Delis, Panagiotis and Degiannakis, Stavros and Giannopoulos, Kostantinos
(2021):
*What should be taken into consideration when forecasting oil implied volatility index?*

Preview |
PDF
MPRA_paper_110831.pdf Download (1MB) | Preview |

## Abstract

Crude oil is considered a key commodity in all the economies around the world. This study forecasts the oil volatility index (OVX), which is the market’s expectation of future oil volatility, by incorporating information from other asset classes. The literature does not extensively test the long memory of the targeted volatility. Thus, we estimate the Hurst exponent implementing a rolling window rescaled analysis. We provide evidence for a strong long memory in the implied volatility (IV) indices which justifies the use of the HAR model in obtaining multiple days ahead OVX forecasts. We also define a dynamic model averaging (DMA) structure in the HAR model in order to allow for IV indices from other asset classes to be applicable at different time periods. The implementation of the DMA-HAR models informs forecasters to focus on the major stock market IV indices, and more specifically on the DJIA Volatility Index. Our results lead us to the conclusion that accurate OVX forecasts are obtained for short- and mid-run forecasting horizons. The evaluation framework is not limited to statistical loss functions but also embodies an options straddle trading strategy.

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

Original Title: | What should be taken into consideration when forecasting oil implied volatility index? |

Language: | English |

Keywords: | crude oil, implied volatility, HAR modelling, trading strategies, dynamic model averaging, long memory |

Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics 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: | 110831 |

