Currency Crisis Prediction Using Wavelet-Based Support Vector Machine

Abstract:

A currency crisis is typically a kind of rare event, but it hurts sustainable economic development when it occurs. A novel method of wavelet-based support vector machine (SVM) is proposed to predict financial crisis events for early-warning purposes in this paper. In the proposed method, currency exchange rate, a typical currency indicator that usually reflects economic fluctuations, is first chosen. Then a wavelet decomposition algorithm is applied to the currency exchange rate series. Using the wavelet decomposition procedure, some details and features of the currency exchange rate series, with different scales, can be obtained. Using these details and features, a wavelet-based SVM learning paradigm is used to predict future currency crisis events, based upon some historical data. For illustration purpose, the proposed wavelet-based SVM learning paradigm is applied to exchange rate data of two Asian countries to evaluate the state of currency crisis. Experimental results reveal that the proposed wavelet-based SVM learning paradigm can significantly improve the generalization performance relative to some popular forecasting methods.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2560-2564
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2560
• Online since: July 2014
• Authors: Qin Li Zhang*
• Keywords: Currency Crisis, Support Vector Machine (SVM), Wavelet

References

[1] Lin, C.S., Khan, H.A., Chang, R.Y., Wang, Y.C.: Journal of International Money and Finance, Vol. 27(2008), p.1098.

[2] Dridi, A., El Ghourabi, M., Limam, M.: Quantitative Finance, Vol. 12(2012), p.329.

[3] Phornchanok Cumperayot, Roy Kouwenberg: Journal of International Money and Finance, Vol. 36 (2013), p.151.

[4] Vapnik VN. The nature of statistical learning theory (Springer-Verlag, New York 1995).

[5] Y. Liang, E.W. Page: IEEE Transactions on Signal Processing, Vol. 45(1997), p.2858.

[6] J. Sun and H. Li: Applied Soft Computing, Vol. 12(2012), p.2254.

[7] Gallegati, M.: Computational Statistics & Data Analysis, Vol. 52(2008), p.3067.

[8] Wu K-P, Wang S-D. : Pattern Recognition, Vol. 42(2009), p.710.

[9] L. Yu, K.K. Lai, S.Y. Wang : International Journal of Information Technology and Decision Making, Vol. 5(2006), p.437.

[10] G.L. Kaminsky, C.M. Reinhart: American Economic Review, Vol. 89(1999), p.473.