Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis

Agnolucci, 2009, Volatility in crude oil futures: a comparison of the predictive ability of GARCH and implied volatility models, Energy Econ., 31, 316, 10.1016/j.eneco.2008.11.001 Arouri, 2012, Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models, Energy Econ., 34, 283, 10.1016/j.eneco.2011.10.015 Artzner, 1999, Coherent measures of risk, Math. Financ., 9, 203, 10.1111/1467-9965.00068 Baillie, 2009, Modeling long memory and structural breaks in conditional variances: an adaptive FIGARCH approach, J. Econ. Dyn. Control., 33, 1577, 10.1016/j.jedc.2009.02.009 Baillie, 1996, Fractionally integrated generalized autoregressive conditional heteroskedasticity, J. Econ., 74, 3, 10.1016/S0304-4076(95)01749-6 Baillie, 2007, Long memory models for daily and high frequency commodity futures returns, J. Futur. Mark., 27, 643, 10.1002/fut.20267 Bollerslev, 1986, Generalized autoregressive conditional heteroscedasticity, J. Econ., 31, 307, 10.1016/0304-4076(86)90063-1 Bollerslev, 1996, Modeling and pricing long memory in stock market volatility., J. Econ., 73, 151, 10.1016/0304-4076(95)01736-4 Breidt, 1998, The detection and estimation of long memory in stochastic volatility, J. Econ., 83, 325, 10.1016/S0304-4076(97)00072-9 Cai, 1994, A Markov model of switching-regime ARCH, J. Bus. Econ. Stat., 12, 309 Charles, 2014, Volatility persistence in crude oil markets, Energy Policy, Elsevier, vol. 65(C), 729, 10.1016/j.enpol.2013.10.042 Charfeddine, 2009, New evidence on the nonstationary of the US and Canadian inflation series, ICFAI, J. Appl. Financ., 156 Charfeddine, 2011, A varieties of spurious long memory model, Int. J. Bus. Soc. Sci., 23, 52 Charfeddine, 2014, True of spurious long memory: further evidence from the futures energy markets, Energy Policy, 71, 76, 10.1016/j.enpol.2014.04.027 Charfeddine, 2013, The Tunisian Stock Market Index volatility: long memory vs switching regime, Emerg. Mark. Rev., 16, 170, 10.1016/j.ememar.2013.05.003 Charfeddine, 2010, Nonlinear models and the forward discount anomaly: an empirical investigation?, Int. J. Econ. Financ. Can. Cent. Sci. Educ., 21, 81 Charfeddine, 2011, Which is the best for the US inflation time series: a structural change model or a long memory process?, IUP J. Appl. Econ., 101, 5 Charfeddine, 2012, Breaks or long memory behaviour: an empirical investigation, Phys. A Stat. Mech. Appl., 391, 5712, 10.1016/j.physa.2012.06.036 Charfeddine, 2016, Time varying market efficiency of the GCC stock markets, Physica A, 444, 487, 10.1016/j.physa.2015.09.063 Chen, 1990, Random level-shift time series models, ARIMA approximations, and level-shift detection, J. Bus. Econ. Stat., 8, 83 Cheong, 2009, Modelling and forecasting crude oil markets using ARCH-type models, Energy Policy, 37, 2346, 10.1016/j.enpol.2009.02.026 Chkili, 2014, Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory, Energy Econ., 41, 1, 10.1016/j.eneco.2013.10.011 Cunado, 2010, Persistence in some energy futures markets, J. Futur. Mark., 30, 490 Diebold, 2001, Long memory and regime switching, J. Econ., 105, 131, 10.1016/S0304-4076(01)00073-2 Elder, 2008, Long memory in energy futures prices, Rev. Financ. Econ., 17, 146, 10.1016/j.rfe.2006.10.002 Engle, 1982, Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation, Econometrica, 50, 987, 10.2307/1912773 Engle, 1992, Implied ARCH models from option prices., J. Econ., 52, 5, 10.1016/0304-4076(92)90074-2 Engle, 1999, Stochastic permanent breaks, Rev. Econ. Stat., 81, 553, 10.1162/003465399558382 Engle, 2002 Fernandez, 2010, Commodity futures and market efficiency: a fractional integrated approach, Resour. Policy, 35, 276, 10.1016/j.resourpol.2010.07.003 Gallant, 1984, The Fourier flexible form American, J. Agric. Econ., 66, 204, 10.2307/1241043 Geweke, 1983, The estimation and application of long memory time series models, J. Time Ser. Anal., 4, 221, 10.1111/j.1467-9892.1983.tb00371.x Giot, 2003, Markets risk in commodity markets: a VaR approach, Energy Econ., 25, 435, 10.1016/S0140-9883(03)00052-5 Giriatis, 2003, Rescaled variance and related tests for long memory in volatility and levels., J. Econ., 112, 265, 10.1016/S0304-4076(02)00197-5 Gourieroux, 2001, Memory and infrequent breaks, Econ. Lett., 70, 29, 10.1016/S0165-1765(00)00346-3 Granger, 2004, Occasional structural breaks and long memory with application to the S&P500 absolute stock returns, J. Empir. Financ., 11, 399, 10.1016/j.jempfin.2003.03.001 Granger, 1980, An introduction to long memory time series and fractional differencing, J. Time Ser. Anal., 1, 15, 10.1111/j.1467-9892.1980.tb00297.x Granger, 1999, A simple nonlinear time series model with misleading