Testing for linear and nonlinear predictability of stock returns

Abstract

We develop tests for predictability in a first-order ARMA model oftensuggested for stock returns. Instead of the conventional ARMA model,we consider its non-Gaussian and noninvertible counterpart that has identical autocorrelation properties but allows for conditionalheteroskedasticity prevalent in stock returns. In addition to autocorrelation,the tests can also be used to test for nonlinear predictability, incontrast to previously proposed predictability tests based on invertible ARMA models. Simulation results attest to improved power. We apply our tests to postwar U.S. stock returns. All return series considered are found serially uncorrelated but dependent and, hence, nonlinearly predictable.