Robust tests of stock return predictability under heavy-tailed innovations  

作　　者：WONG Hsin-Chieh CHUNG Meng-Hua FUH Cheng-Der PANG Tian-xiao 

机构地区：[1]Department of Statistics&Fintech and Green Finance Center,Taipei University,New Taipei City 23741,China [2]Graduate Institute of Statistics,Central University,Taoyuan County 32049,China [3]School of Mathematical Sciences,Zhejiang University,Hangzhou 310058,China

出　　处：《Applied Mathematics(A Journal of Chinese Universities)》2025年第1期149-168,共20页高校应用数学学报(英文版)(B辑)

基　　金：The research of WONG Hsin-Chieh is partially supported by the NSTC(111-2118-M-305-004-MY2);the research of PANG Tian-xiao is partially supported by the National Social Science Foundation of China(21BTJ067)。

摘　　要：This paper provides a robust test of predictability under the predictive regression model with possible heavy-tailed innovations assumption,in which the predictive variable is persistent and its innovations are highly correlated with returns.To this end,we propose a robust test which can capture empirical phenomena such as heavy tails,stationary,and local to unity.Moreover,we develop related asymptotic results without the second-moment assumption between the predictive variable and returns.To make the proposed test reasonable,we propose a generalized correlation and provide theoretical support.To illustrate the applicability of the test,we perform a simulation study for the impact of heavy-tailed innovations on predictability,as well as direct and/or indirect implementation of heavy-tailed innovations to predictability via the unit root phenomenon.Finally,we provide an empirical study for further illustration,to which the proposed test is applied to a U.S.equity data set.

关 键 词：domain of attraction of the normal law heavy-tailed least squares estimator predictive regres-sion unit root robust test 

分 类 号：O17[理学—数学]