Estimating VaR in crude oil market: A novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network |
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Authors: | Kaijian Chi Shou Kin Keung |
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Affiliation: | aCollege of Business Administration, Hunan University, Changsha, Hunan 410082, China;bDepartment of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong |
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Abstract: | Facing the complicated non-linear nature of risk evolutions, current risk measurement approaches offer insufficient explanatory power and limited performance. Thus this paper proposes wavelet decomposed non-linear ensemble value at risk (WDNEVaR), a novel semi-parametric paradigm, incorporating both, wavelet analysis and artificial neural network technique to further improve the modeling accuracy and reliability. Wavelet analysis is utilized to capture the multi-scale data characteristics across scales while artificial neural network technique is utilized to reduce estimation biases following non-linear ensemble algorithms. Experiment results in three major markets suggest that the proposed WDNEVaR is superior to more traditional approaches as it provides value at risk (VaR) estimates at higher reliability and accuracy. |
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Keywords: | Multi-resolution analysis Wavelet analysis Non-linear ensemble algorithm Neural networks Value at risk |
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