In this paper we introduce an ACD-ECOGARCH(1, 1) model. An xponential autoregressive conditional duration model is used to describe the dependence structure in durations of ultra-highfrequency financial data. The innovation process of the ACD model then defines the interarrival times of a compound Poisson process. We use this compound Poisson process as the background driving Levy process of an exponential continuous time GARCH(1, 1) process. The dynamics of the random time transformed log-price process are then described by the latter process. To estimate its parameters we construct a quasi maximum likelihood estimator under the assumption that all jumps of the logprice process are observable. Finally the model is fitted for illustrative purpose to General Motors tick-by-tick data of the New York Stock Exchange.     «

In this paper we introduce an ACD-ECOGARCH(1, 1) model. An xponential autoregressive conditional duration model is used to describe the dependence structure in durations of ultra-highfrequency financial data. The innovation process of the ACD model then defines the interarrival times of a compound Poisson process. We use this compound Poisson process as the background driving Levy process of an exponential continuous time GARCH(1, 1) process. The dynamics of the random time transformed log-pric...     »