A generalized heterogeneous autoregressive model using market information

Rodrigo Hizmeri, Marwan Izzeldin, Ingmar Nolte and Vasileios Pappas

Quantitative Finance, 2022, vol. 22, issue 8, 1513-1534

Abstract: 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.

Date: 2022
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DOI: 10.1080/14697688.2022.2076606

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