A Generalized Heterogeneous Autoregressive Model using Market Information

This paper introduces a novel class of volatility forecasting models that incorporate market realized (co)variances and semi(co)variances within the framework of a heterogeneous autoregressive (HAR) model. Our empirical analysis shows statistically and economically significant forecasting gains. For our most parsimonious market-HAR specification, stock volatility forecasting is improved by 9.80% points. Using a mixed sampling frequency market-HAR variant with low (high) sampling frequency for the stock (market) improves forecasting by a further 6.90% points. Our paper also develops noise-robust estimators to facilitate the use of realized semi(co)variances at high sampling frequencies.

Keywords: Realized Volatility, Microstructure Noise, Pre-Averaged Estimators, Semi-variances, Semicovariances, Volatility Forecasting.

JEL Classification: C22, C51, C58, G11

Suggested Citation: Suggested Citation

Hizmeri, Rodrigo and Izzeldin, Marwan and Nolte, Ingmar and Pappas, Vasileios, A Generalized Heterogeneous Autoregressive Model using Market Information (May 5, 2022). Quantitative Finance (Forthcoming), Available at SSRN: https://ssrn.com/abstract=3496804 or http://dx.doi.org/10.2139/ssrn.3496804