Depositing User: | Mr Panagiotis Delis |

Date Deposited: | 01 Dec 2021 09:29 |

Last Modified: | 01 Dec 2021 09:29 |

References: | Andersen, T.G., Bollerslev, T., 1998. Answering the Skeptics : Yes , Standard Volatility Models do Provide Accurate Forecasts. Int. Econ. Rev. 39, 885–905. Andrada-Félix, J., Fernández-Rodríguez, F., Fuertes, A.M., 2016. Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay? Int. J. Forecast. 32, 695–715. Angelidis, T., Degiannakis, S., 2008. Volatility forecasting: Intra-day versus inter-day models. J. Int. Financ.Mark. Inst.Money. 18, 449–465. Anis, A., Lloyd, E., 1976. The expected value of the adjusted rescaled hurst range of independent normal summands. Biometrika 63, 111–116. Awartani, B., Aktham, M., Cherif, G., 2016. The connectedness between crude oil and financial markets: Evidence from implied volatility indices. J. Commod.Mark. 4, 56–69. Bašta, M., Molnár, P., 2018. Oil market volatility and stock market volatility. Financ. Res. Lett. 26, 204–214. Blair, B.J., Poon, S.H., Taylor, S.J., 2001. Forecasting S&P 100 volatility: The incremental information content of implied volatilities and high-frequency index returns. J. Econom. 105, 5–26. Buncic, D., Gisler, K.I., 2016. Global equity market volatility spillovers: A broader role for the United States. Int. J. Forecast. 32, 1317–1339. Busch, T., Christensen, B.J., Nielsen, M.Ø., 2011. The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. J. Econom. 160, 48–57. Chatziantoniou, I., Degiannakis, S., Delis, P., Filis, G., 2020. Forecasting oil price volatility using spillover effects from uncertainty indices. Financ. Res. Lett. , 101885. Christensen, B.J., Prabhala, N.R., 1998. The relation between implied and realized volatility. J. financ. econ. 50, 125–150. Corsi, F., 2009. A simple approximate long-memory model of realized volatility. J. Financ. Econom. 7, 174–196. 20 Corsi, F., Renò, R., 2012. Discrete-Time Volatility ForecastingWith Persistent Leverage Effect and the LinkWith Continuous-Time VolatilityModeling. J. Bus. Econ. Stat. 30, 368–380. Degiannakis, S., 2008. Forecasting VIX. J.Money, Invest. Bank. 4, 5–19. Degiannakis, S., Filis, G., 2017. Forecasting oil price realized volatility using information channels from other asset classes. J. Int.Money Financ. 76, 28–49. Degiannakis, S., Filis, G., Hassani, H., 2018. Forecasting global stock market implied volatility indices. J. Empir. Financ. 46, 111–129. Delis, P., Degiannakis, S.A., Filis, G., 2020. What Matters When Developing Oil Price Volatility Forecasting Frameworks? SSRN Electron. J. . Dunis, C., Kellard, N.M., Snaith, S., 2013. Forecasting EUR-USD implied volatility: The case of intraday data. J. Bank. Financ. 37, 4943–4957. Elder, J., Serletis, A., 2010. Oil price uncertainty. J.Money, Credit Bank. 42, 1137–1159. Ferderer, J.P., 1996. Oil price volatility and the macroeconomy. J.Macroecon. 18, 1–26. Fernandes, M.,Medeiros, M.C., Scharth, M., 2014. Modeling and predicting the CBOE market volatility index. J. Bank. Financ. 40, 1–10. Fleming, J., Ostdiek, B., Whaley, R.E., 1995. Predicting stock market volatility: A new measure. J. Futur.Mark. 15, 265–302. Frijns, B., Tallau, C., Tourani-Rad, A., 2010. The information content of implied volatility: Evidence from australia. J. Futur.Mark. 30, 134–155. Giot, P., 2003. The information content of implied volatility in agricultural commodity markets. J. Futur.Mark. 23, 441–454. Gong, X., Lin, B., 2018. The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market. Energy Econ. 74, 370–386. Grassi, S., Nonejad, N., De Magistris, P.S., 2017. Forecasting With the Standardized Self-Perturbed Kalman Filter. J. Appl. Econom. 32, 318–341. Hansen, P., Lunde, A., Nason, J.M., 2011. The Model Confidence Set. Econometrica 79, 453–497. 21 Haugom, E., Langeland, H., Molnár, P., Westgaard, S., 2014. Forecasting volatility of the U.S. oil market. J. Bank. Financ. 47, 1–14. Konstantinidi, E., Skiadopoulos, G., Tzagkaraki, E., 2008. Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices. J. Bank. Financ. 32, 2401–2411. Koop, G., Korobilis, D., 2012. Forecasting inflation using dynamic model averaging. Int. Econ. Rev. 53, 867–886. Koopman, S.J., Jungbacker, B., Hol, E., 2005. Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. J. Empir. Financ. 12, 445–475. Liu, M.L., Ji, Q., Fan, Y., 2013. How does oil market uncertainty interact with other markets? Anempirical analysis of implied volatility index. Energy 55, 860–868. Liu, Z., Tseng, H.K., Wu, J.S., Ding, Z., 2020. Implied volatility relationships between crude oil and the U.S. stock markets: Dynamic correlation and spillover effects. Resour. Policy 66. Lv, W., 2018. Does the OVX matter for volatility forecasting? Evidence from the crude oil market. Phys. A Stat.Mech. its Appl. 492, 916–922. Lyócsa, Š., Molnár, P., 2018. Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds. Energy 155, 462–473. Mandelbrot, B.B.,Wallis, J.R., 1969. Some long-run properties of geophysical records. Water Resour. Res. 5, 321–340. Prokopczuk, M., Symeonidis, L.,Wese Simen, C., 2016. Do JumpsMatter for Volatility Forecasting? Evidence from EnergyMarkets. J. Futur.Mark. 36, 758–792. Raftery, A.E., Kárný, M., Ettler, P., 2010. Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics , 52–66. Sánchez Granero, M.A., Trinidad Segovia, J.E., García Pérez, J., 2008. Some comments on Hurst exponent and the long memory processes on capital markets. Phys. A Stat.Mech. its Appl. 387, 5543–5551. Sévi, B., 2014. Forecasting the volatility of crude oil futures using intraday data. Eur. J. Oper. Res. 235, 643–659. 22 Weron, R., 2002. Estimating long-range dependence: Finite sample properties and confidence intervals. Phys. A Stat.Mech. its Appl. 312, 285–299, arXiv:0103510. |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/110831 |