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

Authors:
Kaijian He;Kin Keung Lai;Jerome Yen
Affiliations:
Business School, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China and Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Finance and Economics, Tung Wah College, Wylie Road, Kowloon, Hong Kong
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Subject to shocks worldwide, the metals markets in the era of structural changes and globalization have seen a very competitive and volatile market environment. Proper risk measurement and management in the metals markets are of critical value to the investors belonging to different parts of the economy due to their unique role as important industry inputs to the manufacturing process. Although traditional risk management methodologies have worked in the past, we are now facing the challenge of rapidly changing market conditions. Markets now demand the methodologies that estimate more reliable and accurate VaRs. This paper proposes a Multi Resolution Analysis (MRA) based nonlinear ensemble methodology for Value at Risk Estimates (MRNEVaR). The MRA using wavelet analysis is introduced to analyze the dynamic risk evolution at a finer time scale domain and provide insights into different aspects of the underlying risk evolution. The nonlinear ensemble approach using the artificial neural network technique is introduced to determine the optimal ensemble weights and stabilize the forecasts. Performances of the proposed MRNEVaR and more traditional ARMA-GARCH VaR are evaluated and compared during empirical studies in three major metals markets using Kupiec backtesting and Diebold-Mariano test procedures. Experiment results confirm that VaR estimates produced by MRNEVaR provide superior forecasts that are significantly more reliable and accurate than traditional methods.