Modeling Indian Bank Nifty volatility using univariate GARCH models

M. N. Nikhil, Suman Chakraborty, B. M. Lithin, Sanket Ledwani, Satyakam

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)127-138
Number of pages12
JournalBanks and Bank Systems
Issue number1
Publication statusPublished - 01-2023

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Organizational Behavior and Human Resource Management
  • Marketing
  • Law


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