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)
PDF
Publisher pdf
Language: English
This work is licensed under a Creative Commons Attribution 4.0 International License.
|
|
Download this file (PDF/1MB) |
Preview |
Request a format suitable for use with assistive technology e.g. a screenreader | |
PDF
Author's Accepted Manuscript
Language: English Restricted to Repository staff only |
|
Contact us about this Publication
|
|
Official URL: 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: | 04 Jul 2023 11:12 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/94915 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):