Degiannakis, Stavros and Filis, George (2017): Forecasting oil price realized volatility using information channels from other asset classes. Published in: Journal of International Money and Finance No. 76 (2017): pp. 2849.

PDF
MPRA_paper_96276.pdf Download (1MB)  Preview 
Abstract
Motivated from Ross (1989) who maintains that asset volatilities are synonymous to the information flow, we claim that crossmarket volatility transmission effects are synonymous to crossmarket information flows or “information channels” from one market to another. Based on this assertion we assess whether crossmarket volatility flows contain important information that can improve the accuracy of oil price realized volatility forecasting. We concentrate on realized volatilities derived from the intraday prices of the Brent crude oil and four different asset classes (Stocks, Forex, Commodities and Macro), which represent the different “information channels” by which oil price volatility is impacted from. We use a HAR framework and we create forecasts for 1day to 66days ahead. Our findings provide strong evidence that the use of the different “information channels” enhances the predictive accuracy of oil price realized volatility at all forecasting horizons. Numerous forecasting evaluation tests and alternative model specifications confirm the robustness of our results.
Item Type:  MPRA Paper 

Original Title:  Forecasting oil price realized volatility using information channels from other asset classes 
Language:  English 
Keywords:  Volatility forecasting, realized volatility, crude oil futures, risk management, HAR, asset classes 
Subjects:  C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries 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:  96276 
Depositing User:  Dr. STAVROS DEGIANNAKIS 
Date Deposited:  15 Oct 2019 13:44 
Last Modified:  15 Oct 2019 13:44 
References:  Abramson, B., & Finizza, A. (1991). Using belief networks to forecast oil prices. International Journal of Forecasting, 7(3), 299315. Agnolucci, P. (2009). Volatility in crude oil futures: a comparison of the predictive ability of GARCH and implied volatility models. Energy Economics, 31(2), 316321. Aiolfi, M., & Favero, C. A. (2005). Model uncertainty, thick modelling and the predictability of stock returns. Journal of Forecasting, 24(4), 233254. Aloui, C., & Jammazi, R. (2009). The effects of crude oil shocks on stock market shifts behaviour: a regime switching approach. Energy Economics, 31(5), 789799. Aloui, R., Aïssa, M. S. B., & Nguyen, D. K. (2013). Conditional dependence structure between oil prices and exchange rates: a copulaGARCH approach. Journal of International Money and Finance, 32, 719738. Alquist, R., Kilian, L., & Vigfusson, R. J. (2013). Forecasting the price of oil. Handbook of Economic Forecasting, 2, 427507. Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 885905. 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), 4255. 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), 279296. Andersen, T. G., Bollerslev, T., Christoffersen, P. F., & Diebold, F. X. (2006). Volatility and correlation forecasting. Handbook of Economic Forecasting, 1, 777878. 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), 701720. Andersen, T. G., Bollerslev, T., Frederiksen, P., & Ørregaard Nielsen, M. (2010). Continuous‐time models, realized volatilities, and testable distributional implications for daily stock returns. Journal of Applied Econometrics, 25(2), 233261. AndradaFelix, J., FernandezRodriguez, F. and Fuertes, A.M. (2016). Combining nearest neighbor predictions and modelbased predictions of realized variance: Does it pay? International Journal of Forecasting, 32, 695–715. Angelidis, T., Benos, A., & Degiannakis, S. (2004). The use of GARCH models in VaR estimation. Statistical Methodology, 1(1), 105128. Angelidis, T., & Degiannakis, S. (2008). Volatility forecasting: Intraday versus interday models. Journal of International Financial Markets, Institutions and Money, 18(5), 449465. Antonakakis, N., Chatziantoniou, I., & Filis, G. (2014). Dynamic spillovers of oil price shocks and economic policy uncertainty. Energy Economics, 44, 433447. Arouri, M. E. H., Jouini, J., & Nguyen, D. K. (2011). Volatility spillovers between oil prices and stock sector returns: implications for portfolio management. Journal of International Money and Finance, 30(7), 13871405. Arouri, M. E. H., Lahiani, A., Lévy, 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), 283293. Baker, S. R., Bloom, N., & Davis, S. J. (2013). Measuring economic policy uncertainty. Chicago Booth research paper, (1302). Baumeister, C., & Kilian, L. (2012). Realtime forecasts of the real price of oil. Journal of Business & Economic Statistics, 30(2), 326336. Baumeister, C., & Kilian, L. (2014). What central bankers need to know about forecasting oil prices. International Economic Review, 55(3), 869889. Baumeister, C., & Kilian, L. (2015). Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach. Journal of Business & Economic Statistics, 33(3), 338351. Baumeister, C., & Peersman, G. (2013). TimeVarying Effects of Oil Supply Shocks on the US Economy. American Economic Journal: Macroeconomics, 5(4), 128. Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637654. Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of Econometrics, 52(1), 559. Bollerslev, T., & Wright, J. H. (2001). Highfrequency data, frequency domain inference, and volatility forecasting. Review of Economics and Statistics, 83(4), 596602. 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 crossmarket linkages. Journal of International Money and Finance, 42, 3870. Cabedo, J. D., & Moya, I. (2003). Estimating oil price ‘value at risk’ using the historical simulation approach. Energy Economics, 25, 239–253. Chatrath, A., Miao, H., Ramchander, S., & Wang, T. (2015). The Forecasting Efficacy of Risk‐Neutral Moments for Crude Oil Volatility. Journal of Forecasting, 34(3), 177190. Chen, YC., Rogoff, S.K., & Rossi, B. (2010). Can exchange rates forecast commodity prices?. The Quarterly Journal of Economics, 125(3), 11451194. 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, 118. Christensen, B. J., & Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50(2), 125150. Clark, T.E. and West, K.D. (2007). Approximately normal tests for equal predictive accuracy in nested models, Journal of Econometrics, 138, 291–311. Corsi, F. (2009). A simple approximate longmemory model of realized volatility. Journal of Financial Econometrics, 7, 174–196. Degiannakis, S. (2004). Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewedt model. Applied Financial Economics, 14(18), 13331342. Degiannakis, S., Filis, G., & Floros, C. (2013). Oil and stock returns: Evidence from European industrial sector indices in a timevarying environment. Journal of International Financial Markets, Institutions and Money, 26, 175191. Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13, 253263. Efimova, O., & Serletis, A. (2014). Energy markets volatility modelling using GARCH. Energy Economics, 43, 264273. Elder, J., & Serletis, A. (2010). Oil price uncertainty. Journal of Money, Credit and Banking, 42(6), 11371159. Engle, R.F., Hong, C.H., Kane, A. and Noh, J. (1993). Arbitrage Valuation of Variance Forecasts with Simulated Options, Advances in Futures and Options Research, 6, 393415. Engle, F. R. (2002). Dynamic conditional correlation: a simple class of multivariate GARCH models. Journal of Business and Economic Statistics, 20, 339−350. Engle, E., & Sun, Z. (2007). When is noise not noise–a microstructure estimate of realized volatility. Fattouh, B., Kilian, L., & Mahadeva, L. (2013). The Role of Speculation in Oil Markets: What Have We Learned So Far?. The Energy Journal, 34(3). Fernandes, M., Medeiros, M. C., & Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking & Finance, 40, 110. Ferraro, D., Rogoff, K., & Rossi, B. (2015). Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates. Journal of International Money and Finance, 54, 116141. Filis, G. (2010). Macro economy, stock market and oil prices: Do meaningful relationships exist among their cyclical fluctuations?. Energy Economics, 32(4), 877886. Filis, G., Degiannakis, S., & Floros, C. (2011). Dynamic correlation between stock market and oil prices: The case of oilimporting and oilexporting countries. International Review of Financial Analysis, 20(3), 152164. Filis, G., & Chatziantoniou, I. (2014). Financial and monetary policy responses to oil price shocks: evidence from oilimporting and oilexporting countries. Review of Quantitative Finance and Accounting, 42(4), 709729. Frijns, B., Tallau, C., & Tourani‐Rad, A. (2010). The information content of implied volatility: evidence from Australia. Journal of Futures Markets, 30(2), 134155. Fuertes, A. M., Izzeldin, M., & Kalotychou, E. (2009). On forecasting daily stock volatility: The role of intraday information and market conditions. International Journal of Forecasting, 25(2), 259281. Giannone, D., L. Reichlin and D. Small (2008). Nowcasting: The real time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665–676. Giot, P., & Laurent, S. (2003). Market risk in commodity markets: A VaR approach. Energy Economics, 25, 435–457. Hamilton, J. D. (1983). Oil and the macroeconomy since World War II. The Journal of Political Economy, 228248. Hansen, P.R. (2005). A Test for Superior Predictive Ability. Journal of Business and Economic Statistics, 23, 365380. Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of Applied Econometrics, 20(7), 873889. Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453497. Haugom, E., Langeland, H., Molnár, P., & Westgaard, S. (2014). Forecasting volatility of the US oil market. Journal of Banking & Finance, 47, 114. Hou, A., & Suardi, S. (2012). A nonparametric GARCH model of crude oil price return volatility. Energy Economics, 34(2), 618626. Huntington, H. G. (1994). Oil price forecasting in the 1980s: what went wrong?. The Energy Journal, 122. IEA (2015). What drives crude oil prices? US International Energy Administration, July 07. https://www.eia.gov/finance/markets/spot_prices.cfm Jiang, G. J., & Tian, Y. S. (2005). The modelfree implied volatility and its information content. Review of Financial Studies, 18(4), 13051342. Jones, C. M., & Kaul, G. (1996). Oil and the stock markets. The Journal of Finance, 51(2), 463491. Kang, S. H., Kang, S. M., & Yoon, S. M. (2009). Forecasting volatility of crude oil markets. Energy Economics, 31(1), 119125. Kang, S.H., & Yoon, S.M. (2013). Modelling and forecasting the volatility of petroleum futures prices. Energy Economics, 36, 354–362. Kilian, L., & Park, C. (2009). The impact of oil price shocks on the US stock market. International Economic Review, 50(4), 12671287. Le Pen, Y., & Sévi, B. (2013). Futures trading and the excess comovement of commodity prices. Available at SSRN 2191659. McAleer, M., & Medeiros, M. C. (2008). Realized volatility: A review. Econometric Reviews, 27(13), 1045. Mensi, W., Hammoudeh, S., Nguyen, D. K., & Yoon, S. M. (2014). Dynamic spillovers among major energy and cereal commodity prices. Energy Economics, 43, 225243. Nomikos, N. K., & Pouliasis, P. K. (2011). Forecasting petroleum futures markets volatility: The role of regimes and market conditions. Energy Economics, 33(2), 321337. Pesaran, M. H., & Timmermann, A. (2009). Testing dependence among serially correlated multicategory variables. Journal of the American Statistical Association, 104(485), 325337. Phan, D. H. B., Sharma, S. S., & Narayan, P. K. (2015). Intraday volatility interaction between the crude oil and equity markets. Journal of International Financial Markets, Institutions and Money, forthcoming. Prokopczuk, M., Symeonidis, L., & Wese Simen, C. (2015). Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets. Journal of Futures Markets, 135. Rahman, S., & Serletis, A. (2011). The asymmetric effects of oil price shocks. Macroeconomic Dynamics, 15(S3), 437471. Ross, S. A. (1989). Information and volatility: The no‐arbitrage martingale approach to timing and resolution irrelevancy. The Journal of Finance, 44(1), 117. Sadorsky, P. (2006). Modeling and forecasting petroleum futures volatility. Energy Economics, 28, 467–488. Sadorsky, P. (2014). Modelling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat. Energy Economics, 43, 728. Sadorsky, P., & McKenzie, M. D. (2008). Power transformation models and volatility forecasting. Journal of Forecasting, 27, 587–606. Samuels, J. D., & Sekkel, R. M. (2013). Forecasting with many models: Model confidence sets and forecast combination (No. 201311). Bank of Canada Working Paper. Sari, R., Hammoudeh, S., & Soytas, U. (2010). Dynamics of oil price, precious metal prices, and exchange rate. Energy Economics, 32(2), 351362. Sévi, B. (2014). Forecasting the volatility of crude oil futures using intraday data. European Journal of Operational Research, 235(3), 643659. Silvennoinen, A., & Thorp, S. (2013). Financialization, crisis and commodity correlation dynamics. Journal of International Financial Markets, Institutions and Money, 24, 4265. Souček, M., & Todorova, N. (2013). Realized volatility transmission between crude oil and equity futures markets: A multivariate HAR approach. Energy Economics, 40, 586597. Souček, M., & Todorova, N. (2014). Realized volatility transmission: The role of jumps and leverage effects. Economics Letters, 122(2), 111115. Stock, J. H. and M. W. Watson (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167–1179. Tay, A., Ting, C., Tse, Y. K., & Warachka, M. (2009). Using highfrequency transaction data to estimate the probability of informed trading. Journal of Financial Econometrics, 7(3), 288311. Thomakos, D. D., & Wang, T. (2003). Realized volatility in the futures markets. Journal of Empirical Finance, 10(3), 321353. Timmermann, A. (2006). Forecast combinations. Handbook of economic forecasting, 1, 135196. Wei, Y., Wang, Y., & Huang, D. (2010). Forecasting crude oil market volatility: Further evidence using GARCHclass models. Energy Economics, 32, 1477–1484. Wen, F., Gong, X., & Cai, S. (2016). Forecasting the volatility of crude oil futures using HARtype models with structural breaks. Energy Economics, 59, 400413. West, K. D., & Cho, D. (1995). The predictive ability of several models of exchange rate volatility. Journal of Econometrics, 69(2), 367391. White, H. (2000). A Reality Check for Data Snooping. Econometrica, 68, 1097–1126. Wu, L. (2011). Variance dynamics: Joint evidence from options and highfrequency returns. Journal of Econometrics, 160(1), 280287. Xekalaki, E. and Degiannakis, S. (2005). Evaluating volatility forecasts in option pricing in the context of a simulated options market. Computational Statistics and Data Analysis, 49, 611–629. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/96276 