The relationship between trading volumes, number of transactions, and stock volatility in GARCH models

TL;DR

This study investigates how trading volumes and transaction counts relate to stock volatility in the Tokyo Stock Exchange using GARCH models, revealing that these proxies do not fully capture information flow affecting volatility.

Contribution

It applies GARCH modeling to assess the explanatory power of trading volumes and transaction counts on volatility, challenging the mixture of distributions hypothesis.

Findings

GARCH effects persist despite including trading volumes and transactions

Trading volumes and transaction counts do not fully represent information arrivals

Volatility persistence is not always explained by these proxies

Abstract

We examine the relationship between trading volumes, number of transactions, and volatility using daily stock data of the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, we use trading volumes and the number of transactions as proxy for the rate of information arrivals affecting stock volatility. The impact of trading volumes or number of transactions on volatility is measured using the generalized autoregressive conditional heteroscedasticity (GARCH) model. We find that the GARCH effects, that is, persistence of volatility, is not always removed by adding trading volumes or number of transactions, indicating that trading volumes and number of transactions do not adequately represent the rate of information arrivals.