Dynamic discrete copula models for high-frequency stock price changes

Siem Jan Koopman*, Rutger Lit, André Lucas, Anne Opschoor

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

We develop a dynamic model for the intraday dependence between discrete stock price changes. The conditional copula mass function for the integer tick-size price changes has time-varying parameters that are driven by the score of the predictive likelihood function. The marginal distributions are Skellam and also have score-driven time-varying parameters. We show that the integration steps in the copula mass function for large dimensions can be accurately approximated via numerical integration. The resulting computational gains lead to a methodology that can treat high-dimensional applications. Its accuracy is shown by an extensive simulation study. In our empirical application of 10 US bank stocks, we reveal strong evidence of time-varying intraday dependence patterns: Dependence starts at a low level but generally rises during the day. Based on one-step-ahead out-of-sample density forecasting, we find that our new model outperforms benchmarks for intraday dependence such as the cubic spline model, the fixed correlation model, or the rolling average realized correlation.

Original languageEnglish
Article number33
Pages (from-to)966-985
Number of pages20
JournalJournal of Applied Econometrics
Volume33
Issue number7
Early online date9 Aug 2018
DOIs
Publication statusPublished - Nov 2018

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