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Abstract:
We use a comprehensive dataset of Funds-of-Hedge-Funds (FoFs) to investigate performance, risk and capital formation in the hedge fund industry over the past ten years. We confirm the finding of high systematic risk exposures in FoF returns. We divide up the past ten years into three distinct subperiods and demonstrate that the average FoF has only delivered alpha in the short second period from October 1998 to March 2000. In the cross section of FoFs, however, we are able to identify FoFs capable of delivering persistent alpha. We find that these more successful hedge funds experience far greater (and steadier) capital inflows than their less fortunate counterparts. Berk and Green's (2004) rational model of active portfolio management implies that diminishing returns to scale combined with the inflow of new capital leads to the erosion of superior performance over time. In keeping with this implication, we provide evidence that even successful hedge funds have experienced a recent, dramatic decline in risk-adjusted performance.

Hedge funds, performance, alpha, factor models, flow, funds-of-hedge funds

Abstract:
We use a comprehensive dataset of funds-of-funds to investigate performance, risk and capital formation in the hedge fund industry over the decade from 1995-2004. We first confirm that there are high systematic risk exposures in the returns of funds-of-funds in our data. We then divide up the ten years into three distinct sub-periods and demonstrate that the average fund-of-funds has only delivered alpha in the short second period from October 1998 to March 2000. In the cross-section, however, we are able to identify funds-of-funds capable of delivering alpha. We find that these alpha producing funds-of-funds experience far greater and steadier capital inflows than their less fortunate counterparts. In turn, these capital inflows adversely affect their ability to produce alpha in the future. These findings strongly support Berk and Green's (2004) rational model of active portfolio management, in which diminishing returns to scale combined with the inflow of new capital into better performing funds leads to the erosion of superior performance over time.

hedge funds, funds-of-funds, performance, alpha, factor models, flows, capacity constraints

Abstract:
This paper discusses the information content and potential measurement biases in hedge fund benchmarks. Hedge fund indices built from databases of individual hedge funds will inherit their measurement biases. In addition, broad-based indices mask the diversity of individual hedge fund return characteristics. Consequently, these indices are less informative for investors seeking diversification from traditional asset classes through the use of hedge funds. This paper proposes a different approach to constructing hedge fund benchmarks. It is based on the simple idea that the most direct way of measuring hedge fund performance is to observe the investment experience of hedge fund investors themselves. In terms of measurement biases, returns of funds-of-hedge funds can deliver a cleaner estimate of the investment experience of hedge fund investors. In terms of risk characteristics, indices of funds-of-hedge funds is more indicative of the demand side dynamics driven by hedge fund investors' preferences. Therefore, indices of funds-of-hedge funds can provide additional valuable information to the assessment of the hedge fund industry's performance.

Hedge funds, benchmarks, performance, styles

Abstract:
This paper discusses the information content and potential measurement biases in hedge fund benchmarks. Hedge fund indices built from databases of individual hedge funds will inherit their measurement biases. In addition, broad-based indices mask the diversity of individual hedge fund return characteristics. Consequently, these indices are less informative for investors seeking diversification from traditional asset classes through the use of hedge funds. This paper proposes a different approach to constructing hedge fund benchmarks. It is based on the simple idea that the most direct way of measuring hedge fund performance is to observe the investment experience of hedge fund investors themselves. In terms of measurement biases, returns of funds-of-hedge funds can deliver a cleaner estimate of the investment experience of hedge fund investors. In terms of risk characteristics, indices of funds-of-hedge funds is more indicative of the demand side dynamics driven by hedge fund investors' preferences. Therefore, indices of funds-of-hedge funds can provide additional valuable information to the assessment of the hedge fund industry's performance.

Hedge funds, benchmarks, performance, styles

Abstract:
Asset-based style factors link returns of hedge-fund strategies to observed market prices. They provide explicit and unambiguous descriptions of hedge-fund strategies that tells us both the nature as well as the quantity of risk. Asset-based style factors are key inputs to portfolio construction and for benchmarking hedge-fund performance on a risk-adjusted basis. The model in Fung and Hsieh (2001a) and Mitchell Pulvino (2001) can be used to construct asset-based style factors. In is shown that the model in Fung and Hsieh (2001a) correctly predicted the return behavior trend-following strategies during out-of-sample periods and particularly so during stressful market conditions like September 2001.

