Trading volume and realized volatility forecasting: Evidence from the China stock market

Abstract

The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research offers new insights into the volume‐volatility nexus by decomposing and reconstructing the trading activity into short‐run components that typically represent irregular information flow and long‐run components that denote extreme information flow in the stock market. We are the first to attempt at incorporating an improved empirical mode decomposition (EMD) method to investigate the volatility forecasting ability of trading volume along with the Heterogeneous Autoregressive (HAR) model. Previous trading volume is used to obtain the decompositions to forecast the future volatility to ensure an ex ante forecast, and both the decomposition and forecasting processes are carried out by the rolling window scheme. Rather than trading volume by itself, the results show that the reconstructed components are also able to significantly improve out‐of‐sample realized volatility (RV) forecasts. This finding is robust both in one‐step ahead and multiple‐step ahead forecasting horizons under different estimation windows. We thus fill the gap in studies by (1) extending the literature on the volume‐volatility linkage to EMD‐HAR analysis and (2) providing a clear view on how trading volume helps improve RV forecasting accuracy.

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