Bivariate Volatility Modeling with High-Frequency Data

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.

Stock Market Volatility and Return Analysis: A Systematic Literature Review

“…The bivariate models showed achieve better in most cases, compared with the univariate models [ 85 ]. MGARCH models could be used for forecasting.…”

Measuring Leverage Effect of Covid 19 on Stock Price Volatility of Energy Companies Using High Frequency Data

“…A paper suggested a methodology to refine modelling volatility by inculcating information that exists on latent volatility processes when the markets are closed and no transactions occur with highfrequency data (Matei et al, 2019).…”