Hedge Fund, Style, Risk, Portfolio Diversification

Abstract:
Asset-based style factors link returns of hedge-fund strategies to observed market prices. They provide explicit and unambiguous descriptions of hedge-fund strategies that tells us both the nature as well as the quantity of risk. Asset-based style factors are key inputs to portfolio construction and for benchmarking hedge-fund performance on a risk-adjusted basis. The model in Fung and Hsieh (2001a) and Mitchell Pulvino (2001) can be used to construct asset-based style factors. In is shown that the model in Fung and Hsieh (2001a) correctly predicted the return behavior trend-following strategies during out-of-sample periods and particularly so during stressful market conditions like September 2001.

Hedge Fund, Style, Risk, Portfolio Diversification

Abstract:
Efficient Method of Moments (EMM) is used to fit the standard stochastic volatility model and various extensions to several daily financial time series. EMM matches to the score of a model determined by data analysis called the score generator. Discrepancies reveal characteristics of data that stochastic volatility models cannot approximate. The two score generators employed here are "Semiparametric ARCH" and "Nonlinear Nonparametric". With the first, the standard model is rejected, although some extensions are accepted. With the second, all versions are rejected. The extensions required for an adequate fit are so elaborate that nonparametric specifications are probably more convenient.

Abstract:
This paper presents the results of a post-sample simulation of a speculative strategy using a portfolio of foreign currency forward contracts.The main new features of the speculative strategy are (a)the use of Kalman filters to update the forecasting equation, (b) the allowance for transactions,costs and margin requirements and (c) the endogenous determination of the leveraging of the portfolio. While the forecasting model tended to overestimate profit and underestimate risk, the strategy was still profitable over a three year period and it was possible to reject the hypothesis that the sum of profits was zero. Furthermore, the currency portfolio was found to have an extremely low market risk. Combinations of the speculative currency portfolio with traditional portfolios of U.S. equities resulted in considerable improvements in risk-adjusted returns on capital.

Abstract:

Abstract:
No abstract is available for this paper.

Abstract:
This paper tests the hypothesis that traders have rational expeatations and charge no risk premium in the forward exchange market. It uses a statistical procedure which is consistent under a large class of heteroscedasticity, and a set of data which takes into account the institutional features of the forward exchange market. The results show that inferences using this procedure are very different from those using the standard assumption of homoscedasticity.

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Abstract:
Tending to be static and single-database oriented, existing models for correcting performance measurement biases are unable to detect potential data errors arising from (1) hedge funds that migrate from one database vendor to another and (2) merged databases. In general, return measurement biases can be traced to two key events: when a hedge fund elects to enter one or more databases (backfill bias) and when a hedge fund exits a database (survivorship bias). Artificial rules (e.g., ignoring the first x number of months of performance history to minimize backfill bias) and survivorship statistics based on a single database vendor are susceptible to another form of bias as databases evolve and consolidate. The authors posit that one must be mindful of how much of the hedge fund industry one is observing before passing judgment on the performance statistics of the hedge fund industry as a whole.

Performance Measurement and Evaluation, Performance Measurement, Portfolio Management, Hedge Fund Strategies, Alternative Investments, Hedge Fund Strategies

Abstract:
Hedge funds have grown substantially in the past few years. According to estimates by Tremont Capital Management (2006), the industry's assets under management increased from just over $200b in 2000 to over $800b by the end of 2005. Along with the rapid inflow of capital, hedge fund performance has declined. According to HFR, the average fund of hedge funds returned 10.5% per annum during 1996-2000, but only 5.8% during 2001-5. This development is consistent with the prediction of Berk and Green (2004) that unchecked inflow of funds will ultimately erode performance due to diminishing returns to scale. There is a sense of deja vu among hedge fund investors that many hedge fund managers are beginning to resemble active managers in the mutual fund industry of the past - failing to deliver returns commensurate to the fees and expenses they imposed on investors. History tells us that over-priced active managers will be replaced by low-cost passive index-liked alternatives. Could the same process be taking place in the hedge fund industry? Against this background, it is not surprising that investors are demanding more cost efficient hedge fund products. But, is existing technology capable of support the creation of rule-based, low-cost, passive hedge funds? The term "alternative beta" refers to the returns achievable from low-cost replication of rule-based trading strategies that capture return characteristics common across hedge funds, while "alternative alpha" refers to the returns that are not easily replicated. The introduction of this terminology was partly motivated by the need to stress that the search for hedge fund alpha properly begins with the identification of beta exposure to systematic risk factors which can go beyond conventional asset-class factors. This in turn points to the need for new technology if alternative beta factors are to be replicated successfully - a new tool kit is needed.

