ARCH effects, trading volume and the information flow interpretation: empirical evidence from the Chinese stock markets

Abstract

This study revisits the relation between ARCH effects and trading volume. We extend the specification of the VA-GARCH (1, 1) model by using various volume variants and constructing contrast equity groups. We verify that the information flow assumed to be contained in the four trading volume variants has a starkly different explanatory power compared with the ARCH effects. Successive improvement of the model's empirical fit and the reduction of the fat-tailedness in the model residuals in the sequence of volume adjustment imply an increase in the strength of explaining the static aspects of volatility dynamics by the further adjusted volume variants.

Unexpected Information Demand and Volatility Clustering of Chinese Stock Returns: Evidence from Baidu Index

“…In order to solve the heteroscedasticity issue, Bollerslev [ 16 ] proposed the generalized autoregressive conditional heteroscedasticity model (GARCH), which is designed to deal with the volatility persistence and describe how the amplitude of return varies over time. In this paper, the GARCH(1,1) model was adopted due to the fact that it has been shown to be suitable to deal with conditional variance that fits many financial time series quite well [ 16 , 17 ]. The GARCH model can be described by the following models: where represents the stock return at day .…”