Stock return autocorrelations revisited: A quantile regression approach

The aim of this study is to provide a comprehensive description of the dependence pattern of stock returns by studying a range of quantiles of the conditional return distribution using quantile autoregression. This enables us to study the behavior of extreme quantiles associated with large positive and negative returns in contrast to the central quantile which is closely related to the conditional mean in the least-squares regression framework. Our empirical results are based on 30 years of daily, weekly and monthly returns of the stocks comprised in the Dow Jones Stoxx 600 index. We find that lower quantiles exhibit positive dependence on past returns while upper quantiles are marked by negative dependence. This pattern holds when accounting for stock specific characteristics such as market capitalization, industry, or exposure to market risk.

► We study the conditional distribution of stock returns using quantile autoregression. ► We distinguish the dependence of extreme quantiles and the median. ► Lower (upper) quantiles are marked by positive (negative) dependence on past returns. ► The pattern holds when accounting for certain stock specific characteristics.