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Gamma stochastic volatility models

Author

Listed:
  • N. Balakrishna

    (Cochin University of Science and Technology, India)

  • Bovas Abraham

    (University of Waterloo, Canada)

  • Ranjini Sivakumar

    (University of Waterloo, Canada)

Abstract

This paper presents gamma stochastic volatility models and investigates its distributional and time series properties. The parameter estimators obtained by the method of moments are shown analytically to be consistent and asymptotically normal. The simulation results indicate that the estimators behave well. The in-sample analysis shows that return models with gamma autoregressive stochastic volatility processes capture the leptokurtic nature of return distributions and the slowly decaying autocorrelation functions of squared stock index returns for the USA and UK. In comparison with GARCH and EGARCH models, the gamma autoregressive model picks up the persistence in volatility for the US and UK index returns but not the volatility persistence for the Canadian and Japanese index returns. The out-of-sample analysis indicates that the gamma autoregressive model has a superior volatility forecasting performance compared to GARCH and EGARCH models. Copyright © 2006 John Wiley _ Sons, Ltd.

Suggested Citation

  • N. Balakrishna & Bovas Abraham & Ranjini Sivakumar, 2006. "Gamma stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(3), pages 153-171.
    • Handle: RePEc:jof:jforec:v:25:y:2006:i:3:p:153-171
    DOI: 10.1002/for.982
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    References listed on IDEAS

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    2. Roberto León-González, 2019. "Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 899-920, September.
    3. Roland Langrock & Théo Michelot & Alexander Sohn & Thomas Kneib, 2015. "Semiparametric stochastic volatility modelling using penalized splines," Computational Statistics, Springer, vol. 30(2), pages 517-537, June.
    4. Raanju R. Sundararajan & Wagner Barreto‐Souza, 2023. "Student‐t stochastic volatility model with composite likelihood EM‐algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 125-147, January.
    5. Božidar V. Popović & Miroslav M. Ristić & Narayana Balakrishna, 2017. "A mixed stationary autoregressive model with exponential marginals," Statistical Papers, Springer, vol. 58(4), pages 1125-1148, December.

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