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Forecasting energy price volatilities and comovements with fractionally integrated MGARCH models
Last modified: 2018-05-18
Abstract
We investigate the use of fractionally integrated MGARCH models from a forecasting and a risk management perspective for energy prices. Our in-sample results show significant evidence of long memory decay in energy price returns volatilities, of leverage effects and of time-varying autocorrelations. The forecasting performance of the models is assessed by the SPA test, the Model Confidence Set and the Value at Risk.
References
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2. Bauwens, L., Braione, M., Storti, G., 2016. Forecasting comparison of long term component dynamic models for realized covariance matrices. Annals of Economics and Statistics/ Annales d’ ´ Economie et de Statistique, (123/124), 103-134.
3. Chang, C. L., McAleer, M., Tansuchat, R., 2010. Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Econ. 32, 1445–1455.
4. Hansen, P.R., 2005. A test for superior predictive ability. Journal of Business Economics and Statistics 23, 365–380.
5. Giot, P., Laurent, S., 2003. Value-at-risk for long and short trading positions. Journal of Applied Econometrics 18, 641-663.
6. Silvennoinen, A., Ter¨asvirta, T. , 2009. Multivariate GARCH models. Handbook of financial time series, 201-229.
7. Wang, Y., Wu, C, 2010. Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?. Energy Economics 34, 2167-2181.
2. Bauwens, L., Braione, M., Storti, G., 2016. Forecasting comparison of long term component dynamic models for realized covariance matrices. Annals of Economics and Statistics/ Annales d’ ´ Economie et de Statistique, (123/124), 103-134.
3. Chang, C. L., McAleer, M., Tansuchat, R., 2010. Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Econ. 32, 1445–1455.
4. Hansen, P.R., 2005. A test for superior predictive ability. Journal of Business Economics and Statistics 23, 365–380.
5. Giot, P., Laurent, S., 2003. Value-at-risk for long and short trading positions. Journal of Applied Econometrics 18, 641-663.
6. Silvennoinen, A., Ter¨asvirta, T. , 2009. Multivariate GARCH models. Handbook of financial time series, 201-229.
7. Wang, Y., Wu, C, 2010. Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?. Energy Economics 34, 2167-2181.
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