This paper reconsiders return-volume dependence for the U.S. and six international equity markets. We contribute to previous work by proposing surprise volume as a new proxy for private information flow and apply extreme value theory in studying dependence for large volume and return, i.e. under...

Generally there is a common belief that returns and trading activities have a strong positive relationship. This paper analyzes return-volume relationship in Indian context, both in contemporaneous as well as lead- lag. Initial screening of returns and trading activity data shows some...

We propose a model with heterogeneous interacting traders which can explain some of the stylized facts of stock market returns. In the model, synchronization effects, which generate large fluctuations in returns, can arise purely from communication and imitation among traders. The key element in...

Heteroskedasticity in returns may be explainable by trading volume. We use different volume variables, including surprise volume---i.e. unexpected above-average trading activity---which is derived from uncorrelated volume innovations. Assuming weakly exogenous volume, we extend the Lamoureux and...

A computerized double auction market with human traders is employed to examine the relation of price and volume under conditions of asymmetric information. In this market, the informed traders receive higher precision signals than the uninformed traders. The relation of price and volume has been...

We examine the effects of thin trading on the specification of event study tests. Simu-lations of upper and lower tail tests are reported with and without variance increases on the event date across levels of trading volume. The traditional standardized test is mis-specified for thinly traded...

The price of financial assets are, since Bachelier, considered to be described by a (discrete or continuous) time sequence of random variables, i.e a stochastic process. Sharp scaling exponents or unifractal behavior of such processes has been reported in several works. In this letter we...

Continuous-time stochastic volatility models are becoming a more and more popular way to describe moderate and high-frequency financial data. Recently, Barndorff-Nielsen and Shephard (2001a) proposed a class of models where the volatility behaves according to an Ornstein-Uhlenbeck process,...

The uncertainty of predicting stock prices emanates pre-eminent concerns around the functionality of the stock market. The possibility of utilising Genetic Algorithms to forecast the momentum of stock price has been previously explored by many optimisation models that have subsequently addressed...