit of using various factor structures for estimating the value-at-risk of the hedge funds. We find that for some of the hedge fund strategies, downside risk estimates can be quite substantial. <b>TOPICS:</b>Factor-based models, analysis of individual factors/risk premia

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.