Modeling and forecasting crude oil price volatility: Evidence from historical and recent data
Mawuli Segnon and
Rangan Gupta ()
No 31, FinMaP-Working Papers from Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents
This paper uses the Markov-switching multifractal (MSM) model and generalized autoregressive conditional heteroscedasticity (GARCH)-type models to forecast oil price volatility over the time periods from January 02, 1875 to December 31, 1895 and from January 03, 1977 to March 24, 2014. Based on six different loss functions and by means of the superior predictive ability (SPA) test, we evaluate and compare their forecasting performance at short and long horizons. The empirical results indicate that none of our volatility models can uniformly outperform other models across all six different loss functions. However, the new MSM model comes out as the model that most often across forecasting horizons and subsamples cannot be outperformed by other models, with long memory GARCH-type models coming out second best.
Keywords: Crude oil prices; GARCH; Multifractal processes; SPA test (search for similar items in EconPapers)
JEL-codes: C52 C53 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ene, nep-for, nep-his and nep-rmg
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Working Paper: Modeling and Forecasting Crude Oil Price Volatility: Evidence from Historical and Recent Data (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:fmpwps:31
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