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  • Volatility Forecasting Paper_QF

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 24/04/2019, available online: https://www.tandfonline.com/doi/full/10.1080/14697688.2019.1600713

    Accepted author manuscript, 0.98 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Forecasting Realised Volatility Using ARFIMA and HAR Models

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

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Forecasting Realised Volatility Using ARFIMA and HAR Models. / Izzeldin, Marwan; Hassan, M. Kabir; Pappas, Vasileios et al.
In: Quantitative Finance, Vol. 19, No. 10, 01.10.2019, p. 1627-1638.

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

Harvard

Izzeldin, M, Hassan, MK, Pappas, V & Tsionas, M 2019, 'Forecasting Realised Volatility Using ARFIMA and HAR Models', Quantitative Finance, vol. 19, no. 10, pp. 1627-1638. https://doi.org/10.1080/14697688.2019.1600713

APA

Izzeldin, M., Hassan, M. K., Pappas, V., & Tsionas, M. (2019). Forecasting Realised Volatility Using ARFIMA and HAR Models. Quantitative Finance, 19(10), 1627-1638. https://doi.org/10.1080/14697688.2019.1600713

Vancouver

Izzeldin M, Hassan MK, Pappas V, Tsionas M. Forecasting Realised Volatility Using ARFIMA and HAR Models. Quantitative Finance. 2019 Oct 1;19(10):1627-1638. Epub 2019 Apr 24. doi: 10.1080/14697688.2019.1600713

Author

Izzeldin, Marwan ; Hassan, M. Kabir ; Pappas, Vasileios et al. / Forecasting Realised Volatility Using ARFIMA and HAR Models. In: Quantitative Finance. 2019 ; Vol. 19, No. 10. pp. 1627-1638.

Bibtex

@article{d3f3ab9304fb48eb9449257be98910e5,
title = "Forecasting Realised Volatility Using ARFIMA and HAR Models",
abstract = "Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010. We allow for different sectors, changing market conditions, variation in the sampling frequency and forecasting horizons. For the overall sample and using the 300 sec sampling frequency, the forecasting performance of both models is indistinguishable. However, differences arise under different market regimes, forecasting horizons and sampling frequencies. ARFIMA models are superior for the crisis and pre-crisis sub-samples. HAR forecasts are less sensitive to regime change and to longer forecasting horizons. Variations in forecasting performance could also be explained using differences in the levels of persistence underlying each model.",
keywords = "High-frequency data, Market conditions, Market sectors, Realised variance, HAR, ARFIMA",
author = "Marwan Izzeldin and Hassan, {M. Kabir} and Vasileios Pappas and Mike Tsionas",
year = "2019",
month = oct,
day = "1",
doi = "10.1080/14697688.2019.1600713",
language = "English",
volume = "19",
pages = "1627--1638",
journal = "Quantitative Finance",
issn = "1469-7688",
publisher = "Routledge",
number = "10",

}

RIS

TY - JOUR

T1 - Forecasting Realised Volatility Using ARFIMA and HAR Models

AU - Izzeldin, Marwan

AU - Hassan, M. Kabir

AU - Pappas, Vasileios

AU - Tsionas, Mike

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010. We allow for different sectors, changing market conditions, variation in the sampling frequency and forecasting horizons. For the overall sample and using the 300 sec sampling frequency, the forecasting performance of both models is indistinguishable. However, differences arise under different market regimes, forecasting horizons and sampling frequencies. ARFIMA models are superior for the crisis and pre-crisis sub-samples. HAR forecasts are less sensitive to regime change and to longer forecasting horizons. Variations in forecasting performance could also be explained using differences in the levels of persistence underlying each model.

AB - Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010. We allow for different sectors, changing market conditions, variation in the sampling frequency and forecasting horizons. For the overall sample and using the 300 sec sampling frequency, the forecasting performance of both models is indistinguishable. However, differences arise under different market regimes, forecasting horizons and sampling frequencies. ARFIMA models are superior for the crisis and pre-crisis sub-samples. HAR forecasts are less sensitive to regime change and to longer forecasting horizons. Variations in forecasting performance could also be explained using differences in the levels of persistence underlying each model.

KW - High-frequency data

KW - Market conditions

KW - Market sectors

KW - Realised variance

KW - HAR

KW - ARFIMA

U2 - 10.1080/14697688.2019.1600713

DO - 10.1080/14697688.2019.1600713

M3 - Journal article

VL - 19

SP - 1627

EP - 1638

JO - Quantitative Finance

JF - Quantitative Finance

SN - 1469-7688

IS - 10

ER -