Integer-Valued Moving Average Modelling of the Number of Transactions in Stocks
The integer-valued moving average model is advanced to model the number of transactions in intra-day data of stocks. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study the least squares estimator comes out as the best choice. Empirically we find support for the use of long-lag moving average models in a Swedish stock series. News about prices are found to exert a symmetric and positive effect on the number of transactions.
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