Halkos, George and Tzirivis, Apostolos (2018): Effective energy commodities’ risk management: Econometric modeling of price volatility.
PDF
MPRA_paper_90781.pdf Download (1MB) |
Abstract
The current study emphasizes on the importance of the development of an effective price risk management strategy regarding energy products, as a result of the high volatility of that particular market. The study provides a thorough investigation of the energy price volatility, through the use of GARCH type model variations and the Markov-Switching GARCH methodology, as they are presented in the most representative academic researches. A large number of GARCH type models are exhibited together with the methodology and all the econometric procedures and tests that are necessary for developing a robust and precise forecasting model regarding energy price volatility. Nevertheless, the present research moves another step forward, in an attempt to cover also the probability of potential shifts in the unconditional variance of the models due to the effect of economic crises and several unexpected geopolitical events into the energy market prices.
Item Type: | MPRA Paper |
---|---|
Original Title: | Effective energy commodities’ risk management: Econometric modeling of price volatility |
Language: | English |
Keywords: | Energy commodities, WTI oil, Brent oil, electricity, natural gas, gasoline, risk management, volatility modeling, ARCH-GARCH models, Markov-Switching GARCH models. |
Subjects: | C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics D - Microeconomics > D8 - Information, Knowledge, and Uncertainty G - Financial Economics > G3 - Corporate Finance and Governance O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O13 - Agriculture ; Natural Resources ; Energy ; Environment ; Other Primary Products P - Economic Systems > P2 - Socialist Systems and Transitional Economies > P28 - Natural Resources ; Energy ; Environment Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q43 - Energy and the Macroeconomy Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q58 - Government Policy |
Item ID: | 90781 |
Depositing User: | G.E. Halkos |
Date Deposited: | 22 Dec 2018 12:55 |
Last Modified: | 28 Sep 2019 02:24 |
References: | Agnolucci P. (2008). Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models. Energy Economics, 31(2), pp. 316-321. Andersen T.G., Bollerslev T., Diebold F. X. and Labys P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), pp. 529–626. Ardia, D., Bluteau, K., Boudt, K., & Catania, L. (2018). ‘Forecasting risk with Markov-switching GARCH models:A large-scale performance study’. International Journal of Forecasting, 34(4), pp. 733-747. Arouri, M., Lahiani, A., Levy, A. and Nguyen, D. (2012). Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models. Energy Economics, 34, pp. 283-293. Artzner P., Delbaen F., Eber J. and Heath D. (1999). Coherent measures of risk, Mathematical Finance, 9(3), pp. 203–228. Baba Y., Engle R.F., Kraft D.F. and Kroner K.F. (1991). Multivariate Simultaneous Generalized ARCH. Econometric Theory, 11(1), pp. 122-150. Badescu A.M. and Kulperger R.J. (2008). GARCH option pricing: A semi-parametric approach. Insurance: Mathematics and Economics, 43(1), pp. 69–84. Baillie R.T., Bollerslev T. and Mikkelsen H.O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), pp. 3-30. Barone-Adesi G., Engle R.F. and Mancini L. (2008). A GARCH option pricing model with filtered historical simulation. Review of Financial Studies, 21(3), pp.1223-1258. Barone-Adesi G., Giannopoulos K. and Vosper L. (2002). Backtesting Derivative Portfolios with FHS. European Financial Management, 8(1), pp. 31-58. Basel Committee on Banking Supervision (1996). Supervisory framework for the use of “backtesting” in conjunction with the internal model-based approach to market risk capital requirements. Bank for International Settlements, Basel, Switzerland. Retrieved from: https://www.bis.org/publ/bcbsc223.pdf Basel Committee on Banking Supervision, (2004). International convergence of capital measurement and capital standards. Bank for International Settlements, Basel, Switzerland. Retrieved from: https://www.bis.org/publ/bcbs128.pdf Basel III. (2010). A global regulatory framework for more resilient banks and banking systems. Bank for international settlements, Basel, Switzerland. Retrieved from: https://www.bis.org/publ/bcbs189_dec2010.pdf Bauwens L., De Backer B. and Dufays A. (2014). A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models. Journal of Empirical Finance, 29, pp. 207-229. Bauwens, L., Preminger, A., & Rombouts, J. V. K. (2010). Theory and inference for a Markov switching GARCH model. Econometrics Journal, 13(2), pp. 218–244. Bertsimas D., Lauprete G.J. and Samarov A. (2004). Shortfall as a Risk Measure: Properties, Optimization and Applications. Journal of Economic Dynamics and Control, 28(7), pp. 1353–1381. Bierbrauer M., Menn C, Rachev S.T. and Truck S. (2007). Spot and derivative pricing in the EEX power market. Journal of Banking and Finance, 31(11), pp. 3462-3485. Black F. (1976). The pricing of commodity contracts. Journal of Financial Economics, 3(1-2), pp. 167-179. Bollerslev T, RY Chou and KF Kroner (1992). ARCH modeling in finance. Journal of Econometrics, 52(1-2), pp. 5–59. Bollerslev T. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 69(3), pp. 542-547. Bollerslev T. (1990). Modeling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Approach. Review of Economics and Statistics, 72, 498-505. Bollerslev T, Engle R.F. and Nelson D.B. (1994). ARCH Model. Handbook of Econometrics, Volume IV, (pp.2959–3038). Elsevier Science B.V. Retrieved from: http://public.econ.duke.edu/~boller/PublishedPapers/benhand94.pdf Bollerslev T. and Mikkelsen H. (1996). Modeling and pricing long memory in stock market volatility. Journal of Econometrics, 73(1), pp. 151–184. Bollerslev T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. Bollerslev T. and Wooldridge J.M. (1992). Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances. Econometric Reviews, 11(2), 143-172. Bollerslev T., Engle R.F. and Wooldridge J.M. (1988). A Capital Asset Pricing Model with Time Varying Covariances. Journal of Political Economy, 96(1), pp. 116-131. Brace A., Gatarek D. and Musiela M. (1997). The Market Model of Interest Rate Dynamics. Mathematical Finance, 7(2), pp. 127-155. Brace A., Goldys B., Klebaner F. and Womersley R. (2001). Market Model of Stochastic Implied Volatility with Application to the BGM Model. Working Paper, Department of Statistics, University of New South Wales. Bühlmann, P & McNeil, AJ (2002). An algorithm for nonparametric GARCH modelling Computational Statistics & Data Analysis, 40(4), pp. 665-683. Cabedo J.D. and Moya I. (2003). Estimating oil price value at risk using the historical simulation approach. Energy Economics, 25(3), pp. 239–253. Cai J. (1994). A Markov Model of Switching-Regime ARCH. Journal of Business & Economic Statistics, 12(3), pp. 309-315. Ceczy C.C., Minton B.A. and Schrand C.M. (2000). Choices among alternative risk management strategies: Evidence from the natural gas industry. Working Paper, University of Pennsylvania. Chan K.F., Gray P. and Van Campen B. (2008). A new approach to characterizing and forecasting electricity price volatility. International Journal of Forecasting, 24(4), pp. 728-743. Chang C., McAleer M. and Tansuchat R. (2009). Modelling conditional correlations for risk diversification in crude oil markets. The Journal of Energy Markets, 2(4), pp. 29-51. Chang C., McAleer M. and Tansuchat R. (2010). Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets, Energy Economics, 32(6), pp. 1445-1455. Chang C., McAleer M. and Tansuchat R. (2011). Crude oil hedging strategies using dynamic multivariate GARCH. Energy Economics, 33(5), pp. 912-923. Chen C.W., So M.K. and Lin E.M. (2009). Volatility forecasting with double Markov switching GARCH models’, Journal of Forecasting, 28(8), pp. 681–697. Cheong C.W. (2009). Modeling and forecasting crude oil markets using ARCH-type models. Energy Policy, 37(6), pp. 2346-2355. Chkili W., Hammoudeh S. and Nguyen K.D. (2014). Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory. Energy Economics, 41, pp. 1-18. Choi K. and Hammoudeh S. (2009). Long memory in oil and refined products markets. The Energy Journal, 30(2), pp.48-55. Christoffersen P.F. (1998). Evaluating interval forecasts, International Economic Review, 39(4), pp. 841-862. Christoffersen P.F. and Diebold F.X. (2006). Financial asset returns, direction-of-change fore-casting and volatility dynamics. Management Science, 52(8), pp. 1273–1287. Christoffersen, P., 1998. Evaluating Intervals Forecasts, International Economic Review, 39, pp. 841-862. Cifter A. (2013). Forecasting electricity price volatility with the Markov-switching GARCH model: Evidence from the Nordic electric power market. Electric Power Systems Research, 102, pp. 61-67. Clewlow, L., & Strickland, C. (2000). Energy derivatives: Pricing and risk management. London: Lacima Publications. Davidson J. (2004). Moment and memory properties of linear conditional heteroscedasticity models, and a new model. Journal of Business & Economic Statistics, 22(1), pp. 16–29. Degiannakis S. (2004). Volatility forecasting: evidence from a fractional Integrated asymmetric Power ARCH skewed-t model. Applied Financial Economics, 14(18), pp. 1333–1342. Denton M., Palmer A., Masiello R. and Skantze P. (2003) Managing Market Risk in Energy. IEEE Transactions on Power Systems, 18(2), pp. 494-502. Devlin J. (2004). Managing oil price risk in developing countries. The World Bank Research Observer, 19(1), pp. 119-139. Di Sanzo S. (2018). A Markov switching long memory model of crude oil price return volatility. Energy Economics, 74, pp. 351-359. Dickey D.A. and Fuller W.A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74(366), pp. 427-431. Diebold F.X. and Mariano R.S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13 (3), pp. 253-263. Dueker M.J. (1997). Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility. Journal of Business & Economic Statistics, 15(1), pp. 26-34. Elder J. and Serletis A. (2008). Long memory in energy futures prices. Review of Financial Economics, 17(2), pp. 146-155. Energy Information Administration (EIA) (2002). Derivatives and Risk Management in the Petroleum, Natural Gas and Electricity Industries. Washington, DC: Department of Energy. Engle R.F. and Bollerslev T. (1986). Modeling the persistence of conditional variances. Econometric Reviews, 5(1), pp. 1–50. Engle R.F. and Mustafa C. (1992). Implied ARCH models from option price. Journal of Econometrics, 52(1-2), pp. 289–311. Engle R.F. and Ng V.K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48(5), pp. 1749-1778. Engle R.F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation. Econometrica, 50(4), pp. 987-1007. Engle R. F. (2001). Garch 101: The use of ARCH and GARCH models in Applied Econometrics. Journal of Economic Perspectives, 15(4), pp. 157-168. Engle R.F. (2002). Dynamic Conditional Correlation. Journal of Business and Economic Statistics, 20(3), pp. 339-350. Engle R.F. and Kroner, K.F. (1995). ‘Multivariate simultaneous generalized ARCH’. Econometric Theory, 11(1), pp. 122-150. Elie, L. and Jeantheau, T. (1995). Consistency in heteroskedastic models. Comptes Rendus de l ’Acad´emie des Sciences, 320, pp. 1255–1258. Eydeland A. and Wolyniec K. (2003). Energy and Power Risk management: new developments in modeling, pricing and hedging, Chicago: Wiley. Fama E.F. (1965). The behavior of stock market prices. Journal of Business, 38(1), pp 34-105. Fleming J., Kirby C. and Ostdiek B. (2001). The economic value of volatility timing. Journal of Finance, 56(1), pp. 329–352. Fong W.M. and See K.H. (2002). A Markov switching model of the conditional volatility of crude oil futures prices. Energy Economics, 24(1), pp. 71–95. Gao Y., Zhang C. and Zhang L. (2012). Comparison of GARCH Models based on Different Distributions’. Journal of Computers, 7(8), pp. 1967-1973. Gray S.F. (1996). Modeling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics, 42(1), pp. 27-62. Gunay S. (2015). Markov Regime Switching Generalized Autoregressive Conditional Heteroskedastic Model and Volatility Modeling for Oil Returns. International Journal of Energy Economics and Policy, 5(4), pp. 979-985. Haas M. (2004). A New Approach to Markov-Switching GARCH Models. Journal of Financial Econometrics, 2(4), pp. 493-530. Halkos G.(2006). Econometrics: Theory and practice. Giourdas Publications. Halkos G.