HYBRID GARCH Models and Intra-Daily Return Periodicity

Xilong Chen 1 , Eric Ghysels 2  and Fangfang Wang 3
  • 1 SAS Institute
  • 2 The University of North Carolina at Chapel Hill
  • 3 University of Illinois at Chicago

We use the HYBRID GARCH model of Chen, Ghysels, and Wang (2009) to predict future volatility at daily horizons using intra-daily returns. The latter requires us to address intra-daily periodic patterns. We propose two approaches and compare their relative merits. The first approach uses raw intra-daily data—with the HYBRID process capturing the intra-daily periodic patterns—whereas the second approach involves pre-adjusted intra-daily returns. We find that the former approach dominates both in-sample and out-of-sample, although for different HYBRID GARCH model specifications.

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The Journal of Time Series Econometrics (JTSE) serves as an internationally recognized outlet for important new research in both theoretical and applied classical and Bayesian time series, spatial and panel data econometrics. The scope of the journal includes papers dealing with estimation, testing and other methodological aspects involved in the application of time series and spatial analytic techniques to economic, financial and related data.