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Forecasting crude-oil market volatility: Further evidence with jumps

Amélie Charles and Olivier Darné

Energy Economics, 2017, vol. 67, issue C, 508-519

Abstract: This paper analyzes volatility models and their forecasting abilities in the presence of jumps in two crude-oil markets - Brent and West Texas Intermediate (WTI) - between January 6th 1992 and December 31st 2014. We compare a number of GARCH-type models that capture short memory as well as asymmetry (GARCH, GJR-GARCH and EGARCH), estimated on raw returns, to three competing approaches that deal with the presence of jumps: GARCH-type models estimated on jump-filtered returns, and two new classes of volatility models, called Generalized Autoregressive Score (GAS) and Markov-switching multifractal (MSM) models, estimated using raw returns. The forecasting performance of these volatility models is evaluated using the model confidence set approach, which allows us to identify a subset of models that outperform all the other competing models. We find that asymmetric models estimated on filtered returns provide better out-of-sample forecasts than do GARCH-, GAS-type and MSM models estimated on raw return series for Brent and WTI returns.

Keywords: Crude oil returns; Volatility forecasting; Jumps (search for similar items in EconPapers)
JEL-codes: C22 C53 F47 G17 Q47 (search for similar items in EconPapers)
Date: 2017
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