Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/32946
Appears in Collections:Accounting and Finance Journal Articles
Peer Review Status: Refereed
Title: Forecasting Realised Volatility: Does the LASSO approach outperform HAR?
Author(s): Ding, Yi
Kambouroudis, Dimos
McMillan, David
Contact Email: david.mcmillan@stir.ac.uk
Keywords: Volatility Forecasting
HAR
Lasso
VaR
Date Deposited: 16-Jul-2021
Citation: Ding Y, Kambouroudis D & McMillan D (2021) Forecasting Realised Volatility: Does the LASSO approach outperform HAR?. Journal of International Financial Markets, Institutions and Money. https://doi.org/10.1016/j.intfin.2021.101386
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(100) at the daily level and an AR(22) for the weekly and monthly horizons. The results support the view that a more flexible lag structure is preferred over the HAR approach.
DOI Link: 10.1016/j.intfin.2021.101386
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Notes: Output Status: Forthcoming/Available Online

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