linearproperties, Econ. Lett., 62, 161, 10.1016/S0165-1765(98)00228-6 Gray, 1996, Modeling the conditional distribution of interest rates as a regime-switching process, J. Financ. Econ., 42, 27, 10.1016/0304-405X(96)00875-6 Hamilton, 1989, A new approach to the economic analysis of non stationarity time series and the business cycle, Econometrica, 57, 357, 10.2307/1912559 Hamilton, 2009, Causes and consequences of the oil shock of 2007–08, 215 Hamilton, 2013, Historical oil shocks, 239 Hamilton, 1994, Autoregressive conditional heteroscedasticity and changes in regimes, J. Econ., 64, 307, 10.1016/0304-4076(94)90067-1 Haas, 2004, A New Approach to Markov-Switching GARCH Models, J. Financ. Econ., 2, 493 Hosking, 1981, Fractional differencing, Biometrica, 68, 165, 10.1093/biomet/68.1.165 Jensen, 1999, An approximate wavelet MLE of short- and long-memory parameters, Stud. Nonlinear Dyn. Econ., 3, 239 Kang, 2013, Modeling and forecasting the volatility of petroleum futures prices, Energy Econ., 36, 354, 10.1016/j.eneco.2012.09.010 Kang, 2009, Forecasting volatility of crude oil markets, Energy Econ., 31, 119, 10.1016/j.eneco.2008.09.006 Khediri, 2015, Evolving efficiency of spot and futures energy markets: a rolling sample approach, J. Behav. Exp. Financ., 6, 67, 10.1016/j.jbef.2015.03.006 Kim, 1999 Klassen, 2002, Improving GARCH volatility forecasts, Empir. Econ., 27, 363, 10.1007/s001810100100 Kupiec, 1995, Technique for verifying the accuracy of risk measurement models, J. Deriv., 2, 173 Lamoureux, 1990, Persistence in variance, structural change and the GARCH model, J. Bus. Econ. Stat., 8, 225 Lo, 1991, Long term memory in stock market prices, Econometrica, 59, 1279, 10.2307/2938368 Marcucci, 2005, Forecasting stock market volatility with regime-switching GARCH models, Stud. Nonlinear Dyn. Econ., 94, 1 Mrabet, 2014, Co-movement on stock market volatility: the BRIC and USA economies Narayan, 2011, Are shocks to commodity prices persistent?, Appl. Energy, 88, 409, 10.1016/j.apenergy.2010.07.032 Narayan, 2015, A unit root model for trending time-series energy variables, Energy Econ., 50, 391, 10.1016/j.eneco.2014.11.021 Narayan, 2013, New evidence on oil price and stock returns, J. Bank. Financ., 35, 3253, 10.1016/j.jbankfin.2011.05.010 Narayan, 2013, An analysis of commodity markets: what gain for investors?, J. Bank. Financ., 37, 3878, 10.1016/j.jbankfin.2013.07.009 Narayan, 2015, Do momentum-based trading strategies work in the commodity futures markets?, J. Futur. Mark., 35, 868, 10.1002/fut.21685 Narayan, 2015, Does data frequency matter for the impact of forward premium on spot exchange rate?, Int. Rev. Financ. Anal., 39, 45, 10.1016/j.irfa.2015.01.011 Nelson, 1991, Conditional heteroskedasticity in asset returns: a new approach, Econometrica, 59, 347, 10.2307/2938260 Ozdemir, 2013, Persistence in crude oil spot and futures prices, Energy, 59, 29, 10.1016/j.energy.2013.06.008 Peng, 1994, Mosaic organization of DNA nucleotides, Phys. Rev. E, 49, 1685, 10.1103/PhysRevE.49.1685 Phan, 2015, Oil price and stock returns of consumers and producers of crude oil, J. Int. Financ. Mark. Inst. Money, 34, 245, 10.1016/j.intfin.2014.11.010 Phan, 2015, Stock return forecasting: some new evidence, Int. Rev. Financ. Anal., 40, 38, 10.1016/j.irfa.2015.05.002 Robinson, 1998, Long and short memory conditional heteroscedasticity in estimating the memory parameter of levels Schwert, 1990, Stock volatility and the crash of 87, Rev. Financ. Stud., 3, 77, 10.1093/rfs/3.1.77 Shimotsu, 2010, Exact local whittle estimation of fractional integration with unknown mean and time trend, Econ. Theory, 26, 501, 10.1017/S0266466609100075 Shimotsu, 2005, Exact local Whittle estimation of fractional integration, Ann. Stat., 334, 1890, 10.1214/009053605000000309 Susmel, 2000, Switching volatility in international equity markets, Int. J. Financ. Econ., 5, 265, 10.1002/1099-1158(200010)5:4<265::AID-IJFE132>3.0.CO;2-H Tang, 2006, Long memory in stock index futures markets: a value at risk approach, Physica A, 366, 437, 10.1016/j.physa.2005.10.017 Wang, 2013, Oil Price Effects on Personal Consumption Expenditures, Energy Econ., 36, 198, 10.1016/j.eneco.2012.08.007 Wang, 2010, Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?, Energy Econ., 34, 2167, 10.1016/j.eneco.2012.03.010 Wang, 2012, Long memory in energy futures markets: further evidence, Resour. Policy, 37, 261, 10.1016/j.resourpol.2012.05.002 Wang, 2011, Can GARCH-class models capture long memory in WTI crude oil markets?, Econ. Model., 28, 921, 10.1016/j.econmod.2010.11.002 Wei, 2010, Forecasting crude oil market volatility: further evidence using GARCH-class models, Energy Econ., 32, 1477, 10.1016/j.eneco.2010.07.009