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Bivariate Volatility Modeling with High-Frequency Data

Marius Matei, Xari Rovira () and Núria Agell ()
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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

Econometrics, 2019, vol. 7, issue 3, 1-15

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.

Keywords: high-frequency; volatility; forecasting; realized measures; bivariate GARCH (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2019
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