A generalized heterogeneous autoregressive model using market information

Hizmeri, Rodrigo, Izzeldin, Marwan, Nolte, Ingmar and Pappas, Vasileios (2022) A generalized heterogeneous autoregressive model using market information. QUANTITATIVE FINANCE, 22 (8). pp. 1513-1534.

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Official URL: http://dx.doi.org/10.1080/14697688.2022.2076606

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.

Item Type:	Article
Uncontrolled Keywords:	Realized volatility, Microstructure noise, Pre-averaged estimators, Semi-variance, Semi-covariance, Volatility forecasting
Divisions:	Faculty of Humanities and Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	08 Jun 2022 14:21
Last Modified:	18 Jan 2023 21:01
DOI:	10.1080/14697688.2022.2076606
Open Access URL:	https://doi.org/10.1080/14697688.2022.2076606
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3155472