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Open AccessFeature PaperArticle

Wavelet Neural Network Model for Yield Spread Forecasting

1
Department of Mathematics, University of Kashmir, South Campus, Anantnag-192 101, Jammu and Kashmir, India
2
School of Mathematical and Statistical Sciences, University of Texas–Rio Grande Valley, Edinburg, TX 78539, USA
*
Author to whom correspondence should be addressed.
Mathematics 2017, 5(4), 72; https://doi.org/10.3390/math5040072
Received: 28 September 2017 / Revised: 14 November 2017 / Accepted: 20 November 2017 / Published: 27 November 2017
(This article belongs to the Special Issue Recent Developments in Wavelet Transforms and Their Applications)
In this study, a hybrid method based on coupling discrete wavelet transforms (DWTs) and artificial neural network (ANN) for yield spread forecasting is proposed. The discrete wavelet transform (DWT) using five different wavelet families is applied to decompose the five different yield spreads constructed at shorter end, longer end, and policy relevant area of the yield curve to eliminate noise from them. The wavelet coefficients are then used as inputs into Levenberg-Marquardt (LM) ANN models to forecast the predictive power of each of these spreads for output growth. We find that the yield spreads constructed at the shorter end and policy relevant areas of the yield curve have a better predictive power to forecast the output growth, whereas the yield spreads, which are constructed at the longer end of the yield curve do not seem to have predictive information for output growth. These results provide the robustness to the earlier results. View Full-Text
Keywords: wavelet; neural network; wavelet neural network (WNN); forecasting; yield spread wavelet; neural network; wavelet neural network (WNN); forecasting; yield spread
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MDPI and ACS Style

Shah, F.A.; Debnath, L. Wavelet Neural Network Model for Yield Spread Forecasting. Mathematics 2017, 5, 72.

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