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A generalized heterogeneous autoregressive model using market information

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A generalized heterogeneous autoregressive model using market information. / Hizmeri, Rodrigo; Izzeldin, Marwan; Nolte, Ingmar et al.
In: Quantitative Finance, Vol. 22, No. 8, 31.08.2022, p. 1513-1534.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Hizmeri, R, Izzeldin, M, Nolte, I & Pappas, V 2022, 'A generalized heterogeneous autoregressive model using market information', Quantitative Finance, vol. 22, no. 8, pp. 1513-1534. https://doi.org/10.1080/14697688.2022.2076606

APA

Hizmeri, R., Izzeldin, M., Nolte, I., & Pappas, V. (2022). A generalized heterogeneous autoregressive model using market information. Quantitative Finance, 22(8), 1513-1534. https://doi.org/10.1080/14697688.2022.2076606

Vancouver

Hizmeri R, Izzeldin M, Nolte I, Pappas V. A generalized heterogeneous autoregressive model using market information. Quantitative Finance. 2022 Aug 31;22(8):1513-1534. Epub 2022 Jun 2. doi: 10.1080/14697688.2022.2076606

Author

Hizmeri, Rodrigo ; Izzeldin, Marwan ; Nolte, Ingmar et al. / A generalized heterogeneous autoregressive model using market information. In: Quantitative Finance. 2022 ; Vol. 22, No. 8. pp. 1513-1534.

Bibtex

@article{7bc7f12fdea04ef29800c9345e6f808f,
title = "A generalized heterogeneous autoregressive model using market information",
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.",
keywords = "Realized volatility, Microstructure noise, Pre-averaged estimators, Semi-variance, Semi-covariance, Volatility forecasting",
author = "Rodrigo Hizmeri and Marwan Izzeldin and Ingmar Nolte and Vasileios Pappas",
year = "2022",
month = aug,
day = "31",
doi = "10.1080/14697688.2022.2076606",
language = "English",
volume = "22",
pages = "1513--1534",
journal = "Quantitative Finance",
issn = "1469-7688",
publisher = "Routledge",
number = "8",

}

RIS

TY - JOUR

T1 - A generalized heterogeneous autoregressive model using market information

AU - Hizmeri, Rodrigo

AU - Izzeldin, Marwan

AU - Nolte, Ingmar

AU - Pappas, Vasileios

PY - 2022/8/31

Y1 - 2022/8/31

N2 - 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.

AB - 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.

KW - Realized volatility

KW - Microstructure noise

KW - Pre-averaged estimators

KW - Semi-variance

KW - Semi-covariance

KW - Volatility forecasting

U2 - 10.1080/14697688.2022.2076606

DO - 10.1080/14697688.2022.2076606

M3 - Journal article

VL - 22

SP - 1513

EP - 1534

JO - Quantitative Finance

JF - Quantitative Finance

SN - 1469-7688

IS - 8

ER -