Wavelet Neural Network Model for Yield Spread Forecasting

Firdous Ahmad Shah and Lokenath Debnath
Additional contact information
Firdous Ahmad Shah: Department of Mathematics, University of Kashmir, South Campus, Anantnag-192 101, Jammu and Kashmir, India
Lokenath Debnath: School of Mathematical and Statistical Sciences, University of Texas–Rio Grande Valley, Edinburg, TX 78539, USA

Mathematics, 2017, vol. 5, issue 4, 1-15

Abstract: 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.

Keywords: wavelet; neural network; wavelet neural network (WNN); forecasting; yield spread (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/5/4/72/pdf (application/pdf)
https://www.mdpi.com/2227-7390/5/4/72/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:5:y:2017:i:4:p:72-:d:120564

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().