Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects

Abstract

This paper provides empirical support for the notion that Autoregressive Conditional Heteroskedasticity (ARCH) in daily stock return data reflects time dependence in the process generating information flow to the market. Daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns, which is an implication of the assumption that daily returns are subordinated to intraday equilibrium returns. Furthermore, ARCH effects tend to disappear when volume is included in the variance equation.

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

“…However, for the stock returns The financial press embraces the economic theory, backing such variations in conditional variances of stocks. Lamoureux and Lastrapes (1990) stated that at the micro level, it is the manifestation of clustering in trading volume that feeds the ARCH effect. However, at the macro level; business cycle and information patterns together with macro-economic factors (e.g.…”

Foreign exchange market data analysis reveals statistical features that predict price movement acceleration

“…After the BS model, many other models were proposed for interest rate, forex, and stocks. It is also well known that the ARCH model and related models were introduced in order to reproduce volatility clustering, one of the established facts in financial engineering [18][19][20][21][22][23][24].…”

“…This assumption is used in the Black-Scholes model [17]. Furthermore, many other financial data-based models, such as autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH models, change the volatility according to the price changes one or several time steps before [18][19][20][21][22][23][24]. This means that the volatility does not depend on the distant past prices, although it depends on recent volatility (short memory).…”

Time-varying international stock market interaction and the identification of volatility signals

“…This is likely to be one of the main reasons why information flow was connected to volatility in the first place. By the same token, a strand of literature examined trading volume as an observable variable that is at least partly driven by the information arrival process; see Tauchen and Pitts (1983), Harris (1987), Lamoureux and Lastrapes (1990), Viswanathan (1993, 1995), Gagnon and Karolyi (2009). Certainly, volume cannot explain volatility, in the sense of an exogenous variable.…”