Chatziantoniou, Ioannis and Degiannakis, Stavros and Filis, George
(2019):
*Futures-based forecasts: How useful are they for oil price volatility forecasting?*
Published in: Energy Economics
No. 81
(2019): pp. 639-649.

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## Abstract

Oil price volatility forecasts have recently attracted the attention of many studies in the energy finance field. The literature mainly concentrates its attention on the use of daily data, using GARCH-type models. It is only recently that efforts to use more informative intraday data to forecast oil price realized volatility have been made. Despite all these previous efforts, no study has examined the usefulness of futures-based models for oil price realized volatility forecasting, although the use of such models is extensive for oil price predictions. This study fills this void and shows that futures-based forecasts based on intra-day data provide informative forecasts for horizons that span between 1-day and 66-days ahead. More importantly, these results hold true even during turbulent times for the oil market, such as the Global Financial Crisis of 2007-09 and the oil collapse period of 2014-15.

Item Type: | MPRA Paper |
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Original Title: | Futures-based forecasts: How useful are they for oil price volatility forecasting? |

English Title: | Futures-based forecasts: How useful are they for oil price volatility forecasting? |

Language: | English |

Keywords: | Brent crude oil, realized volatility, forecasting, futures-based forecasts |

Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods G - Financial Economics > G1 - General Financial Markets > G13 - Contingent Pricing ; Futures Pricing Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting |

