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A generalized heterogeneous autoregressive model using market information

Hizmeri, Rodrigo, Izzeldin, Marwan, Nolte, Ingmar, Pappas, Vasileios (2022) A generalized heterogeneous autoregressive model using market information. Quantitative Finance, . ISSN 1469-7688. (doi:10.1080/14697688.2022.2076606) (KAR id:94915)

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https://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
DOI/Identification number: 10.1080/14697688.2022.2076606
Uncontrolled keywords: Realized volatility; Microstructure noise; Pre-averaged estimators; Semi-variance; Semi-covariance; Volatility forecasting
Subjects: H Social Sciences > HG Finance
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance
Depositing User: Vasileios Pappas
Date Deposited: 06 May 2022 16:16 UTC
Last Modified: 16 Jun 2022 09:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/94915 (The current URI for this page, for reference purposes)
Pappas, Vasileios: https://orcid.org/0000-0003-1885-4832
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