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Bivariate Volatility Modeling with High-Frequency Data

Author

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  • Marius Matei

    (Department of Economics, Macquarie Business School, Macquarie University, Sydney, NSW 2109, Australia
    Systemic Risk Monitoring Division, Financial Stability Department, National Bank of Romania, Bucharest 030031, Romania
    Centre for Macroeconomic Modelling, National Institute of Economic Research ‘Costin C. Kirițescu’, Romanian Academy, Bucharest 050711, Romania)

  • Xari Rovira

    (Department of Operations, Innovation and Data Sciences, ESADE Business School, Ramon Llull University, E-08172 Sant Cugat, Spain)

  • Núria Agell

    (Department of Operations, Innovation and Data Sciences, ESADE Business School, Ramon Llull University, E-08172 Sant Cugat, Spain)

Abstract

We propose a methodology to include night volatility estimates in the day volatility modeling problem with high-frequency data in a realized generalized autoregressive conditional heteroskedasticity (GARCH) framework, which takes advantage of the natural relationship between the realized measure and the conditional variance. This improves volatility modeling by adding, in a two-factor structure, information on latent processes that occur while markets are closed but captures the leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that includes intraday, day, and night volatility estimates is proposed and was empirically tested to confirm whether using night volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate models over those that do not include night volatility estimates.

Suggested Citation

  • Marius Matei & Xari Rovira & Núria Agell, 2019. "Bivariate Volatility Modeling with High-Frequency Data," Econometrics, MDPI, vol. 7(3), pages 1-15, September.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:3:p:41-:d:267457
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    Cited by:

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    2. Kumar SANTOSH & Meher Kumar BHARAT & Ramona BIRAU & Mircea Laurentiu SIMION & Anand ABHISHEK & Singh MANOHAR, 2023. "Quantifying Long-Term Volatility for Developed Stock Markets: An Empirical Case Study Using PGARCH Model on Toronto Stock Exchange (TSX)," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-68.

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