Cross-sectional return dispersion and volatility prediction

Tianlun Fei,Xiaoquan Liu,Conghua Wen

doi:10.1016/j.pacfin.2019.101218

Abstract

We use intraday and daily data to examine the impact of cross-sectional return dispersion on volatility forecasting in the Chinese equity market. We adopt the GARCH, GJR-GARCH, and HAR models and, by augmenting them with return dispersion measures, provide empirical evidence that the return dispersion exhibits substantial information in describing the volatility dynamics by generating significantly lower forecasting errors at market and industry levels. Furthermore, the information content of the return dispersion tends to offer economic gain to a mean-variance utility investor. The findings are robust with respect to alternative volatility proxies, subsample analysis, and alternative market-wide stock indices.

Highlights

In this paper, we explore the information content of the cross-sectional dispersion of stock returns for the purpose of volatility prediction
We implement the generalized autoregressive conditional heteroskedasticity model (GARCH), GJR-GARCH, and the heterogeneous autoregressive (HAR) models whereas only the GARCH model is adopted in these studies; second, we use six different measures of stock return dispersion, which is more comprehensive; third and importantly, we explore the relation between stock dispersion and volatility in the biggest emerging market when these studies focus on the US and UK equity markets; and we examine volatility forecasts both in statistical and economic terms, whereas the economic value of volatility forecasts is not considered in these related papers
The X variable is the cross-sectional standard deviation (CSSD) and cross-sectional absolute deviation (CSAD), both and value-weighted from individual stocks, and and value-weighted CSSD based on size- and BM-sorted portfolios, that capture the cross section of stock return dispersion

Summary

Introduction

We explore the information content of the cross-sectional dispersion of stock returns for the purpose of volatility prediction. Our paper is motivated by the existing literature that examines the role of return dispersion in financial markets. With regard to volatility, Stivers (2003) and Connolly and Stivers (2006) clearly outline economic channels through which firm return dispersion impacts on future aggregate volatility. From an economic perspective, the literature shows that return dispersion is related to the macroeconomic environment and influences activities such as unemployment (Loungani et al, 1990), economic expansion and recessions (Duffee, 2001), and financial integration (Bekaert and Harvey, 1997). The evidence suggests that stock return dispersion is likely to contain information for describing future state of the economy, part of which is captured by market volatility

Methods

Results

Conclusion