Halkos, George and Tsirivis, Apostolos (2019): Using Value-at-Risk for effective energy portfolio risk management.
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Abstract
It is evident that the prediction of future variance through advanced GARCH type models is essential for an effective energy portfolio risk management. Still it fails to provide a clear view on the specific amount of capital that is at risk on behalf of the investor or any party directly affected by the price fluctuations of specific or multiple energy commodities. Thus, it is necessary for risk managers to make one further step, determining the most robust and effective approach that will enable them to precisely monitor and accurately estimate the portfolio’s Value-at-Risk, which by definition provides a good measure of the total actual amount at stake. Nevertheless, despite the variety of the variance models that have been developed and the relative VaR methodologies, the vast majority of the researchers conclude that there is no model or specific methodology that outperforms all the others. On the contrary, the best approach to minimize risk and accurately forecast the future potential losses is to adopt that specific methodology that will be able to take into consideration the particular characteristic features regarding the trade of energy products.
Item Type: | MPRA Paper |
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Original Title: | Using Value-at-Risk for effective energy portfolio risk management |
Language: | English |
Keywords: | Energy commodities, Risk Management, Value-at-Risk (VaR). |
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 > D81 - Criteria for Decision-Making under Risk and Uncertainty G - Financial Economics > G3 - Corporate Finance and Governance > G30 - General 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 Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q58 - Government Policy |
Item ID: | 91674 |
Depositing User: | G.E. Halkos |
Date Deposited: | 23 Jan 2019 21:38 |
Last Modified: | 26 Sep 2019 14:04 |
References: | Adamko P., Spuchľáková E. and Valášková K. (2015). The history and ideas behind VaR. Procedia Economics and Finance, 24, pp. 18-24. Abad P., Benito S. and Lopez C. (2014). A comprehensive review of Value at Risk methodologies. The Spanish Review of Financial Economics,12(1), pp. 15-32. Adcock C. J., Areal N. and Oliveira B. (2011). Value-at-Risk forecasting ability of filtered historical simulation for non-Normal GARCH returns. SSRN Electronic Journal. Retrieved from: http://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.225.6092&rep=rep1&type=pdf Aloui C. and Mabrouk S. (2010). Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models. Energy Policy, 38(5), pp. 2326–2339. Andriosopoulos K. and Nomikos N. (2013). Risk management in the energy markets and Value-at-Risk modelling: a Hybrid approach. The European Journal of Finance, 21(7), pp. 548-574. Angelidis T., Benos A. and Degiannakis S. (2004). The use of GARCH models in VaR estimation. Statistical Methodology, 1(1-2), pp. 105–128. Angelidis T. and Degiannakis S. Α. (2005). Modelling risk:VaR methods for long and short trading positions. The Journal of Risk Finance, 6(3), pp. 226-238. Angelidis T., Benos A. and Degiannakis S. (2006). A robust VaR model under different time periods and weighting schemes. Review of Quantitative Finance and Accounting, 28(2), pp. 187–201. Antonelli S. and Iovino M.G. (2002). Optimization of Monte Carlo procedures for value at risk estimates. Economic Notes, 31(1), pp. 59–78. Ardia D., Bluteau K., Boudt K. and Catania L. (2018). Forecasting risk with Markov-switching GARCH models: A large-scale performance study. International Journal of Forecasting, 34(4), pp. 733-747. Artzner P., Delbaen F., Eber J. and Heath D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), pp. 203–228. Badescu A.M. and Kulperger R.J. (2008). GARCH option pricing: A semi-parametric approach. Insurance: Mathematics and Economics, 43(1), pp. 69–84. Bali T. G. and Theodossiou P. (2006). A conditional-SGT-VaR approach with alternative GARCH models. Annals of Operations Research , 151(1), pp. 241–267. Bali T.G. and Weinbaum D. (2007). A conditional extreme value volatility estimator based on high-frequency returns. Journal of Economic Dynamics and Control, 31(2), pp. 361–397. Balkema A.A., and Haan L. (1974). Residual lifetime at great age. Annals of Probability, 2, pp. 792-804. Barone-Adesi G., Giannopoulos K. and Vosper L. (1999). VaR without correlations for portfolios of derivative securities. Journal of Futures Markets, 19(5), pp. 583–602. 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 Bekiros S. D. and Georgoutsos D. A. (2005). Estimation of value at risk by extreme value and conventional methods: a comparative evaluation of their predictive performance. Journal of International Financial Markets, Institutions & Money, 15(3), pp. 209–228. Bertsimasa 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. Boudoukh J., Richardson M. and Whitelaw R. (1998). The Best of Both Worlds: A Hybrid Approach to Calculating Value at Risk. Stern School of Business, NYU. Retrieved from: http://www.faculty.idc.ac.il/kobi/thebestrisk.pdf Brace A., Gatarek D. and Musiela M. (1997). The Market Model of Interest Rate Dynamics. Mathematical Finance, 7(2), pp. 127-155. Brooks C. J., Clare A., Dalle Molle J., and Persand G. (2005). A comparison of extreme value theory approaches for determining value at risk. Journal of Empirical Finance, 12(2), pp. 339-352. Byström H. N. (2004). Managing extreme risks in tranquil and volatile markets using conditional extreme value theory. International Review of Financial Analysis, 13(2), pp. 133–152. Byström H. N. (2005). Extreme value theory and extremely large electricity price changes. International Review of Economics and Finance, 14(1), pp. 41–55. Bühlmann P. & McNeil A.J. (2002). An algorithm for nonparametric GARCH modelling Computational Statistics & Data Analysis, 40(4), pp. 665-683. Costello A., Asem E. and Gardner, E. (2008). Comparison of Historically Simulated Var: Evidence from Oil Prices. Energy Economics, 30, pp. 2154–2166. 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. 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. Cheng W. and Hung J. (2011). Skewness and leptokurtosis in GARCH-typed VaR estimation of petroleum and metal asset returns. Journal of Empirical Finance, 18(1), pp. 160–173. Chiu Y.W., Chuang I. and Lai J. (2010). The performance of composite forecast models of value-at-risk in the energy market. Energy Economics, 32(2), pp. 423-431. Chkili W., Hammoudeh S., and Nguyen D. K. (2014). Volatility forecasting and riskmanagement for commodity markets in the presence of asymmetry and long memory. Energy Economics, 41, pp. 1–18. Christoffersen P.F. (1998). Evaluating interval forecasts, International Economic Review, 39(4), pp. 841-862. Christoffersen P.F. (2004). Elements of Financial Risk Management 1st Ed’, Elsevier Science, USA. 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. Clewlow L. and Strickland C. (2000). Energy derivatives: Pricing and risk management. London: Lacima Publications. Danielsson and de Vries, (2000). Value-at-risk and extreme returns. Annales d’Economie et de Statistique, (60), pp. 239-270. 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. Diebold F.X. and Mariano R.S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13 (3), pp. 253-263. Dorris, G. and Dunn, A. (2001). Energy Risk Management: Making the shift to Earnings at Risk. Electric & Gas Trading Magazine, pp.5-7. Duffie D. and Pan J. (1997). An overview of value at risk. The Journal of Derivatives, 4(3), pp. 7-49. Energy Information Administration (EIA) (2002). Derivatives and Risk Management in the Petroleum. Natural Gas and Electricity Industries, Washington, DC: Department of Energy. Eydeland A. and Wolyniec K. (2003). Energy and Power Risk management: new developments