In Search of Leading Indicator Property of Yield Spread for India: An Approach Based on Quantile and Wavelet Regression

Abstract

The leading indicator ability of yield spread for future output growth and inflation is tested for India. Using the yields on securities with maturities ten years and three months to construct yield spread, we study the predictive power of yield spread for output growth and inflation. Our results based on regression of future inflation and output on yield spreads indicate that there is no information in the yield spread about future economic activity and inflation in India. Further, the predictive power of yield spread is analyzed over different quantiles of inflation and output growth using quantile regression; we find that there is again no evidence of predictive information in the yield spreads. Using multiscale wavelet based regression, predictive power is however unveiled at higher time scales for output growth only.

Wavelet Neural Network Model for Yield Spread Forecasting

“…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. Recent results in this direction can be found in Tabak and Feitosa [8,9] and Dar and Shah [26]. Therefore, in order to increase the result accuracy and better estimation of yield spread peaks, which is the most and important part in forecast modelling, we develop a hybrid WNN model to test the leading indicator property of yield spreads for India.…”

“…The actual derivation of the smooth and detailed coefficients may be done via the so-called discrete wavelet transform (DWT), which can be computed in several alternative ways. The intuitively most appealing procedure is the pyramid algorithm, suggested in Mallat [26,29] (and fully explained in Percival and Walden [30]). In this context, the Daubechies family of wavelets is very useful because the mother wavelets in this family have compact support.…”