Dynamic Discrete Copula Models for High Frequency Stock Price Changes (joint with Rutger Lit and Andre Lucas)

We develop a dynamic model for the intraday dependence between discrete stock price changes. The model combines Skellam marginal distributions for one-second positive and negative integer price changes together with discrete copula functions to capture dependence. The marginal and copula parameters vary over time using an observation driven autoregressive updating equation that is based on the score of the conditional probability mass function. Our empirical study investigates the intraday dependence structures between U.S. bank stocks. We consider a range of different copula functions and find strong evidence that dependence between stock price changes varies within a trading day. In particular, dependence is lower on average after the opening and before the closure of the market. We relate this to more idiosyncratic trading during these periods.

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