(2011). Econometrics: Theory and practice: Instructions in using Eviews, Minitab, SPSS and excel. Gutenberg: Athens. Halkos G., Papadamou S. (2006). An investigation of bond term premia in international government bond indices. Research in International Business and Finance, 20(1), 45-61. Halkos G., Papadamou S. (2007). Significance of risk modelling in the term structure of interest rates. Applied Financial Economics, 17(3), 237-247. Halkos G. and Sepetis A. (2007). Can capital markets respond to environmental policy of firms? Evidence from Greece, Ecological Economics, 63(2-3), 578-587. Halkos G. and Zisiadou A. (2018a). Reporting the natural environmental hazards occurrences and fatalities over the last century, MPRA Paper 87936, University Library of Munich, Germany. Halkos G. and Zisiadou A. (2018b). Analysing last century’s occurrence and impacts of technological and complex environmental hazards, MPRA Paper 90503, University Library of Munich, Germany. Hamilton J.D. (1989). A New Approach to the Economic Analysis of Non-stationary Time Series and the Business Cycle. Econometrica, 57(2), pp. 357-384. Hamilton J.D. (1990). Analysis of time series subject to changes in regime. Journal of Econometrics, 45(1-2), pp. 39-70. Hamilton J.D. and Susmel R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 64(1-2), pp. 307-333. Hamilton J.D. (1994). Time series Analysis, New Jersey: Princeton University Press. Hammoudeh S.E. and Aleisa E. (2004). Dynamic relationships among GCC stock markets and NYMEX oil futures, Contemporary Economic Policy, 22(2), pp. 250–269. Harris R. and Sollis R. (2003). Applied time series Modeling and Forecasting, West Sussex: Wiley and Sons Limited. Harvey D.I., Leybourne S.J. and Newbold P. (1997). Testing the Equality of Prediction Mean Squared Errors. International Journal of Forecasting, 13(2), pp. 281-291. Hibon M. and Evgeniou T. (2005). To combine or not to combine: selecting among forecasts and their combinations. International Journal of Forecasting, 21(1), pp. 15–24. Hillebrand E. (2005). Neglecting parameter changes in GARCH models. Journal of Econometrics, 129(1-2), pp. 121-138. Hou A. and Suardi S. (2012). A nonparametric GARCH model of crude oil price return volatility. Energy Economics, 34(2), pp. 618-626. Hudson Doran S.J. and Ronn E.I., (2008). Computing the Market Price of Volatility Riskin the Energy Commodity Markets. Journal of Banking and Finance, 32(12), pp. 2541-2552. Huisman R. and Mahieu R.J., (2001). Regime jumps in electricity prices. SSRN Electronic Journal, Volume 25, Issue 5, pp. 425-434. Huisman R., Dahlen K.E. and Westgaard S. (2015). Risk Modelling of Energy Futures: A comparison of RiskMetrics, Historical Simulation, Filtered Historical Simulation, and Quantile Regression. In Stochastic Models, Statistics and Their Applications, pp 283-291. Wraclow, Switzerland: Springer. Hurst, H.E., 1951. Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, pp. 770-799. Jackson J. (2010). Promoting energy efficiency investments with risk management decision tools. Energy Policy, 38(8), pp. 3865-3873. Jarque C.M. and Bera A.K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), pp. 255-259. Kang S.H. and Yoon S. (2012). Modeling and forecasting the volatility of petroleum futures prices’. Energy Economics, 36, pp. 354-362. Kang S.H., Kang S. and Yoon S. (2008). Forecasting volatility of crude oil markets’.Energy Economics, 31(1), pp. 119-125. Khindarova I. and Atakhanova Z. (2002). Stable modeling in energy risk management. Mathematical Methods of Operations Research (ZOR), 55(2), pp. 225-245. Klaassen F.F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH. Empirical Economics, 27(2), pp. 363-394. Klar B., Lindner F. and Meintanis S. (2012). Specification tests for the error distribution in GARCH models. Computational Statistics and Data Analysis, 56(11), pp. 3587–3598. Kupiec P.H. (1995). Techniques for verifying the accuracy of risk measurement Models. The Journal of Derivatives, 3(2), pp. 73–84. Lamoureux C.G., and Lastrapes W.D. (1990). Persistence in Variance, Structural Change and the GARCH Model. Journal of Business & Economic Statistics, 8(2), pp. 225-234. Lanza A., Mateo M. and McAleer M., (2004). Modelling Dynamic Conditional Correlations in WTI oil forwards and futures returns, Finance Research Letters, Elsevier, 3(2), pp. 114-132. Ledoit O. and Santa-Clara P. (1998). Relative Pricing of Options with Stochastic Volatility. Los Angeles: Anderson Graduate School of Management, UCLA. Ljung G.M., and Box G.E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), pp. 297-303. Lopez J.A. (2001). Evaluating the predictive accuracy of volatility models. Journal of Forecasting, 20(2), pp. 87-109. Luo C., Seco L.A., Wang H. and Dash Wu D. (2010). Risk modeling in crude oil market: a comparison of Markov switching and GARCH models. Kybernetes, 39(5), pp. 750-769. Marcucci J. (2005). Forecasting Stock Market Volatility with Regime-Switching GARCH Models. Studies in Nonlinear Dynamics & Econometrics, 9(4), pp. 1-55. McNeil A.J. and Frey R. (2000). Estimation of tail-related risk measures for Heteroskedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7(3-4), pp. 271-300. Menn C. and Rachev S.T. (2005). A GARCH option pricing model with α-stable innovations. Europrean Journal of Operational Research, 163(1), pp. 201–209. Mikosch T. and Stărică C. (2004). Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects. Review of Economics and Statistics, 86(1), pp. 378-390. Mincer, J. and V. Zarnowitz (1969). The evaluation of economic forecasts. In J. Mincer (Ed.), Economic Forecasts and Expectations. New York: Columbia University Press. Mohammadi H. and Su L. (2010). International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models. Energy Economics, 32(5), pp. 1001-1008. Morana C. (2001). A semi-parametric approach to short-term oil price forecasting. Energy Economics, 23(3), pp. 325–338. Morgan J.P. (1996). RiskMetrics Technical Document 4th ed’, New York. Mount T.D., Ning Y. and Cai X. (2006). Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters. Energy Economics, 28(1), pp. 62-80. Narayan P.K. and Narayan S. (2007). Modeling oil price volatility. Energy Policy, 35(12), pp. 6549-6553. Nelson D.B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), pp. 347-370. Nomikos N.K. and Pouliasis P.K. (2011). Forecasting petroleum futures markets volatility: The role of regimes and market conditions. Energy Economics, 33(2), pp. 321-337. Panas E. (2001). Estimating fractal dimension using stable distributions and exploring long memory through ARFIMA models in Athens Stock Exchange. Applied Financial Economics, 11(4), pp. 395-402. Papapetrou E. (2001). Oil price shocks, stock markets, economic activity and employment in Greece. Energy Economics, 23(5), pp. 511–532. Patton, Andrew J., (2011). Volatility forecast comparison using imperfect volatility proxies, Journal of Econometrics, 160(1), pp. 246-256. Peng C., Buldyrev S.V., Simons H.M., Stanley H.E. and Goldberger A.L. (1994). Mosaic organization of DNA nucleotides. Physical Review E, 49(2), pp. 1685–1689. Pilipovic D. (1998). Energy risk: Valuing and managing energy derivatives, New York: McGraw-Hill. Pindyck R.S. (1999). The Long-Run Evolution of Energy Prices’. The Energy Journal, 20 (2), pp. 1-27. \ Politis D.N. and Romano J.P. (1994). The stationary bootstrap. Journal of The American Statistical Association, 89 (428), pp. 1303-1313. Pritsker M. (2005). The hidden dangers of Historical Simulation. Journal of Banking and Finance, 30(2), pp. 561-582. Runfang Y., Jiangze D. and Xiaotao L. (2017). Improved Forecast Ability of Oil Market Volatility Based on combined Markov Switching and GARCH-class Model. Procedia Computer Science, 122, pp. 415-422. Sadorsky P., (2006). Modeling and forecasting petroleum futures volatility’, Energy Economics, 28(4), pp. 467-488. Schönbucher P. (1999). A Market Model for Stochastic Implied Volatility. SSRN Electronic Journal. 357(1758), pp. 2071-2092. Smithson C.J. and Simkins B.J. (2005). Does Risk Management Add Value? A Survey of the Evidence. Journal of Applied Corporate Finance, 17(3), pp. 8-17. Sun P. and Zhou C. (2014). Diagnosing the Distribution of GARCH Innovations. Journal of Empirical Finance, 29, pp. 287–303. Vo M.T. (2009). Regime-switching stochastic volatility: Evidence from the crude oil market. Energy Economics, 31(5), pp. 779-788. Wang Y. and Wu C. (2012). Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?, Energy Economics, 34(6), pp. 2167-2181. Wang Y., Wu C., Wei Y. (2011). Can GARCH-class models capture long memory in WTI crude oil markets?. Economic Modelling, 28(3), pp. 921-327. Wei Y., Wang Y. and Huang D. (2010). Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics, 32(6), pp. 1477-1484. White H. (2000). A reality check for data snooping. Econometrica, 68(5), pp. 1097–1126. Yue-Jun Zhang, Ting Yao, Ling-Yun He, (2018). Forecasting crude oil market volatility: can the Regime Switching GARCH model beat the single-regime GARCH models?’ International Review of Economics & Finance, 59, pp. 302-317. Xiao L. and Aydemir A. (2007) Volatility modelling and forecasting in finance. In: Knight J. and Satchell S. (Eds). Forecasting volatility in the financial markets. Elsevier Finance. Elsevier. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/90781 |