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• W2235390017 abstract "The aim of this paper is to propose an empirical strategy that allows the discrimination between true and spurious long memory behaviors. That strategy is based on the comparison between the estimated long memory parameter before and after filtering out the breaks. To date the breaks, we use the probability smoothing of the Markov Switching GARCH model of Haas et al. (2004). Application of this strategy to the crude oil, heating oil, RBOB regular gasoline and the propane futures energy with the one, two, three and four months maturities show strong evidence for the presence of long range dependence in all futures energy prices volatility1 time series. This result of long range dependence in the volatility is confirmed by the superiority of the FIGARCH and FIEGARCH models compared with the Markov switching GARCH models in terms of out-of-sample forecasting and value at risk (VaR) performances. Moreover, we show that the proposed empirical strategy is robust to different data frequency. Practical implications of the results for market participants are proposed and discussed." @default.
• W2235390017 created "2016-06-24" @default.
• W2235390017 creator A5067982580 @default.
• W2235390017 date "2016-02-01" @default.
• W2235390017 modified "2023-10-16" @default.
• W2235390017 title "Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis" @default.
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• W2235390017 doi "https://doi.org/10.1016/j.econmod.2015.12.009" @default.
• W2235390017 hasPublicationYear "2016" @default.
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