A Wavelet-based MRA-EDCC-GARCH Methodology for the Detection of News and Volatility Spillover across Sectoral Indices—Evidence from the Indian Financial Market. (February 2015)

• Record Type: Journal Article
• Authors: Chakrabarty, Anindya; De, Anupam; Bandyopadhyay, Gautam
• Is Part Of: Global business review. Volume 16:Number 1(2015)
• Page Start: 35
• Page End: 49
• Publication Date: 2015-02
• Subjects: Volatility spillovers -- DCC-GARCH -- wavelet analysis -- multi-resolution analysis (MRA); Economic history -- 1990- -- Periodicals; Industrial management -- Periodicals; Business -- Periodicals; Commerce -- Periodicals; 650.05
• DOI: 10.1177/0972150914553506
• Languages: English
• ISSNs: 0972-1509

Abstract

The article studies the nature and direction of shock and volatility transmission among the nine non-overlapping sectoral indices of Bombay Stock Exchange (BSE) across eight different scales (from 2–4 days to 1–2 years) using a newly developed wavelet-based multi-resolution–extended dynamic conditional correlation GARCH (MRA–EDCC GARCH) model and compared the results with that of the traditional vector-auto regression–extended dynamic conditional correlation GARCH (VAR–EDCC GARCH) model. The study reveals that the volatility interaction is scale dependent. Significant variation in the magnitude and direction of the spillover incidences are observed between the results of the two models which elucidates that the traditional VAR–EDCC GARCH model may not be sufficient in unlocking the complex pattern of volatility interaction and the multiscale analysis can be further used to extract the hidden information. Shock spillover incidences are found to decrease with scale while the volatility spillover is found to vary both in magnitude and direction across scales. Previous literatures have established that volatility interaction among financial assets can be leveraged successfully in designing trading strategies that generates better results in comparison to the trading strategies that does not employ volatility interactions in their model. Given the findings that the magnitude and direction of volatility interaction changes with the change in investment horizon, it can be concluded that a strategy calibrated for short-term traders may not be optimal for long-term traders and vice versa.