Forecasting Realized Volatility of Crude Oil Futures Prices based on Variable Selection Approaches

Item Type Monograph (Working Paper)
Abstract We augment the HAR model with additional information channels to forecast realized volatility of WTI futures prices. These channels include stock markets, sentiment indices, commodity and FX markets, and text-based Google indices. We then apply four differing machine learning techniques to identify the most suitable endo- and exogenous factors which improve baseline model forecasts. We show that machine learning generated forecasts provide better forecasting quality and that portfolios which are constructed with these forecasts outperform their competing models. We find LASSO and SSVS to provide outperforming forecasts and portfolio weights. Analyzing the selection process, we show that variable choices vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels.
Authors Luo, Jiawen; Ji, Qiang; Klein, Tony & Walther, Thomas
Language English
Subjects business studies
economics
finance
HSG Classification contribution to scientific community
HSG Profile Area SOF - System-wide Risk in the Financial System
Date 2020
Official URL https://papers.ssrn.com/sol3/papers.cfm?abstract_i...
Depositing User Prof. Dr. Thomas Walther
Date Deposited 01 Oct 2020 14:45
Last Modified 20 Jul 2022 17:43
URI: https://www.alexandria.unisg.ch/publications/261105

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Luo, Jiawen; Ji, Qiang; Klein, Tony & Walther, Thomas: Forecasting Realized Volatility of Crude Oil Futures Prices based on Variable Selection Approaches. , 2020,

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https://www.alexandria.unisg.ch/id/eprint/261105
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