Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data

Thomas Lux, Mawuli Segnon and Rangan Gupta

Energy Economics, 2016, vol. 56, issue C, 117-133

Abstract: This paper adopts the Markov-switching multifractal (MSM) model and a battery of generalized autoregressive conditional heteroscedasticity (GARCH)-type models to model and forecast oil price volatility. Extending previous work by Wei et al., (2010) and Wang et al., (2016), we evaluate the forecasting performance of all these models via a superior predictive ability (SPA) test. We go beyond previous research by (i) considering oil price volatility in the nineteenth century along with recent data, (ii) applying different types of MSM models and (iii) considering value-at-risk predictions besides our forecasting of volatility. Confirming its successful performance in other studies, the new MSM model comes out as the model that most often across forecasting horizons and subsamples cannot be outperformed by other models. This superiority also applies to forecasting of value-at-risk.

Keywords: Crude oil prices; GARCH; Multifractal processes; Superior predictive ability test; Encompassing test; VaR (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (80)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:56:y:2016:i:c:p:117-133

DOI: 10.1016/j.eneco.2016.03.008

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