• Graduate program
    • Why Tinbergen Institute?
    • Program Structure
    • Courses
    • Course Registration
    • Recent PhD Placements
    • Facilities
    • Admissions
  • Research
  • News
  • Events
    • Summer School
      • Crash Course in Experimental Economics
      • Introduction in Genome-Wide Data Analysis
      • Research on Productivity, Trade, and Growth
      • Econometric Methods for Forecasting and Data Science
  • Times

Koopman, S., Lit, R., Lucas, A., and Opschoor, A. (2018). Dynamic discrete copula models for high-frequency stock price changes. Journal of Applied Econometrics, 33(7):966-985.

  • Journal
    Journal of Applied Econometrics

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.