Medición del riesgo de la cola en el mercado del petróleo mexicano aplicando la teoría de valores extremos condicional


  • Raúl De Jesús Gutiérrez Facultad de Economía, Universidad Autónoma del Estado de México
  • Edgar Ortiz Calisto Facultad de Ciencias Políticas y Sociales, Universidad Nacional Autónoma de México, UNAM
  • Oswaldo García Salgado Facultad de Economía, Universidad Autónoma del Estado de México
  • Verónica Ángeles Morales Facultad de Economía, Universidad Autónoma del Estado de México


Palabras clave:

Crude oil, Conditional extreme value theory, VaR and ES measures


This paper applies the extreme values theory to the conditional distribution of standardized residuals from the specifications GARCH, EGARCH and TGARCH, and proposes dynamic risk measures to estimate VaR and expected shortfall of long and short positions of the Mexican Blend crude oil from January 4, 1989 to December 31, 2013. The results of backtesting procedure show that the models based on the conditional extreme value theory and filtered historical simulation yield more accurate estimates of conditional VaR at all confidence levels although their performance is lowered significantly for the conditional expected shortfall prediction. At 99.5% and 99.9% confidence levels, the empirical findings reveal that the government is prone to experience a higher risk than the consumers of Mexican crude oil at the international market because the inferior tail of empirical distribution is more stable and heavier than the superior tail.


Los datos de descargas todavía no están disponibles.


Aloui, C. y Mabrouk, S. (2010). Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models. Energy Policy, 38(5): 2326-2339.

Artzner, P., Delbean, F., Eber, J. M. y Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3): 203-228.

Basel Committee, (1996a). Supervisory framework for the use of backtesting in conjunction with the internal models approach to market risk capital requirements. Basel Committee on Banking and Supervision, Switzerland.

Cabedo, J. D. y Moya, I. (2003). Estimating oil prices value at risk using the historical simulation approach. Energy Economics, 25(3): 239-253.

Cheng, W. H. y Hung, J. C. (2011). Skewness and leptokurtosis in GARCH-Typed VaR estimation of petroleum and metal asset returns. Journal of Empirical Finance, 18(1): 160-173.

Chiu, Y. C., Chuang, Y. y Lai, J. Y. (2010). The performance of composite forecast models of value-at-risk in the energy market. Energy Economics, 32(2): 423-431.

Coles, S. (2001). An introduction to statistical modeling of extreme values. Springer-Verlag, London.

Costello, A., Asem, E. y Gardner, E. (2008). Comparison of historically simulated VaR: Evidence from oil prices. Energy Economics, 30(5): 2154-2166.

De Jesús, R. y Ortiz, E. (2011). Risk in emerging stock markets from Brazil and Mexico: Extreme value theory and alternative value at risk models. Frontiers in Finance and Economics, 8(2): 49-88.

Fan, Y. y 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):83-93.

Fan, Y., Zhang, Y., Tsai H. y Wei, Y. (2008). Estimating “value at risk” of crude oil price and its spillover rffect using the GED-GARCH approach. Energy Economics, 30(6):3156-3171.

Geman, H. y Kharoubi, C. (2008). WTI crude oil futures in portfolio diversification: The time to maturity effect. Journal of Banking and Finance, 32(12): 2553-2559.

Giot, P. y Laurent, S. (2003). Market risk in commodity markets: A VaR approach.Energy Economics, 25(25): 435-457.

Ghorbel, A. y Trabelsi, A. (2014). Energy portfolio risk management using time-varying extreme value copula methods. Economic Modelling, 38(2): 470-485.

Hung, J. C., Lee, M. C. y Liu, H. C. (2008). Estimation of value-at risk for energy commodities via fat-tailed GARCH models. Energy Economics, 30(3): 1173-1191.

Krehbiel, T. y Adkins, L. C. (2005). Price risk in the NYMEX energy complex: An extreme value approach. The Journal of Futures Markets, 25(4): 309-337.

Kroner, K. F., Kneafsey, K. P. y Claessens, S. (1995). Forecasting volatility in commodity markets. Journal of Forcasting, 14(2): 77-95.

Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. Journal of Derivatives, 3(2): 73-84.

Liu, H. C., Lee, M. C. y Chang, C. M. (2009). The role SGT distribution in the value at risk estimation: Evidence from the WTI crude oil markets. Investment Management and Financial Innovations, 6(1): 86-95.

Marimoutou, V., Raggad, B. y Trabelsi, A. (2009). Extreme value theory and value at risk: Application to oil market. Energy Economics, 31(4): 519-530.

McNeil, A. J. y Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach. Journal of Empirical Finance, 7(3-4): 271-300.

McNeil, A. J., Frey, R. y Embrechts, P. (2005). Quantitative risk management: Concepts, techniques, and tools. Priceton University Press.

Plitsker, M. (2001). The hidden dangerous of historical simulation. Working Paper, Board of Governors of the Federal Reserve System and University of California at Berkeley, The Federal Reserve Board, Washington, DC, USA.

Plourde, A. y Watkins, G. C. (1998). Crude oil prices between 1985 and 1994: How volatile in relation to other commodities? Resources and Energy Economics, 20(3): 245-262.

Reigner, E. (2007). Oil and energy price volatility. Energy Economics, 29(3): 405-427.

Ren, F. y Giles, D. E. (2010). Extreme value analysis of daily canadian crude oil prices. Applied Financial Economics, 20(12): 941-954.

Sadeghi, M. y Shavvalpour, S. (2006). Energy risk management and value at risk modeling. Energy Policy, 34(18): 3367-3373.

Sadorsky, P. (2005). Stochastic volatility forecasting and risk management. Applied Financial Economics, 15(2): 121-135.

Sadorsky, P. (2006). Modeling and forecasting petroleum futures volatility. Energy Economics, 28(4): 467-488.

Zhao, X., Scarrott, C., Oxley, L. y Reale, M. (2010). Extreme value modelling for forecasting market crisis impacts. Applied Financial Economics, 20(1): 63-72.

Zikovic, S. (2011). Measuring risk of crude oil at extreme quantiles. Journal of Economics and Business, 29(1): 9-31.




Cómo citar

De Jesús Gutiérrez, R., Ortiz Calisto, E., García Salgado, O., & Ángeles Morales, V. (2016). Medición del riesgo de la cola en el mercado del petróleo mexicano aplicando la teoría de valores extremos condicional. EconoQuantum, 13(2), 77–98.


Artículos más leídos del mismo autor/a