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 | 01 Oct 2020 14:45 |
URI: | https://www.alexandria.unisg.ch/publications/261105 |
DownloadFull text not available from this repository.CitationLuo, Jiawen; Ji, Qiang; Klein, Tony & Walther, Thomas: Forecasting Realized Volatility of Crude Oil Futures Prices based on Variable Selection Approaches. , 2020, Statisticshttps://www.alexandria.unisg.ch/id/eprint/261105
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