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Forecasting crude-oil market volatility: Further evidence with jumps

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.
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https://hal-audencia.archives-ouvertes.fr/hal-01598141
Contributor : Amelie Charles <>
Submitted on : Monday, December 28, 2020 - 3:07:26 PM
Last modification on : Tuesday, January 5, 2021 - 3:27:47 AM

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Amélie Charles, Olivier Darné. Forecasting crude-oil market volatility: Further evidence with jumps. Energy Economics, Elsevier, 2017, ⟨10.1016/j.eneco.2017.09.002⟩. ⟨hal-01598141⟩

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