3.8 Article

Bivariate Volatility Modeling with High-Frequency Data

Journal

ECONOMETRICS
Volume 7, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/econometrics7030041

Keywords

high-frequency; volatility; forecasting; realized measures; bivariate GARCH

Categories

Funding

  1. Agency for Management of University and Research Grants (AGAUR) of the Government of Catalonia [IUE/2681/2008, 5208]
  2. ESADE Business School (Ramon Llull University)
  3. University of Tasmania [ARC DP130100168]

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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.

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