David A. Hsieh

Performance Characteristics of Hedge Funds and Commodity Funds: Natural vs. Spurious Biases

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. Theis 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.

Testing for Nonlinear Dependence in Daily Foreign Exchange Rates

The purpose of this article is to investigate whether daily changes in five major foreign exchange rates contain any nonlinearities. Although the data contain no linear correlation, evidence indicates the presence of substantial nonlinearity in a multiplicative rather than additive form. Further examination reveals that a generalized autoregressive conditional heteroskedasticity model can explain a large part of the nonlinearities for all five exchange rates. Copyright 1989 by the University of Chicago.

Nonlinear dynamics, chaos, and instability : statistical theory and economic evidence

The BDS statistic the changing structure of stock returns nonlinearity in foreign exchange summary, relation to other work, and future horizons. Appendices: size and distribution of the BDS statistic quantiles of the BDS statistic size of the BDS statistic on GARCH residuals.

The statistical properties of daily foreign exchange rates: 1974–1983

Abstract This paper examines the statistical properties of daily rates of change of five foreign currencies from 1974 to 1983. The main purpose is to discriminate between two competing explanations for the observed heavy tails of the distribution: that the data are independently drawn from a heavy tail distribution which remains fixed over time, and that the data come from distributions which vary over time. Evidence point to the rejection of the first hypothesis. Further investigations show that the rejection can be attributed to changing means and variances in the data, which can be described by a simple statistical model.

An exploratory investigation of the firm size effect

Abstract We investigate the firm size effect for the period 1958 to 1977 in the framework of a multi-factor pricing model. The risk-adjusted difference in returns between the top five percent and the bottom five percent of the NYSE firms is about one to two percent a year, a drop from about twelve percent per year before risk adjustment. The variable most responsible for the adjustment is the sensitivity of asset returns to the changing risk premium, measured by the return difference between low-grade bonds and long-term government bonds.

Modeling Heteroscedasticity in Daily Foreign-Exchange Rates

This article estimates autoregressive conditionally heteroscedastic (ARCH) and generalized ARCH (GARCH) models for five foreign currencies, using 10 years of daily data, a variety of ARCH and GARCH specifications, a number of nonnormal error densities, and a comprehensive set of diagnostic checks. It finds that ARCH and GARCH models can usually remove all heteroscedasticity in price changes in all five currencies. Goodness-of-fit diagnostics indicate that exponential GARCH with certain nonnormal distributions fits the Canadian dollar extremely well and the Swiss franc and the deutsche mark reasonably well. Only one nonnormal distribution fits the Japanese yen reasonably well. None fit the British pound.

A primer on hedge funds

Abstract In this paper, we provide a rationale for how hedge funds are organized and some insight on how hedge fund performance differs from traditional mutual funds. Statistical differences among hedge fund styles are used to supplement qualitative differences in the way hedge fund strategies are described. Risk factors associated with different trading styles are discussed. We give examples where standard linear statistical techniques are unlikely to capture the risk of hedge fund investments where the returns are primarily driven by non-linear dynamic strategies.

Hedge Fund Benchmarks: A Risk-Based Approach

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.

The determination of the real exchange rate: The productivity approach

Abstract This paper explains deviations of exchange rates from purchasing power parity with the differences between countries of the relative growth rates of labor productivity between traded and nontraded sectors. Two cases are considered: Germany and Japan versus their respective major trading partners. The results show that the time series methodology yields a more favorable confirmation of the productivity differential model than the cross section regressions in the literature.

Implications of Nonlinear Dynamics for Financial Risk Management

This paper demonstrates that when log price changes are not IID, their conditional density may be more accurate than their unconditional density for describing short-term behavior. Using the BDS test of independence and identical distribution, daily log price changes in four currency futures contracts are found to be not IID. While there appear to be no predictable conditional mean changes, conditional variances are predictable, and can be described by an autoregressive volatility model that seems to capture all the departures from independence and identical distribution. Based on this model, daily log price changes are decomposed into a predictable part, which is described parametrically by the autoregressive volatility model, and an unpredictable part, which can be modeled by an empirical density, either parametrically or nonparametrically. This two-step seminonparametric method yields a conditional density for daily log price changes, which has a number of uses in financial risk management.