Nonlinear deterministic forecasting of noisy financial time series: Does noise reduction matter?

Abdol Soofi and Liangyue Cao

No PO9, CeNDEF Workshop Papers, January 2001 from Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance

Abstract: Two series, German mark/US dollar exchange rate and US consumer price index, are tested to illustrate if nonlinear noise reduction could help to improve prediction. Three nonlinear noise reduction methods, local projective (LP), singular value decomposition (SVD) and simple nonlinear filtering (SNL), are used to generate the filtered time series. Different projection dimensions of the noise reduction methods are also selected for the sensitivity test on the prediction results. The results show that noise reduction does help in improving prediction in both of the examples providing that an appropriate method of noise reduction and suitable parameter values for the method are used.

Date: 2001-01-04
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