Degiannakis, Stavros and Filis, George
(2019):
*Oil price volatility forecasts: What do investors need to know?*

PDF
MPRA_paper_94445.pdf Download (1MB) |

## Abstract

Contrary to the current practice that mainly considers stand-alone statistical loss functions, the aim of the paper is to assess oil price volatility forecasts based on objective-based evaluation criteria, given that different forecasting models may exhibit superior performance at different applications. To do so, we forecast implied and several intraday volatilities and we evaluate them based on financial decisions for which these forecasts are used. In this study we confine our interest on the use of such forecasts from financial investors. More specifically, we consider four well established trading strategies, which are based on volatility forecasts, namely (i) trading the implied volatility based on the implied volatility forecasts, (ii) trading implied volatility based on intraday volatility forecasts, (iii) trading straddles in the United States Oil Fund ETF and finally (iv) trading the United States Oil Fund ETF based on implied and intraday volatility forecasts. We evaluate the after-cost profitability of each forecasting model for 1-day up to 66-days ahead. Our results convincingly show that our forecasting framework is economically useful, since different models provide superior after-cost profits depending on the economic use of the volatility forecasts. Should investors evaluate the forecasting models based on statistical loss functions, then their financial decisions would be sub-optimal. Thus, we maintain that volatility forecasts should be evaluated based on their economic use, rather than statistical loss functions. Several robustness tests confirm these findings.

Item Type: | MPRA Paper |
---|---|

Original Title: | Oil price volatility forecasts: What do investors need to know? |

Language: | English |

Keywords: | Volatility forecasting, implied volatility, intraday volatility, WTI crude oil futures, objective-based evaluation criteria. |

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 > G11 - Portfolio Choice ; Investment Decisions G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting |

Item ID: | 94445 |

Depositing User: | George Filis |

Date Deposited: | 12 Jun 2019 11:33 |

Last Modified: | 12 Jun 2019 11:34 |

References: | Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 885-905. Andersen, T., Bollerslev, T. & Lange, S. (1999). Forecasting Financial Market Volatility: Sample Frequency vis-à-vis Forecast Horizon. Journal of Empirical Finance, 6, 457-477. Andersen, T., Bollerslev T. & Cai, J. (2000). Intraday and Interday Volatility in the Japanese Stock Market. Journal of International Financial Markets, Institutions and Money, 10, 107-130. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (2001b). The Distribution of Realized Exchange Rate Volatility. Journal of the American Statistical Association, 96, 42-55. Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71, 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. Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007). Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. The Review of Economics and Statistics, 89(4), 701-720. Andersen, T., Dobrev, D. & Schaumburg, E. (2012). Jump-Robust Volatility Estimation Using Nearest Neighbor Truncation. Journal of Econometrics, 169(1), 75-93. Angelidis, T., & Degiannakis, S. (2008). Volatility forecasting: Intra-day versus inter-day models. Journal of International Financial Markets, Institutions and Money, 18(5), 449-465. Barndorff-Nielsen, O. & Shephard, N. (2004). Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2(1), 1-37. Barndorff-Nielsen, O. & Shephard, N. (2006). Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation. Journal of Financial Econometrics, 4, 1-30. Barndorff-Nielsen, O., Kinnebrock, S., & Shephard, N. (2010). Measuring downside risk – Realised semivariance. In: T. Bollerslev, J. Russell and M. Watson (eds) Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle. Oxford University Press. Busch, T., Christensen, B. J., and Nielsen, M. Ø. (2011). The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. Journal of Econometrics, 160, 48–57. Buyuksahin, B., & Robe, M. A. (2014). Speculators, commodities and cross-market linkages. Journal of International Money and Finance, 42, 38-70. Chaboud, A., Chiquoine, B., Hjalmarsson, E. & Loretan, M. (2010). Frequency of Observation and the Estimation of Integrated Volatility in Deep and Liquid Financial Markets. Journal of Empirical Finance, 17(2), 212-240. 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. Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7, 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. Degiannakis, S., & Filis, G. (2018). Forecasting oil prices: High-frequency financial data are indeed useful. Energy Economics, 76, 388-402. Degiannakis, S., Filis, G., & Hassani, H. (2018). Forecasting global stock market implied volatility indices. Journal of Empirical Finance, 46, 111-129. Engle, R.F., Hong, C.H., Kane, A., & Noh, J. (1993). Arbitrage valuation of variance forecasts with simulated options. Advances in Futures and Options Research, 6, 393–415. Fernandes, M., Medeiros, M. C., & Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking & Finance, 40, 1-10. Gargano, A., Pettenuzzo, D., & Timmermann, A. (2017). Bond return predictability: Economic value and links to the macroeconomy. Management Science, 65(2), 508-540. Gong, X., & Lin, B. (2018). The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market. Energy Economics, 74, 370-386. 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., Molnár, P., & Westgaard, S. (2014). Forecasting volatility of the US oil market. Journal of Banking & Finance, 47, 1-14. Huang, X. & Tauchen, G. (2005). The relative contribution of jumps to total price variance. Journal of Financial Econometrics, 3, 456-499. Jung, Y. C. (2016). A portfolio insurance strategy for volatility index (VIX) futures. The Quarterly Review of Economics and Finance, 60, 189-200. Kang, S. H., Kang, S. M., & Yoon, S. M. (2009). Forecasting volatility of crude oil markets. Energy Economics, 31(1), 119-125. Le Pen, Y., & Sévi, B. (2017). Futures trading and the excess co-movement of commodity prices. Review of Finance, 22(1), 381-418. Liu, J., Ma, F., Yang, K., & Zhang, Y. (2018). Forecasting the oil futures price volatility: Large jumps and small jumps. Energy Economics, 72, 321-330. Ma, F., Wahab, M. I. M., Huang, D., & Xu, W. (2017). Forecasting the realized volatility of the oil futures market: A regime switching approach. Energy Economics, 67, 136-145. Ma, F., Zhang, Y., Huang, D., & Lai, X. (2018). Forecasting oil futures price volatility: New evidence from realized range-based volatility. Energy Economics, 75, 400-409. McAleer, M., & Medeiros, M. C. (2008). Realized volatility: A review. Econometric Reviews, 27(1-3), 10-45. 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. Patton, A.J. & Sheppard, K. (2015). Good volatility, bad volatility: signed jumps and the persistence of volatility. The Review of Economics and Statistics, 97(3): 683-697. Prokopczuk, M., Symeonidis, L., & Wese Simen, C. (2015). Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets. Journal of Futures Markets, 1-35. Sadorsky, P. (2006). Modeling and forecasting petroleum futures volatility. Energy Economics, 28, 467–488. Sadorsky, P., & McKenzie, M. D. (2008). Power transformation models and volatility forecasting. Journal of Forecasting, 27, 587–606. Sévi, B. (2014). Forecasting the volatility of crude oil futures using intraday data. European Journal of Operational Research, 235(3), 643-659. Tang, K., & Xiong, W. (2012). Index investment and the financialization of commodities. Financial Analysts Journal, 68(5), 54-74. 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. Theodosiou, M. & Zikes, P. (2011). A comprehensive comparison of alternative tests for jumps in asset prices. Working Paper, Central Bank of Cyprus, 2. Tseng, T.C., Chung, H., & Huang, C. S. (2009). Modeling jump and continuous components in the volatility of oil futures. Studies in Nonlinear Dynamics & Econometrics, 13(3). Yang, Z., & Zhou, Y. (2017). Quantitative easing and volatility spillovers across countries and asset classes. Management Science, 63(2), 333-354. |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/94445 |