Abstract
SummaryWe 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.
Highlights
We investigate how dependence between price changes of financial stocks varies within the day
In our study we have aimed to extend this literature by capturing intraday dynamic features of dependence using an observation-driven model-based copula approach with discrete marginals
The complete dependence model is composed of dynamic Skellam marginal distributions for the discrete price changes combined with a time-varying copula structure
Summary
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 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. 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
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