Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

First Advisor

Christopher G. Lamoureux


The objective of this dissertation is to examine the stock price volatility-volume relationship. The dissertation begins with an estimation of the time deformation market model in which stock contemporaneous trading volume is utilized as a proxy for the rate of information arrival. This local time market model is economically appealing because it is capable of explaining the observed heteroskedasticity and leptokurtosis in daily return data. With a sample of firms which have stock splits, it is shown that the inferences drawn from a modified event study which incorporates the local time market model are similar to those drawn from a typical event study which uses the simple OLS market model. In other words, a typical event study which employs daily stock return data and the OLS market model yields robust inferences in spite of the violation of the normality assumption in daily return data. The time deformation market model is also able to show that the increase in price volatility induced by stock splits is due to a structural change in the relationship between economic time and calendar time. The impact of option introduction on the stock price volatility-volume relationship is investigated. The asymmetric price change-volume relationship is affected by option trading because option trading is capable of reducing the short selling constraints. Although exactly how option trading can affect the asymmetry is a complex matter, the empirical findings do give mild support to the hypothesis that option trading can attenuate the asymmetric price change-volume relationship. Option trading also influences the local time market model. Empirical evidence is supportive of a structural shift in the model. These findings are consistent with the notion that information flows to both the stock and the options markets when option trading is viable. Also, one-day-lagged option volume is important in explaining the conditional return variance.