Chaos theory has touched on such fields as biology, cognitive science, and physics. By providing a unified and complete explanation of new statistical methods that are useful for testing for chaos in data sets, Brock, Hsieh, and LeBaron show how the principles of chaos theory can be applied to...

This paper explores the time series implications of introducing credit constraints into a production based asset pricing model. Simulations are performed choosing parameter values which generate reasonable values for aggregate fluctuations. These results show that mean reversion in simulated...

This paper seeks to develop a structural model that lets data on asset returns and trading volume speak to whether volatility autocorrelation comes from the fundamental that the trading process is pricing or, is caused by the trading process itself. Returns and volume data argue, in the context...

A fundamental statistical test of serial independence is developed and applied to daily stock returns. Let <italic>x</italic> be the deviation of the daily return on a stock from its sample mean after any autocorrelation present has been removed. If <italic>x</italic> is serially independent, then the cumulative sum of <italic>x</italic> over...

Much research has demonstrated the existence of patterns in high-frequency equity returns, return volatility, bid-ask spreads and trading volume. In this paper, we employ a new test for detecting periodicities based on a signal coherence function. The technique is applied to the returns, bid-ask...