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Modelling Stock Market Volatility: Evidence from India

Author

Listed:
  • Karunanithy Banumathy

    (Pondicherry Central University, India)

  • Ramachandran Azhagaiah

    (Pondicherry Central University, India)

Abstract

This study empirically investigates the volatility pattern of Indian stock market based on time series data which consists of daily closing prices of S&P CNX Nifty Index for ten years period from 1st January 2003 to 31st December 2012. The analysis has been done using both symmetric and asymmetric models of Generalized Autoregressive Conditional Heteroscedastic (GARCH). As per Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC), the study proves that GARCH (1,1) and TGARCH (1,1) estimations are found to be most appropriate model to capture the symmetric and asymmetric volatility respectively. The study also provides evidence for the existence of a positive and insignificant risk premium as per GARCH-M (1,1) model. The asymmetric effect (leverage) captured by the parameter of EGARCH (1,1) and TGARCH (1,1) models show that negative shocks have significant effect on conditional variance (volatility).

Suggested Citation

  • Karunanithy Banumathy & Ramachandran Azhagaiah, 2015. "Modelling Stock Market Volatility: Evidence from India," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 13(1 (Spring), pages 27-41.
    • Handle: RePEc:mgt:youmgt:v:13:y:2015:i:1:p:27-41
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    References listed on IDEAS

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    3. Schwert, G William, 1990. "Stock Volatility and the Crash of '87," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 77-102.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Shekar Bose & Hafizur Rahman, 2022. "Are News Effects Necessarily Asymmetric? Evidence from Bangladesh Stock Market," SAGE Open, , vol. 12(4), pages 21582440221, October.
    2. Neifar, Malika, 2020. "Stock Market Volatility Analysis: A Case Study of TUNindex," MPRA Paper 99140, University Library of Munich, Germany.
    3. Novy Ann M. Etac & Roel F. Ceballos, 2019. "Forecasting the Volatilities of Philippine Stock Exchange Composite Index Using the Generalized Autoregressive Conditional Heteroskedasticity Modeling," Papers 1904.00749, arXiv.org.
    4. Bonga, Wellington Garikai, 2019. "Stock Market Volatility Analysis using GARCH Family Models: Evidence from Zimbabwe Stock Exchange," MPRA Paper 94201, University Library of Munich, Germany.
    5. Amanjot SINGH, 2017. "Modeling Conditional Volatility Of Indian Banking Sector’S Stock Market Returns," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 64(3), pages 325-338, September.
    6. Aluko Olufemi Adewale & Adeyeye Patrick Olufemi & Migiro Stephen Oseko, 2017. "Modelling Volatility Persistence and Asymmetry with Structural Break: Evidence from the Nigerian Stock Market," Journal of Economics and Behavioral Studies, AMH International, vol. 8(6), pages 153-160.

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    More about this item

    Keywords

    asymmetric volatility; conditional volatility; GARCH models and leverage effect;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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