We introduce the dynamic DNB model for financial tick by tick data. Our model explicitly takes into account the discreteness of the observed prices, fat tails of tick returns and intraday patterns of volatility. We propose a Markov chain Monte Carlo estimation method, which takes advantage of an auxiliary mixture representation of the DNB distribution.  We illustrate our methodology using tick by tick data of several stocks from the NYSE in different periods. Using predictive likelihoods we find evidence for the dynamic DNB model.

Discussant: Yang Liu (University of Amsterdam)

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