Dr. Andrew J. Patton - Scholarly Author

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Dr. Andrew J. Patton
Lecturer in Finance
Department of Accounting and Finance, London School of Economics

Academic Home Page • Personal Home Page • Curriculum Vitae

Research Interests:
• Financial econometrics
• Forecasting
• Volatility and dependence modeling
• Copulas
• Portfolio decisions

     Dr. Patton's Table of Contents
     in chronological order

Are “Market Neutral” Hedge Funds Really Market Neutral?
by Andrew J. Patton
October 5, 2005
Testable Implications of Forecast Optimality
by Andrew J. Patton & Allan G. Timmermann
November, 2004
On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation
by Andrew J. Patton
March, 2004
Simple Tests for Models of Dependence Between Multiple Financial Time Series, with Applications to U.S. Equity Returns and Exchange Rates
by Andrew J. Patton, Xiaohong Chen, & Yanqin Fan
January, 2004
Common Factors in Conditional Distributions for Bivariate Time Series
by Andrew J. Patton, Clive W.J. Granger, & Timo Terasvirta
November, 2003
Properties of Optimal Forecasts
by Andrew J. Patton & Allan G. Timmermann
August, 2003
Estimation of Copula Models for Time Series of Possibly Different Lengths
by Andrew J. Patton
November, 2001

Are “Market Neutral” Hedge Funds Really Market Neutral?
by Andrew J. Patton
London School of Economics
October 5, 2005

Abstract
Using a variety of different definitions of “neutrality”, we find significant evidence against the neutrality to market risk of hedge funds in a range of style categories, including the “market neutral” category. We suggest that the market neutrality of hedge funds has a “breadth” and a “depth” component: breadth reflects the number of market risks to which a fund is neutral, while depth reflects the “completeness” of the neutrality of the fund to market risks. We focus on neutrality depth, and propose five different neutrality concepts. “Mean neutrality” nests the standard correlation-based definition of neutrality. “Variance neutrality” and “tail neutrality” relate to the neutrality of the risk of the hedge fund to market risks. Finally, “complete neutrality” corresponds to independence of the fund to market risks. We suggest statistical tests for each neutrality concept, and apply the tests to a combined database of monthly returns on 1,619 hedge funds from five fund styles categories. For the so-called “market neutral” style we find that around one-quarter of funds exhibit some significant exposure to market risk; this proportion is statistically significantly different from zero, but less than the proportion of significant exposures for other hedge fund styles.

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Testable Implications of Forecast Optimality
by Andrew J. Patton & Allan G. Timmermann
London School of Economics & University of California, San Diego
November, 2004

Abstract
Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. This paper considers properties of optimal forecasts under general loss functions and establishes new testable implications of forecast optimality. These hold when the forecaster's loss function is unknown but testable restrictions can be imposed on the data generating process, trading off conditions on the data generating process against conditions on the loss function. Finally, we propose flexible parametric estimation of the forecaster's loss function, and obtain a test of forecast optimality via a test of over-identifying restrictions.

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On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation
by Andrew J. Patton
London School of Economics - Financial Markets Group
March, 2004

Abstract
Recent studies in the empirical finance literature have reported evidence of two types of asymmetries in the joint distribution of stock returns. The first is skewness in the distribution of individual stock returns. The second is an asymmetry in the dependence between stocks: stock returns appear to be more highly correlated during market downturns than during market upturns. In this paper we examine the economic and statistical significance of these asymmetries for asset allocation decisions in an out-of-sample setting. We consider the problem of a CRRA investor allocating wealth between the risk-free asset, a small-cap and a large-cap portfolio. We use models that can capture time-varying moments up to the fourth order, and we use copula theory to construct models of the time-varying dependence structure that allow for different dependence during bear markets than bull markets. The importance of these two asymmetries for asset allocation is assessed by comparing the performance of a portfolio based on a normal distribution model with a portfolio based on a more flexible distribution model. For investors with no short sales constraints we find that knowledge of higher moments and asymmetric dependence leads to gains that are economically significant, and statistically significant in some cases. For short sales constrained investors the gains are limited.

