A wavelet approach towards examining dynamic association, causality and spillovers

Abstract

This paper presents an integrated granular framework of wavelet decomposition, DCC-GARCH, ADCC-GARCH, Diks-Panchenko nonlinear Granger's causality and Diebold-Yilmaz spillover assessment techniques to understand temporal correlation, causal interplay and spillovers among volatile financial time series data exhibiting nonparametric behavior. The exercise has been carried out on daily closing observations of eight financial time series. Wavelet decomposition has been used to generate time varying components in which the other research models are applied to extract the interactive pattern of interaction to ascertain short and long run nexus. The findings rationalize the effectiveness of the presented research framework.

Financial Sector Development and Real Sector Growth – Association, Spillover and Causality During Pre Covid and Covid Regimes

“…Several algorithms have been reported for implementing decomposition. In this research, MODWT has been used which has previously been successfully applied for modelling financial time series and is known for having several advantages over orthodox discrete wavelet transformation (DWT) (Ghosh and Datta Chaudhuri, 2019;Ghosh et al, 2021;Jana et al, 2020). The present research has utilized multi-resolution analysis using MODWT at 4 levels of decomposition considering the number of samples available for both Pre COVID and COVID time frames.…”