Abstract
Participants in the agricultural industries are subject to significant market risks due to long production lags. Traditional methodology analyzes the risk evolution following a time invariant approach. However, this paper analyzes and proposes wavelet analysis to track risk evolution in a time variant fashion. A wavelet-econometric hybrid model is further proposed for VaR estimates. The proposed wavelet decomposed VaR (WDVaR) is ex-ante in nature and is capable of estimating risks that are multi-scale structured. Empirical studies in major agricultural markets are conducted for both the hybrid ARMA-GARCH VaR and the proposed WDVaR. Experiment results confirm significant performance improvement. Besides, incorporation of time variant risks tracking capability offers additional flexibility for adaptability of the proposed hybrid algorithm to different market environments. WDVaR can be tailored to specific market characteristics to capture unique investment styles, time horizons, etc.
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© 2008 Springer-Verlag Berlin Heidelberg
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He, K., Keung Lai, K., Guu, SM., Zhang, J. (2008). A Wavelet Based Multi Scale VaR Model for Agricultural Market. In: Le Thi, H.A., Bouvry, P., Pham Dinh, T. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2008. Communications in Computer and Information Science, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87477-5_46
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DOI: https://doi.org/10.1007/978-3-540-87477-5_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87476-8
Online ISBN: 978-3-540-87477-5
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