Bivariate volatility modeling with high-frequency data

Marius Matei*, Xari Rovira, Núria Agell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
29 Downloads (Pure)


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.

Original languageEnglish
Article number41
Pages (from-to)1-15
Number of pages15
Issue number3
Publication statusPublished - 15 Sept 2019

Bibliographical note

Copyright 2019 by the authors. Licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Bivariate GARCH
  • Forecasting
  • High-frequency
  • Realized measures
  • Volatility


Dive into the research topics of 'Bivariate volatility modeling with high-frequency data'. Together they form a unique fingerprint.

Cite this