Item ID: | 96446 |

Depositing User: | Dr. Stavros Degiannakis |

Date Deposited: | 16 Oct 2019 10:48 |

Last Modified: | 16 Oct 2019 10:48 |

References: | Agnolucci, P., 2009. Volatility in crude oil futures: a comparison of the predictive ability of GARCH and implied volatility models. Energy Economics 31 (2), 316-321. Alquist, R., Kilian, L., 2010. What do we learn from the price of crude oil futures? Journal of Applied Econometrics 25 (4), 539-573. Andersen, T. G., Bollerslev, T., 1998. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 885-905. Andersen, T. G., Bollerslev, T., Christo_ersen, P. F., Diebold, F. X., 2006. Volatility and correlation forecasting. Handbook of Economic Forecasting 1, 777-878. Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P., 2001. The distribution of realized exchange rate volatility. Journal of the American Statistical Association 96 (453), 42-55. Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P., 2003. Modeling and forecasting realized volatility. Econometrica 71 (2), 579-625. Andersen, T. G., Bollerslev, T., Meddahi, N., 2005. Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities. Econometrica 73 (1), 279-296. Arouri, M. E. H., Lahiani, A., L_evy, A., Nguyen, D. K., 2012. Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models. Energy Economics 34 (1), 283-293. Bai, J., Perron, P., 1998. Estimating and testing linear models with multiple structural changes. Econometrica, 47-78. Bai, J., Perron, P., 2003. Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18 (1), 1-22. Basher, S. A., Sadorsky, P., 2016. Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics 54, 235-247. Bauer, G. H., Vorkink, K., 2011. Forecasting multivariate realized stock market volatility. Journal of Econometrics 160 (1), 93-101. Busch, T., Christensen, B. J., Nielsen, M. _., 2011. The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. Journal of Econometrics 160 (1), 48-57. Cabedo, J. D., Moya, I., 2003. Estimating oil price ‘value at risk’ using the historical simulation approach. Energy Economics 25 (3), 239-253. Charles, A., Darne, O., 2017. Forecasting crude-oil market volatility: Further evidence with jumps. Energy Economics 67, 508-519. Chinn, M. D., Coibion, O., 2014. The predictive content of commodity futures. Journal of Futures Markets 34 (7), 607-636. Chiriac, R., Voev, V., 2011. Modelling and forecasting multivariate realized volatility. Journal of Applied Econometrics 26 (6), 922-947. Chkili, W., Hammoudeh, S., Nguyen, D. K., 2014. Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory. Energy Economics 41, 1-18. Coppola, A., 2008. Forecasting oil price movements: Exploiting the information in the futures market. Journal of Futures Markets 28 (1), 34-56. Corsi, F., 2009. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7 (2), 174-196. Degiannakis, S., Filis, G., 2017. Forecasting oil price realized volatility using information channels from other asset classes. Journal of International Money and Finance 76, 28-49. Efimova, O., Serletis, A., 2014. Energy markets volatility modelling using GARCH. Energy Economics 43, 264-273. Elder, J., Serletis, A., 2010. Oil price uncertainty. Journal of Money, Credit and Banking 42 (6), 1137-1159. Giot, P., Laurent, S., 2003. Market risk in commodity markets: a var approach. Energy Economics 25 (5), 435-457. Griffin, J. E., Oomen, R. C., 2008. Sampling returns for realized variance calculations: tick time or transaction time? Econometric Reviews 27 (1-3), 230-253. Hansen, P. R., Lunde, A., 2005. A realized variance for the whole day based on intermittent high-frequency data. Journal of Financial Econometrics 3 (4), 525-554. Hansen, P. R., Lunde, A., Nason, J. M., 2011. The model confidence set. Econometrica 79 (2), 453-497. Haugom, E., Langeland, H., Moln_ar, P., Westgaard, S., 2014. Forecasting volatility of the us oil market. Journal of Banking & Finance 47, 1-14. Kang, S. H., Kang, S.-M., Yoon, S.-M., 2009. Forecasting volatility of crude oil markets. Energy Economics 31 (1), 119-125. Kang, S. H., Yoon, S.-M., 2013. Modeling and forecasting the volatility of petroleum futures prices. Energy Economics 36, 354-362. Le Pen, Y., Sevi, B., 2017. Futures trading and the excess co-movement of commodity prices. Review of Finance 22 (1), 381-418. McAleer, M., Medeiros, M. C., 2008. Realized volatility: A review. Econometric Reviews 27 (1-3), 10-45. Murat, A., Tokat, E., 2009. Forecasting oil price movements with crack spread futures. Energy Economics 31 (1), 85-90. Nomikos, N. K., Pouliasis, P. K., 2011. Forecasting petroleum futures markets volatility: The role of regimes and market conditions. Energy Economics 33 (2), 321-337. Oomen, R. C. A., 2006. Properties of realized variance under alternative sampling schemes. Journal of Business & Economic Statistics 24 (2), 219-237. Patton, A. J., 2011. Data-based ranking of realised volatility estimators. Journal of Econometrics 161 (2), 284-303. Pesaran, M. H., Timmermann, A., 2007. Selection of estimation window in the presence of breaks. Journal of Econometrics 137 (1), 134-161. Pesaran, M. H., Timmermann, A., 2009. Testing dependence among serially correlated multicategory variables. Journal of the American Statistical Association 104 (485), 325-337. Prokopczuk, M., Symeonidis, L., Wese Simen, C., 2016. Do jumps matter for volatility forecasting? evidence from energy markets. Journal of Futures Markets 36 (8), 758-792. Sadorsky, P., 2006. Modeling and forecasting petroleum futures volatility. Energy Economics 28 (4), 467-488. Sadorsky, P., McKenzie, M. D., 2008. Power transformation models and volatility forecasting. Journal of Forecasting 27 (7), 587-606. Sanso, A., Arago, V., Carrion, J. L., et al., 2004. Testing for changes in the unconditional variance of financial time series. Revista de Economia Financiera 4 (1), 32-53. Sevi, B., 2014. Forecasting the volatility of crude oil futures using intraday data. European Journal of Operational Research 235 (3), 643-659. Silvennoinen, A., Thorp, S., 2013. Financialization, crisis and commodity correlation dynamics. Journal of International Financial Markets, Institutions and Money 24, 42-65. Tay, A., Ting, C., Tse, Y. K., Warachka, M., 2009. Using high-frequency transaction data to estimate the probability of informed trading. Journal of Financial Econometrics 7 (3), 288-311. Vivian, A., Wohar, M. E., 2012. Commodity volatility breaks. Journal of International Financial Markets, Institutions and Money 22 (2), 395-422. Wei, Y., Wang, Y., Huang, D., 2010. Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics 32 (6), 1477-1484. Xu, B., Ouenniche, J., 2012. A data envelopment analysis-based framework for the relative performance evaluation of competing crude oil prices' volatility forecasting models. Energy Economics 34 (2), 576-583. Zhang, Y.-J., Zhang, J.-L., 2017. Volatility forecasting of crude oil market: A new hybrid method. Journal of Forecasting 37(8), 781-789. |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/96446 |