Wavelet decomposition approach for understanding time-varying relationship of financial sector variables: a study of the Indian stock market

• Authors: Ghosh Indranil , Chaudhuri Tamal Datta
• Languages of publication: EN

Abstracts

EN

In this paper, we study the effect of overall stock market sentiment in India on sectoral indices and on individual stock prices in terms of co-movement, dependence and volatility transmission along with the magnitude and persistence of the effects. The study uses wavelet decomposition framework for breaking down different financial time series into time-varying components. Quantile Regression, Wavelet Multiple Correlation and Cross-Correlation analysis, and Diebold-Yilmaz spillover analysis are then applied to investigate the nature of dependence, association, and spillover dynamics. For further focus, we have considered different time periods separately to identify the effect of market phases. Interesting results are obtained with respect to persistence of shocks, both across and within time periods. These have implications with respect to understanding market behavior and also perception of sectors and stocks.

Keywords

EN

Diebold-Yilmaz Spillover; Quantile Regression; SENSEX; Wavelet Decomposition; Wavelet Multiple Correlation; Wavelet Multiple Cross Correlation

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Multiple Criteria Decision Making
• Year: 2020
• Volume: 15
• Pages: 36-65

Contributors

author

Ghosh Indranil

• IT & Analytics Area, Institute of Management Technology Hyderabad, Shamshabad,

author

Chaudhuri Tamal Datta

• Centre for Knowledge, Ideas and Development Studies, KnIDS, Kolkata, India

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Identifiers

ISSN

2084-1531