کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5063596 | 1476697 | 2017 | 12 صفحه PDF | دانلود رایگان |
- We analyze volatility models and their forecasting abilities with the presence of jumps for Brent and WTI crude oil markets.
- We compare several GARCH-type models estimated from raw and filtered returns.
- We also use GAS and MSM models estimated from raw returns.
- Asymmetric models estimated on filtered returns provide the best out-of-sample forecasts.
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.
Journal: Energy Economics - Volume 67, September 2017, Pages 508-519