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.
Highlights
Forecasting the behaviour of macro and financial variables is of utmost importance to the financial and macroeconomic policy makers
We find that there is a significant evidence of the leading indicator property of the yield spreads, which are constructed at the shorter end and policy relevant areas of the yield curve; spreads that are constructed at the longer end of the yield curve do not have predictive information for output growth
There is a scant but significant research that is available for finding the predictive power of term spread for economic activity using wavelet approach, for example Zagaglia [25] used this methodology to find the predictive power of term spread for the USA and his results report a heterogeneous relation across time scales
Summary
Forecasting the behaviour of macro and financial variables is of utmost importance to the financial and macroeconomic policy makers. The difference between short and longer-term interest rates may contain useful predictive information about future economic activity in any economy. Bordo and Haubrich [6] test the predictive information in the level and slope of yield curve and find that both the level and slope of yield curve has information about economic activity All of these results have found support in studies, like as Stock and Watson [7], and Tabak and Feitosa [8,9]. We employ a hybrid wavelet neural network (WNN) approach to test the ability of the yield spreads to predict economic growth in India. We find that there is a significant evidence of the leading indicator property of the yield spreads, which are constructed at the shorter end and policy relevant areas of the yield curve; spreads that are constructed at the longer end of the yield curve do not have predictive information for output growth.
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