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.