the evidences of asymmetries and long-range dependence in oil price volatilities is reassesed using a multivariate fractionally integrated exponential DCC model for three markets: Brent , Dubai and West-Texas intermediate. We estimate several MGARCH models, compare their in sample performances and their predictive ability with three approaches: the SPA tests, the MCS method and the value at risk approach. We extend the MCS method to include cases where the forecast error loss differential is strongly autocorrelated. In the overall, our results indicate significant gains from using models thet include long-range dependence and asymmetries against short memory models.
Forecasting oil proce volatilities with multivariate fractionally integrated asymmetric DCC models
MARCHESE, MALVINA;
2016-01-01
Abstract
the evidences of asymmetries and long-range dependence in oil price volatilities is reassesed using a multivariate fractionally integrated exponential DCC model for three markets: Brent , Dubai and West-Texas intermediate. We estimate several MGARCH models, compare their in sample performances and their predictive ability with three approaches: the SPA tests, the MCS method and the value at risk approach. We extend the MCS method to include cases where the forecast error loss differential is strongly autocorrelated. In the overall, our results indicate significant gains from using models thet include long-range dependence and asymmetries against short memory models.
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