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Simple Tests for Models of Dependence Between Multiple Financial Time Series, with Applications to U.S. Equity Returns and Exchange Rates
by Andrew J. Patton, Xiaohong Chen, & Yanqin Fan
London School of Economics, New York University, & Vanderbilt University
January, 2004

Abstract
Evidence that asset returns are more highly correlated during volatile markets and during market downturns (see Longin and Solnik, 2001, and Ang and Chen, 2002) has lead some researchers to propose alternative models of dependence. In this paper we develop two simple goodness-of-fit tests for such models. We use these tests to determine whether the multivariate Normal or the Student's t copula models are compatible with U.S. equity return and exchange rate data. Both tests are robust to specifications of marginal distributions, and are based on the multivariate probability integral transform and kernel density estimation. The first test is consistent but requires the estimation of a multivariate density function and is recommended for testing the dependence structure between a small number of assets. The second test may not be consistent against all alternatives but it requires kernel estimation of only a univariate density function, and hence is useful for testing the dependence structure between a large number of assets. We justify our tests for both observable multivariate strictly stationary time series and for standardized innovations of GARCH models. A simulation study demonstrates the efficacy of both tests. When applied to equity return data and exchange rate return data, we find strong evidence against the normal copula, but little evidence against the more flexible Student's t copula.

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Common Factors in Conditional Distributions for Bivariate Time Series
by Andrew J. Patton, Clive W.J. Granger, & Timo Terasvirta
London School of Economics, University of California, San Diego, & Stockholm School of Economics
November, 2003

Abstract
A definition for a common factor for bivariate time series is suggested by considering the decomposition of the conditional density into the product of the marginals and the copula, with the conditioning variable being a common factor if it does not directly enter the copula. We show the links between this definition and the idea of a common factor as a dominant feature in standard linear representations. An application using a business cycle indicator as the common factor in the relationship between U.S. income and consumption found that both series held the factor in their marginals but not in the copula.

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Properties of Optimal Forecasts
by Andrew J. Patton & Allan G. Timmermann
London School of Economics & University of California, San Diego
August, 2003

Abstract
Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single-period horizon with increasing variance as the forecast horizon grows. Using analytical results we show in this Paper that all the standard properties of optimal forecasts can be invalid under asymmetric loss and non-linear data-generating processes and thus may be very misleading as a benchmark for an optimal forecast. Our theoretical results suggest that many of the conclusions in the empirical literature concerning sub-optimality of forecasts could be premature. We extend the properties that an optimal forecast should have to a more general setting than previously considered in the literature. We also present new results on forecast error properties that may be tested when the forecaster's loss function is unknown but restrictions can be imposed on the data-generating process, and introduce a change of measure, following which the optimum forecast errors for general loss functions have the same properties as optimum errors under MSE loss.

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Estimation of Copula Models for Time Series of Possibly Different Lengths
by Andrew J. Patton
London School of Economics
November, 2001

Abstract
The theory of conditional copulas provides a means of constructing flexible multivariate density models, allowing for time-varying conditional densities of each individual variable, and for time-varying conditional dependence between the variables. Further, the use of copulas in constructing these models often allows for the partitioning of the parameter vector into elements relating only to a marginal distribution, and elements relating to the copula. This paper presents a two-stage (or multi-stage) maximum likelihood estimator for the case that such a partition is possible. We extend the existing statistics literature on the estimation of copula models to consider data that exhibit temporal dependence and heterogeneity. The estimator is flexible enough that the case that unequal amounts of data are available on each variable is easily handled. We investigate the small sample properties of the estimator in a Monte Carlo study, and find that it performs well in comparisons with the standard (one-stage) maximum likelihood estimator. Finally, we present an application of the estimator to a model of the joint distribution of daily Japanese yen - U.S. dollar and euro - U.S. dollar exchange rates. We find some evidence that a copula that captures asymmetric dependence performs better than those that assume symmetric dependence.

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