Modeling Indian Bank Nifty volatility using univariate GARCH models

Nikhil M N, Suman Chakraborty, Lithin B M and Sanket Ledwani

MPRA Paper from University Library of Munich, Germany

Abstract: The crumble of financial markets due to the recent crises has wobbled precariousness in the stock market and intensified the returns vulnerability of banking indices. Against this backdrop, this study intends to model the volatility of the Indian Bank Nifty returns using a battery of GARCH specifications. The finding of the present research contributes to the literature in three ways. First, volatility during the sample period, which corresponds to a time of stress (a bear market), is more persistent, with an estimated coefficient of 0.995695. Moreover, when volatility rises, it persists for a long time before returning to the mean in an average of 16 days. Second, for a positive γ, the results insinuate the possibility of an “anti-leverage effect” with a coefficient of 0.139638. Thus, the volatility of the Bank Nifty returns tends to rise in response to positive shocks relative to negative shocks of equal magnitude in India. Finally, the findings demonstrate that EGARCH with Student’s t-distribution offers lower forecast errors in modeling conditional volatility.

Keywords: anti-leverage; asymmetry; bank nifty; GARCH; index returns; Indian stock; leverage; return volatility (search for similar items in EconPapers)
JEL-codes: C22 C52 G10 G17 (search for similar items in EconPapers)
Date: 2022-10-11, Revised 2023-02-06
New Economics Papers: this item is included in nep-rmg
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Published in Banks and Bank Systems 1.18(2023): pp. 127-138

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