Accomplishment Based Renewal of: Autoregressive Conditional Duration, Arch, Common Features, and Cointegration

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9730062 Engle As computers increase in power and memory it becomes feasible to collect and analyze data at higher and higher frequencies. Data sets that record every transaction - the highest frequency possible - now exist for many financial data sets as well as microeconomic transactions such as telephone calls and credit card purchases that are recorded by computers. The analysis of such data sets poses new and interesting economic challenges, one of them being the choice of the proper interval of time within which to aggregate the data so as to generate a data set with observations spaced evenly part. The problem with fixed interval analysis is that it can leave the investigator with many uninformative data points or disguise the periods of most interest. This Accomplishment Based Renewal continues the study and application to financial microdata of an alternative to fixed interval analysis developed by the investigator. This approach is called Autoregressive Conditional Duration (ACD). Instead of selecting a fixed interval for analyzing the data, the interval between transactions becomes a random variable to be analyzed. Thus the data set becomes a list of durations and characteristics of each transaction. This procedure models the time intervals directly without using auxiliary data or imposing assumptions on the causes of the time flow. The previous grant used the ACD model to analyze the price, volume and duration process of stock transactions. The timing of transactions, order submissions and quote revisions are the focus of new proposed econometric studies. From these analyses it is possible to infer key behavioral aspects of financial markets. The term liquidity is often used to describe effectively functioning markets. With more precise empirical measures of market quality such as the bid ask spread, the depth of the market and the price impact of trade, it is possible to examine the ebb and flow of market liquidity. This is clearly important for assessing the s tability of markets and for institutions seeking to trade large volumes. Using TORQ, TAQ, QEX options, S&P Futures and potentially other data sets, this project measures various dimensions of market liquidity, examines the time series behavior, forecastability, and economic determinants of these measures. ??