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Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model

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DOI: 10.4236/ajor.2014.44023    2,768 Downloads   3,795 Views   Citations

ABSTRACT

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models; however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Ouenniche, J. , Xu, B. and Tone, K. (2014) Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model. American Journal of Operations Research, 4, 235-245. doi: 10.4236/ajor.2014.44023.

References

[1] Xu, B. and Ouenniche, J. (2012) A Data Envelopment Analysis-Based Framework for the Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models. Energy Economics, 34, 576-583.
http://dx.doi.org/10.1016/j.eneco.2011.12.005
[2] Hamilton, J.D. (2008) Oil and the Macroeconomy. In: Durlauf, S. and Blume, L., Eds., The New Palgrave Dictionary of Economics, Palgrave MacMillan.
http://dx.doi.org/10.1057/9780230226203.1215
[3] Kilian, L. (2008) The Economic Effects of Energy Price Shocks. Journal of Economic Literature, 46, 871-909.
http://dx.doi.org/10.1257/jel.46.4.871
[4] Sadorsky, P. (2005) Stochastic Volatility Forecasting and Risk Management. Applied Financial Economics, 15, 121-135. http://dx.doi.org/10.1080/0960310042000299926
[5] Sadorsky, P. (2006). Modelling and forecasting petroleum futures volatility. Energy Economics, 28, 467-488.
http://dx.doi.org/10.1016/j.eneco.2006.04.005
[6] Agnolucci, P. (2009) Volatility in Crude Oil Futures: A Comparison of the Predictive Ability of GARCH and Implied Volatility Models. Energy Economics, 31, 316-321.
http://dx.doi.org/10.1016/j.eneco.2008.11.001
[7] Marzo, M. and Zagaglia, P. (2010) Volatility Forecasting for Crude Oil Futures. Applied Economics Letter, 17, 1587-1599.
http://dx.doi.org/10.1080/13504850903084996
[8] Andersen, P. and Petersen, N.C. (1993) A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science, 39, 1261-1294.
http://dx.doi.org/10.1287/mnsc.39.10.1261
[9] Banker, R.D., Charnes, A. and Cooper, W.W. (1984) Models for the Estimation of Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30, 1078-1092.
http://dx.doi.org/10.1287/mnsc.30.9.1078
[10] Charnes, A., Cooper, W.W. and Rhodes, E. (1978) Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2, 429-444.
http://dx.doi.org/10.1016/0377-2217(78)90138-8
[11] Tone, K. (2001) A Slacks-Based Measure of Efficiency in Data Envelopment Analysis. European Journal of Operational Research, 130, 498-509.
http://dx.doi.org/10.1016/S0377-2217(99)00407-5
[12] Kang, S.H., Kang, S.M. and Yoon, S.M. (2009) Forecasting Volatility of Crude Oil Markets. Energy Economics, 31, 119-125.
http://dx.doi.org/10.1016/j.eneco.2008.09.006
[13] Wang, Y.D. and Wu, C.F. (2012) Forecasting Energy Market Volatility Using GARCH Models: Can Multivariate Models Beat Univariate Models? Energy Economics, 34, 2167-2181.
http://dx.doi.org/10.1016/j.eneco.2012.03.010
[14] Day, T.E. and Lewis, C.M. (1993). Forecasting Futures Market Volatility. The Journal of Derivatives, 1, 33-50.
http://dx.doi.org/10.3905/jod.1993.407876
[15] Fong, W.M. and See, K.H. (2002) A Markov Switching Model of the Conditional Volatility of Crude Oil Futures Prices. Energy Economics, 24, 71-95.
http://dx.doi.org/10.1016/S0140-9883(01)00087-1
[16] Nomikos, N.K. and Pouliasis, P.K. (2011) Forecasting Petroleum Futures Markets Volatility: The Role of Regimes and Market Conditions. Energy Economics, 33, 321-337.
http://dx.doi.org/10.1016/j.eneco.2010.11.013
[17] Ghysels, E., Harvey, A. and Renault, E. (1996) Stochastic Volatility. In: Maddala, G.S. and Rao, C.R., Eds., Handbook of Statistics 14: Statistical Methods in Finance, Elsevier Science, Amsterdam.
[18] Poon, S.H. and Granger, C.W.J. (2003) Forecasting Financial Market Volatility: A Review. Journal of Economic Literature, 41, 478-539.
http://dx.doi.org/10.1257/jel.41.2.478
[19] Charnes, A. and Cooper, W.W. (1962) Programming with Linear Fractional Functionals. Naval Research Logistics Quarterly, 15, 333-334.
[20] Johnson, A.L. and Ruggiero, J. (2012) Nonparametric Measurement of Productivity and Efficiency in Education. Annals of Operations Research, 194, 1-14.
http://dx.doi.org/10.1007/s10479-011-0880-9
[21] Korhonen, P.J. and Syrjanen, M.J. (2003) Evaluation of Cost Efficiency in Finnish Electricity Distribution. Annals of Operations Research, 121, 105-122.
http://dx.doi.org/10.1023/A:1023355202795
[22] Ozcan, Y.A., Lins, M.E., Lobo, M.S.C., da Silva, A.C.M., Fiszman, R. and Pereira, B.B. (2010) Evaluating the Performance of Brazilian University Hospitals. Annals of Operations Research, 178, 247-261.
http://dx.doi.org/10.1007/s10479-009-0528-1
[23] Seiford, L.M. (1997) A Bibliography for Data Envelopment Analysis (1978-1996). Annals of Operations Research, 73, 393-438. http://dx.doi.org/10.1023/A:1018949800069
[24] Cooper, W.W., Seiford, L.M. and Tone, K. (2007) Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References. 2nd Edition, Springer, New York.
[25] Liu, J.S., Lu, L.Y., Lu, W.M. and Lin, B.J. (2013) A Survey of DEA Applications. Omega, 41, 893-902.
http://dx.doi.org/10.1016/j.omega.2012.11.004
[26] Banker, R.D., Cooper, W.W., Seiford, L.M., Thrall, R.M. and Zhu, J. (2004) Returns to Scale in Different DEA Models. European Journal of Operational Research, 154, 345-362.
http://dx.doi.org/10.1016/S0377-2217(03)00174-7
[27] Tone, K. (2002) A Slacks-Based Measure of Super-Efficiency in Data Envelopment Analysis. European Journal of Operational Research, 143, 32-41.
http://dx.doi.org/10.1016/S0377-2217(01)00324-1
[28] Du, J., Liang, L. and Zhu, J. (2010) A Slacks-Based Measure of Super-Efficiency in Data Envelopment Analysis: A Comment. European Journal of Operational Research, 204, 694-697.
http://dx.doi.org/10.1016/j.ejor.2009.12.007
[29] Andersen, T.G. and Bollerslev, T. (1998) Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts. International Economic Review, 39, 885-905.
http://dx.doi.org/10.2307/2527343

  
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