Hedge fund, rule-based trading strategy, synthetic hedge fund, passive index fund, trend follower, merger arbitrage strategy, alternative alpha, alternative beta

Abstract:
This paper shows empirically that Equity Long/Short hedge funds have significant alpha to both conventional as well as alternative (hedge fund-like) risk factors utilizing hedge fund data from three major data bases. Following the terminology introduced in Fung and Hsieh (2003a), we call these Equity alternative alphas (or Equity "AAs" for short). Equity AAs are extracted from Equity L/S hedge fund returns by first identifying the systematic risk factors inherent in their strategies. Hedging out these systematic risk factors, the resultant AA return series are empirically shown to be independent of systematic risks during normal as well as stressful conditions in asset markets. This provides collaborative evidence that AA returns are portable across conventional asset-class indices. By modeling the AA return series as GARCH(1,1)-AR(1) processes, it is shown that the unconditional return distributions are normal with time-varying variance free of serial correlations, skewness and kurtosis. Alpha-enhanced equity alternative are constructed admitting higher mean return, better annual returns, and Sharpe ratios to the S&P 500 index over the sample period 1996 to 2002.

Hedge funds

Abstract:
Following a review of the data and methodological difficulties in applying conventional models used for traditional asset class indexes to hedge funds, this article argues against the conventional approach. Instead, in an extension of previous work on asset-based style (ABS) factors, the article proposes a model of hedge fund returns that is similar to models based on arbitrage pricing theory, with dynamic risk-factor coefficients. For diversified hedge fund portfolios (as proxied by indexes of hedge funds and funds of hedge funds), the seven ABS factors can explain up to 80 percent of monthly return variations. Because ABS factors are directly observable from market prices, this model provides a standardized framework for identifying differences among major hedge fund indexes that is free of the biases inherent in hedge fund databases.

Portfolio Management: Hedge Fund Strategies, Alternative Investments: Hedge Fund Strategies, Risk Measurement and Management: Alternative Investments

Abstract:
Asset-based style factors link returns of hedge fund strategies to observed market prices. They provide explicit and unambiguous descriptions of hedge fund strategies that reveal the nature and quantity of risk. Asset-based style factors are key inputs for portfolio construction and for benchmarking hedge fund performance on a risk-adjusted basis. We used previously developed models to construct asset-based style factors and demonstrate that one model correctly predicted the return behavior of trend-following strategies during out-of-sample periods - in particular, during stressful market conditions like those of September 2001.

Alternative Investments, hedge funds

Abstract:
We discuss the information content and potential measurement biases in hedge fund benchmarks. Hedge-fund indexes built from databases of individual hedge funds inherit the measurement biases in the databases. In addition, broad-based indexes mask the diversity of individual hedge-fund return characteristics. Consequently, these indexes provide incomplete information to investors seeking diversification from traditional asset classes through the use of hedge funds. The approach to constructing hedge-fund benchmarks we propose is based on the simple idea that the most direct way to measure hedge-fund performance is to observe the investment experience of hedge-fund investors themselves - the funds of hedge funds. In terms of measurement biases, returns of FOFs can deliver a cleaner estimate of the investment experience of hedge-fund investors than the traditional approach. In terms of risk characteristics, indexes of FOFs are more indicative of the demand-side dynamics driven by hedge-fund investors' preferences than are broad-based indexes. Therefore, indexes of FOFs can provide valuable information for assessing the hedge-fund industry's performance.

Abstract:
Hedge fund strategies typically generate option-like returns. Linear-factor models using benchmark asset indices have difficulty explaining them. Following the suggestions in Glosten and Jagannathan (1994), this article shows how to model hedge fund returns by focusing on the popular 'trend-following' strategy. We use lookback straddles to model trend-following strategies, and show that they can explain trend-following funds' returns better than standard asset indices. While standard straddles lead to similar empirical results, lookback straddles are theoretically closer to the concept of trend following. Our model should be useful in the design of performance benchmarks for trend-following funds.

Hedge fund, risk management, style factors, trend following, options

Abstract:
It is well known that the pro forma performance of a sample of investment funds contains biases. These biases are documented in Brown, Goetzmann, Ibbotson, and Ross (1992) using mutual funds as subjects. The organization structure of hedge funds, as private and often offshore vehicles, makes data collection a much more onerous task, amplifying the impact of performance measurement biases. This paper reviews these biases in hedge funds. We also propose using funds-of-hedge funds to measure aggregate hedge fund performance, based on the idea that the investment experience of hedge fund investors can be used to estimate the performance of hedge funds.

Abstract:
This paper presents some new results on an unexplored data set on hedge fund performance. The results indicate that hedge funds follow strategies that are dramatically different from mutual funds, and support the claim that these strategies are highly dynamic. The paper finds five dominant investment styles in hedge funds, which, when added to Sharpe's (1992) asset class factor model, can provide an integrated framework for style analysis of both buy-and-hold and dynamic trading strategies.