Modeling Indian Bank Nifty volatility using univariate GARCH models

  • 868 Views
  • 262 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

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.

view full abstract hide full abstract
    • Figure 1. The trend (Closing prices) and log-returns of the Bank NIFTY index during the sample period
    • Figure 2. Histogram for Bank Nifty Index returns
    • Figure 3. Normal QQ plot for daily Bank Nifty Index returns: 2005–2022
    • Table 1. Unit root test results
    • Table 2. ARCH LM test results
    • Table 3. Results showing summary statistics
    • Table 4. Estimation of results using GARCH models
    • Table 5. Model selection criteria
    • Conceptualization
      Nikhil M. N., Lithin B. M., Sanket Ledwani
    • Data curation
      Nikhil M. N., Lithin B. M., Sanket Ledwani
    • Formal Analysis
      Nikhil M. N., Suman Chakraborty, Satyakam
    • Methodology
      Nikhil M. N., Lithin B. M., Sanket Ledwani
    • Software
      Nikhil M. N., Lithin B. M., Sanket Ledwani
    • Writing – original draft
      Nikhil M. N., Lithin B. M., Sanket Ledwani
    • Funding acquisition
      Suman Chakraborty
    • Investigation
      Suman Chakraborty, Satyakam
    • Project administration
      Suman Chakraborty, Satyakam
    • Resources
      Suman Chakraborty, Satyakam
    • Supervision
      Suman Chakraborty, Satyakam
    • Validation
      Suman Chakraborty, Lithin B. M., Sanket Ledwani
    • Visualization
      Suman Chakraborty, Satyakam
    • Writing – review & editing
      Suman Chakraborty, Satyakam