in modeling, pricing and hedging, Chicago: Wiley. Chan K. and Gray P. (2006). Using the extreme value theory to measure value-at-risk for daily electricity spot prices. International Journal of Forecasting, 22(2), pp. 283-300. Fan Y. and Jiao J.L (2006). An improved historical simulation approach for estimating Value-at-Risk of crude oil price. International Journal of Global Energy Issues, 25(1/2), pp. 83-93. Fan Y., Zhang Y., Tsai H. and Wei Y. (2008). Estimating Value-at-Risk of crude oil price and its spillover effect using the GED-GARCH approach. Energy Economics, 30(6), pp. 3156-3171. Fleming J., Kirby C. and Ostdiek B. (2001). The economic value of volatility timing. Journal of Finance, 56(1), pp. 329–352. Giot P. and Laurent S. (2003). Market risk in commodity markets: a VaR approach. Energy Economics, 25(5), pp. 435–457. Giot P. and Laurent S. (2004). Modelling daily value-at-risk using realized volatility and ARCH type models. Journal of Empirical Finance, 11(3), pp. 379–398. Glosten L. R., Jagannathan R. and Runkle D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), pp. 1779- 1801. Guth L., and Sepetys K. (2001). Cash flow at risk for non-financial companies, Global Energy Business, pp. 8-11. Halkos G. and Tsirivis A. (2018). Effective energy commodities’ risk management: Econometric modeling of price volatility. MPRA Paper 85280, University Library of Munich, Germany. Harris R. and Sollis R. (2003). Applied time series Modeling and Forecasting, West Sussex: Wiley and Sons Limited. Harris R. and Shen J. (2006), Hedging and Value at Risk. Journal of Futures Markets, 26(4), pp. 369-390. Hendricks D.,(1996). Evaluation of value-at-risk models using historical data. SSRN Electronic Journal, 2(1), pp. 39-69. Holton G. (1998). Simulating value-at-risk, Risk, 11(5), pp. 60-63. Holton G.A. (2003). Value-at-risk, Theory and Practice. New York: San Diego, Chapman & Hall/CRC. Huang A. Y. (2009). A value-at-risk approach with kernel estimator. Applied Financial Economics, 19(5), pp. 379–395. Hung J.Y., Lee M. and Liu H. (2008). Estimation of value-at-risk for energy commodities via fat-tailed GARCH models. Energy Economics, 30(3), pp. 1173–1191. Huang Y. and Lin B. (2004). Value-at-risk analysis for Taiwan stock index futures: fat tails and conditional asymmetries in return innovations. Review of Quantitative Finance and Accounting, 22(2), pp. 79–95. 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. Hull J. and White A. (1998). Incorporating volatility updating the historical simulation method for value-at-risk. The Journal of Risk, 1(1), pp. 5–19. Jorion P. (2001). Value at Risk: The New Benchmark for Controlling Market Risk, New York, NY: McGraw-Hill. Khindarova I. and Atakhanova Z. (2002). Stable modeling in energy risk management. Mathematical Methods of Operations Research (ZOR), 55(2), pp. 225-245. Khindarova I., Rachev S. and Schwartz E. (2001). Stable modeling of value at risk. Mathematical methods and Computer Modelling, 34(9-11), pp. 1223–1259. Krehbiel T. and Adkins L.C. (2005). Price risk in the NYMEX energy complex: an extreme value approach. Journal of Futures Markets, 25(4), pp. 309-337. Kupiec P.H. (1995). Techniques for verifying the accuracy of risk measurement Models. The Journal of Derivatives, 3(2), pp. 73–84. Lopez J. (1999). Regulatory evaluation of Value-at-Risk models. Journal of Risk, 1(2), pp. 37-63. Lopez J.A. (2001). Evaluating the predictive accuracy of volatility models. Journal of Forecasting, 20(2), pp. 87-109. Lux T., Segnon M. and Gupta R. (2016). Forecasting crude oil price volatility and Value-at-Risk: Evidence from historical and recent data. Energy Economics, 56(C), pp. 117-133. Manganelli S. and Engle R.F. (2001). Value-at-Risk models in Finance. European Central Bank, Working Paper No 75. Retrieved from: https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp075.pdf Marimoutou V., Raggad B. and Trabelsi A. (2009). Extreme value theory and value at risk: application to oil market. Energy Economics, 31(4), pp. 519–530. Markowitz H. (1952). Portfolio Selection. The Journal of Finance, 7(1), pp. 77-91. 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. McNeil A.J., Frey R. and Embrechts P. (2005). Quantitative Risk Management: Concepts, Techniques, and Tools. Journal of The American Statistical Association, 101(10), pp. 284-351. 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. Mincer, J. and V. Zarnowitz (1969). The evaluation of economic forecasts. In J. Mincer (Ed.), Economic Forecasts and Expectations. New York: Columbia University Press. 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. 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. Paraschiv F., Hadzi-Mishev R. and Keles D. (2016). Extreme Value Theory for Heavy Tails in Electricity Prices. The Journal of Energy Markets, 9(2), pp. 21-50. Pickands J. (1975). Statistical Inference Using Extreme Order Statistics. Annals of Statistics, 3(1), pp. 119-131. 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. and Romano J.P. (1994). The stationary bootstrap. Journal of The American Statistical Association, 89 (428), pp. 1303-1313. Politis D. (2004), “A heavy-tailed distribution for ARCH residuals with application to volatility prediction”, Annals of Economics and Finance, 23, pp. 34-56. Pritsker M. (2005). The hidden dangers of Historical Simulation. Journal of Banking and Finance, 30(2), pp. 561-582. Ren F. and Giles D.E. (2010). Extreme value analysis of daily Canadian crude oil prices. Applied Financial Economics, 20(12), pp. 941-954. Rockafellar R. T. and Uryasev S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), pp. 21–41. Ronn E. I. (2003). Real Options and Energy Management: Using Options Methodology to Enhance Capital Budgeting Decisions. London: Risk Waters Publishers. Roy A. (1952). Safety first and the holding of assets, Econometrica, 20 (3), pp. 431-449. Sadeghi M. and Shavvalpour S. (2006). Energy risk management and value at risk modelling. Energy Policy, 34(18), pp. 3367-3373. Sadorsky P. (1999). Oil price shocks and stock market activity. Energy Economics, 21(5), pp. 449–469. Sadorsky P. (2003). The macroeconomic determinants of technology stock price volatility. Review of Financial Economics, 12(2), pp. 191–205. Sadorsky P. (2006). Modeling and forecasting petroleum futures volatility’, Energy Economics, 28(4), pp. 467-488. Sarma M., Thomas S. and Shah A. (2003). Selection of value-at-risk models. Journal of Forecasting, 22(4), pp. 337–358. Saunders A. and Allen L. (2002). Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms 2th ed’. Wiley, New York. Retrieved from:http://www.untagsmd.ac.id/files/Perpustakaan_Digital_1/CREDIT%20RISK%20Credit%20risk%20measurement,%20New%20approaches%20to%20value%20at%20risk%20and%20other%20paradigms.pdf 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. Stein J.C., Usher S.E., LaGattuta D. and Youngen J. (2001). A comparables approach to measuring cash-flow‐at‐risk for non‐financial firms. Journal of Applied Corporate Finance, 13(4), pp. 100–109. Vlaar P.J.G. (2000). Value at risk models for Dutch bond portfolios. Journal of Banking and Finance, 24(7), pp. 1131-1154. 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. Youssef M., Belkacem L., and Mokni, K. (2015). Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach. Energy Economics, 51, pp. 99–110. Zikovic S. and Aktan B. (2009). Global financial crisis and VaR performance in emerging markets: a case of EU candidate states – Turkey and Croatia. Journal of Economics and Business, 27(1), pp. 149–170. Zikovic S. and Filler R. K. (2009). Hybrid Historical Simulation VAR and ES: Performance in Developed and Emerging Markets. CESifo Working paper Series, 2820, pp. 1-39. Žiković S., Weron R. and Žiković I.T. (2015). Evaluating the performance of VaR models in energy markets. Springer Proceedings in Mathematics & Statistics, pp.479-487. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/91674 |