Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach

Lu Yang and Shigeyuki Hamori

Energies, 2020, vol. 13, issue 14, 1-27

Abstract: We propose the use of wavelet-based semiparametric models for forecasting the value-at-risk (VaR) and expected shortfall (ES) in the crude oil market. We compared the forecast outcomes across different time scales for three semiparametric models, three nonparametric, distribution-based, generalized, autoregressive, conditional, heteroskedasticity (GARCH) models, and three rolling-window models. We found that the GARCH model estimated by the Fissler and Ziegel (FZ) zero loss minimization (GARCH-FZ) model performs the best at forecasting the VaR and ES in the short term, whereas the hybrid model performs the best for mid- and long-term time scales. Thus, long-term investors should consider the hybrid model and short-term investors should employ the GARCH-FZ model in their risk management processes. Overall, our proposed wavelet-based semiparametric models outperform the other models tested for all time scales and market conditions. As such, we suggest that these models are considered for the management of crude oil price risk and in the development of energy policy.

Keywords: crude oil market; value-at-risk; expected shortfall; wavelet transform; risk management; portfolio management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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