Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis
Author | Charfeddine, Lanouar |
Available date | 2021-04-29T08:10:44Z |
Publication Date | 2016 |
Publication Name | Economic Modelling |
Resource | Scopus |
ISSN | 2649993 |
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 volatility. 11As proxy of volatility, we use the squared returns. Others proxies are used in the empirical studies such as the absolute value of returns. Our empirical studies show there are strong differences between these two proxies. 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. |
Language | en |
Publisher | Elsevier B.V. |
Subject | Energy futures time series Fractional GARCH-class of models Long range dependence Markov switching GARCH model Out-of-sample forecasting and VaR |
Type | Article |
Pagination | 354-374 |
Volume Number | 53 |
Check access options
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
-
Finance & Economics [394 items ]