Modelling Nifty Volatility: Application of Hybrid GARCH Models

Authors

  • Abhijit Biswas Assistant Professor, St. Xavier’s University, Kolkata
  • Arindam Das Professor, Department of Commerce, The University of Burdwan, Bardhaman
  • Anupam Mitra Professor, St. Xavier’s University, Kolkata

DOI:

https://doi.org/10.33516/maj.v58i9.76-81p

Keywords:

No Keywords

Abstract

In this study, the financial time series data on Nifty and India VIX have been used for estimating and forecasting volatility of returns on Nifty. Daily returns on Nifty are found to be negatively skewed and leptokurtic in nature. The daily return data are found to be stationary and show presence of ARCH effects. Volatility of daily return is found to be time-varying in nature. Leverage effects are found to be present and the same is reconfirmed by the shapes of the news impact curves. For evaluating the forecasting power of volatility models, an exogenous variable in the form of a lagged value of the India VIX, a proxy for implied volatility estimate, was included in the basic GARCH and GJR GARCH equations to obtain hybrid versions of these models. The experiment produced mixed results in terms of improvements in the predictive powers of the hybrid models. For the hybrid Basic-GARCH model, the predictive power improved while the predictive power weakened, though marginally, for the hybrid GJR GARCH model.

Downloads

Download data is not yet available.

Published

2024-03-23

How to Cite

Biswas, A., Das, A., & Mitra, A. (2024). Modelling Nifty Volatility: Application of Hybrid GARCH Models. The Management Accountant Journal, 58(9), 76–81. https://doi.org/10.33516/maj.v58i9.76-81p

Issue

Section

Risk Management

References

Eagle, F. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, Volume 50, Issue 4 , 987-1008

Bollerslev, T (1986). ‘‘Generalized Autoregressive Conditional Heteroscedasticity,’’ Journal of Econometrics,31: 307–27.

Glosten, L. R., Jagannathan, R. and Runkle, D. E. (1993) On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, The Journal of Finance 48(5), 1779–801

Dumas, B., Fleming, J., and Whaley, E. (2002) “Implied Volatility Functions: Empirical Tests,” The Journal of Finance

Shaikh, I. and Padhi. P. (2014). “The Forecasting Performance of Implied Volatility Index: Evidence from India VIX,” Economic Change and Restructuring, Springer, 47(4):251-274

Day, T. E. and Lewis, C. M. (1992) Stock Market Volatility and the Information Content of Stock Index Options, Journal of Econometrics 52, 267–87

Pagan and Schwert (1990). “Alternative models for conditional stock volatility,” Journal of Econometrics, vol. 45, issue 1-2, 267-290

Andersen and Bollerslev (1998). “Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts,” International Economic Review, Vol. 39, No. 4, 885-905

Risteski and Sadoghi (2013), “Improving Predicting Power of EGARCH Models for Financial Time Series Volatility by Using Google Trend” DEStech Publications

Naik, N.; Mohan, B.R. “Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market.” Mathematics 2021, 9, 1595

Similar Articles

<< < 31 32 33 34 35 36 37 38 39 40 > >> 

You may also start an advanced similarity search for this article.