Next Article in Journal
Partial Cointegrated Vector Autoregressive Models with Structural Breaks in Deterministic Terms
Next Article in Special Issue
Representation of Japanese Candlesticks by Oriented Fuzzy Numbers
Previous Article in Journal
Forecast Bitcoin Volatility with Least Squares Model Averaging
Previous Article in Special Issue
Covariance Prediction in Large Portfolio Allocation
Open AccessArticle

Bivariate Volatility Modeling with High-Frequency Data

by Marius Matei 1,2,3,*, Xari Rovira 4 and Núria Agell 4
1
Department of Economics, Macquarie Business School, Macquarie University, Sydney, NSW 2109, Australia
2
Systemic Risk Monitoring Division, Financial Stability Department, National Bank of Romania, Bucharest 030031, Romania
3
Centre for Macroeconomic Modelling, National Institute of Economic Research ‘Costin C. Kirițescu’, Romanian Academy, Bucharest 050711, Romania
4
Department of Operations, Innovation and Data Sciences, ESADE Business School, Ramon Llull University, E-08172 Sant Cugat, Spain
*
Author to whom correspondence should be addressed.
Econometrics 2019, 7(3), 41; https://doi.org/10.3390/econometrics7030041
Received: 6 August 2018 / Revised: 4 September 2019 / Accepted: 6 September 2019 / Published: 15 September 2019
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. View Full-Text
Keywords: high-frequency; volatility; forecasting; realized measures; bivariate GARCH high-frequency; volatility; forecasting; realized measures; bivariate GARCH
MDPI and ACS Style

Matei, M.; Rovira, X.; Agell, N. Bivariate Volatility Modeling with High-Frequency Data. Econometrics 2019, 7, 41.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop