Modeling VaR in Crude Oil Market: A Multi Scale Nonlinear Ensemble Approach Incorporating Wavelet Analysis and ANN

  • Authors:

  • Kin Keung Lai;Kaijian He;Jerome Yen

  • Affiliations:

  • Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Hong Kong and School of Business Administration, Hunan University, Changsha, Hunan, 410082, China;Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Hong Kong and School of Business Administration, Hunan University, Changsha, Hunan, 410082, China;Department of Finance, Hong Kong University of Science and Technology, Hong Kong

  • Venue:
  • ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
  • Year:
  • 2007

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Abstract

Price fluctuations in the crude oil markets worldwide have attracted significant attentions from both, industries and academics, due to their profound impact on businesses and governments. Proper measurement and management of risks due to unexpected price movements in the markets has been crucial from both, operational and strategic perspectives. However, risk measurements from current approaches offer insufficient explanatory power and performance due to the complicated non-linear nature of risk evolutions. This paper adopts a VaR approach to measure risks and proposes multi-scale non-linear ensemble approaches to model the risk evolutions in WTI crude oil market. The proposed WDNEVaR follows a semi-parametric paradigm, incorporating both, wavelet analysis and artificial neural network techniques. Experiment results from empirical studies suggest that the proposed WDNEVaR is superior to traditional approaches. It provides VaR estimates of higher reliability and accuracy. It also brings significantly more flexibility during the modeling attempts.