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Autoregressive conditional duration: a new model for irregularly spaced transaction data. (English) Zbl 1055.62571
Summary: This paper proposes a new statistical model for the analysis of data which arrives at irregular intervals. The model treats the time between events as a stochastic process and proposes a new class of point processes with dependent arrival rates. The conditional intensity is developed and compared with other self-exciting processes. The model is applied to the arrival times of financial transactions and therefore is a model of transaction volume, and also to the arrival of other events such as price changes. Models for the volatility of prices are estimated, and examined from a market microstructure point of view.

MSC:
62P20 Applications of statistics to economics
91B62 Economic growth models
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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