Bivariate volatility modeling with high-frequency data

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...

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Bibliographic Details
Published in:Econometrics 2019-09, Vol.7 (3), p.1-15
Main Authors: Matei, Marius, Rovira, Xari, Agell, Núria
Format: Article
Language:English
Subjects:
bivariate GARCH
Econometrics
Estimates
forecasting
high-frequency
Prices
realized measures
Stochastic models
Volatility
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Summary: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.
ISSN:2225-1146
2225-1146
DOI:10.3390/econometrics7030041