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A perspective on recent methods on testing predictability of asset returns

Applied Mathematics(A Journal of Chinese Universities) 2018年第2期33卷 127-144页

作者： LIAO Xiao-sai CAI Zong-wu CHEN Hai-qiang The Wang Yanan Institute for Studies in Economics MOE Key Laboratory of Econometrics (Xiamen University) Fujian Provincial Key Laboratory of Statistical Science Xiamen University Department of Economics University of Kansas

This paper highlights some recent developments in testing predictability of asset returns with focuses on linear mean regressions, quantile regressions and nonlinear regression models. For these models, when predictors are highly persistent and their innovations are contemporarily correlated with dependent variable, the ordinary least squares estimator has a finite-sample bias, and its limiting distribution relies on some unknown nuisance parameter, which is not consistently estimable. Without correcting these issues, conventional test statistics are subject to a serious size distortion and generate a misleading conclusion in testing pre- dictability of asset returns in real applications. In the past two decades, sequential studies have contributed to this subject and proposed various kinds of solutions, including, but not limit to, the bias-correction procedures, the linear projection approach, the IVX filtering idea, the variable addition approaches, the weighted empirical likelihood method, and the double-weight robust approach. Particularly, to catch up with the fast-growing literature in the recent decade, we offer a selective overview of these methods. Finally, some future research topics, such as the econometric theory for predictive regressions with structural changes, and nonparametric predictive models, and predictive models under a more general data setting, are also discussed.

关键词： asset returns Heteroskedasticity high persistency nonlinearity predictability quantile regressions

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