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Forecasting realised volatility: does the LASSO approach outperform HAR?

Forecasting realised volatility: does the LASSO approach outperform HAR?
Forecasting realised volatility: does the LASSO approach outperform HAR?
The HAR model dominates current volatility forecasting. This model implies a restricted lag approach, with three parameters accounting for an AR(22) structure. This paper uses the Lasso method, which selects a parsimonious lag structure, while allowing both a flexible lag structure and lags greater than 22. In-sample results suggest that while significance is largely found among the first 22 lags, consistent with the HAR model, there is evidence that longer lags contain information, as Lasso models provide an improved fit. Out-of-sample forecasts for daily, weekly and monthly volatility, evaluated using MSE, QLIKE, MCS and VaR measures, suggest that the ordered Lasso model provides the preferred forecasts using an AR(1 0 0) at the daily level and an AR(22) for the weekly and monthly horizons. The results support t
1042-4431
Ding, Yi
c2398405-127c-4dfc-a0d8-e4025d736610
Kambouroudis, Dimos
a1263623-1c79-48a4-b837-893bd31e6275
McMillan, David
fc95292e-bf0c-48b1-8fb9-85ee39a46d0d
Ding, Yi
c2398405-127c-4dfc-a0d8-e4025d736610
Kambouroudis, Dimos
a1263623-1c79-48a4-b837-893bd31e6275
McMillan, David
fc95292e-bf0c-48b1-8fb9-85ee39a46d0d

Ding, Yi, Kambouroudis, Dimos and McMillan, David (2021) Forecasting realised volatility: does the LASSO approach outperform HAR? Journal of International Financial Markets, Institutions and Money, 74. (doi:10.1016/j.intfin.2021.101386).

Record type: Article

Abstract

The HAR model dominates current volatility forecasting. This model implies a restricted lag approach, with three parameters accounting for an AR(22) structure. This paper uses the Lasso method, which selects a parsimonious lag structure, while allowing both a flexible lag structure and lags greater than 22. In-sample results suggest that while significance is largely found among the first 22 lags, consistent with the HAR model, there is evidence that longer lags contain information, as Lasso models provide an improved fit. Out-of-sample forecasts for daily, weekly and monthly volatility, evaluated using MSE, QLIKE, MCS and VaR measures, suggest that the ordered Lasso model provides the preferred forecasts using an AR(1 0 0) at the daily level and an AR(22) for the weekly and monthly horizons. The results support t

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SSRN-id3802466 - Accepted Manuscript
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Accepted/In Press date: 11 July 2021
e-pub ahead of print date: 16 July 2021
Published date: 22 September 2021

Identifiers

Local EPrints ID: 456823
URI: http://eprints.soton.ac.uk/id/eprint/456823
ISSN: 1042-4431
PURE UUID: 0efecc1e-1eb6-4eb5-8df3-6c4983ffb155
ORCID for Yi Ding: ORCID iD orcid.org/0000-0001-8778-3201

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Date deposited: 12 May 2022 16:37
Last modified: 13 May 2022 02:02

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Contributors

Author: Yi Ding ORCID iD
Author: Dimos Kambouroudis
Author: David McMillan

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