This project continues research on nonlinear regularities that emerge from apparently chaotic daily and weekly movements in financial asset prices. The research focuses on intriguing regularities in the behavior of stock market indices and foreign exchange markets discovered earlier by the investigator. The results of the research should improve our understanding of the way institutions for processing information affect the stability of financial markets. It should also provide better statistical techniques based on chaos theory for studying nonlinear relationships in economic time series. The project addresses the connections between correlations and volatility in stock returns series at the daily and weekly frequencies. These correlation patterns are shown to change with changing market volatility levels. Correlations are larger at smaller volatility levels. The robustness of the results is determined by analyzing data for individual firms. Some possible causes for the results are explored. Transactions costs and costs of acquiring new information may cause prices to be less informative during quieter periods. If these costs are unrelated to the level of volatility then periods of low volatility may appear less "efficient" in the sense of positive correlations. New information may take several days to get impounded into prices. These correlations may also be the result of market maker interventions. In their attempts at maintaining price continuity market makers actions could show up as positive correlations. During volatile periods information and prices are changing so fast that the actions of the market maker are not observed over the daily horizon. A closely related issue is how quickly limit orders are being eliminated and if these also carry across the daily boundaries.
