Estimating VaR in crude oil market: A novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network

Authors:
Kaijian He;Chi Xie;Shou Chen;Kin Keung Lai
Affiliations:
College of Business Administration, Hunan University, Changsha, Hunan 410082, China and Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;College of Business Administration, Hunan University, Changsha, Hunan 410082, China;College of Business Administration, Hunan University, Changsha, Hunan 410082, China;Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Venue:
Neurocomputing
Year:
2009

Citing 8
Cited 1

Regression neural network for error correction in foreign exchange forecasting and trading

Computers and Operations Research
On the Use of Wavelet Decomposition for String Classification

Data Mining and Knowledge Discovery
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

Computers and Operations Research
Computational intelligence approaches and linear models in case studies of forecasting exchange rates

Expert Systems with Applications: An International Journal
Review: Neural networks and statistical techniques: A review of applications

Expert Systems with Applications: An International Journal
Multivariate denoising using wavelets and principal component analysis

Computational Statistics & Data Analysis
Stable modeling of value at risk

Mathematical and Computer Modelling: An International Journal
Haar wavelet-based technique for sharp jumps classification

Mathematical and Computer Modelling: An International Journal

Ensemble forecasting of Value at Risk via Multi Resolution Analysis based methodology in metals markets

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

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.