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Kalman filter-based modelling and forecasting of stochastic volatility with threshold

Author

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  • Himadri Ghosh
  • Bishal Gurung
  • Prajneshu

Abstract

We propose a parametric nonlinear time-series model, namely the Autoregressive-Stochastic volatility with threshold (AR-SVT) model with mean equation for forecasting level and volatility. Methodology for estimation of parameters of this model is developed by first obtaining recursive Kalman filter time-update equation and then employing the unrestricted quasi-maximum likelihood method. Furthermore, optimal one-step and two-step-ahead out-of-sample forecasts formulae along with forecast error variances are derived analytically by recursive use of conditional expectation and variance. As an illustration, volatile all-India monthly spices export during the period January 2006 to January 2012 is considered. Entire data analysis is carried out using EViews and matrix laboratory (MATLAB) software packages. The AR-SVT model is fitted and interval forecasts for 10 hold-out data points are obtained. Superiority of this model for describing and forecasting over other competing models for volatility, namely AR-Generalized autoregressive conditional heteroscedastic, AR-Exponential GARCH, AR-Threshold GARCH, and AR-Stochastic volatility models is shown for the data under consideration. Finally, for the AR-SVT model, optimal out-of-sample forecasts along with forecasts of one-step-ahead variances are obtained.

Suggested Citation

  • Himadri Ghosh & Bishal Gurung & Prajneshu, 2015. "Kalman filter-based modelling and forecasting of stochastic volatility with threshold," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 492-507, March.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:492-507
    DOI: 10.1080/02664763.2014.963524
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    References listed on IDEAS

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    Cited by:

    1. Zea Bermudez, Patrícia de & Marín Díazaraque, Juan Miguel & Rue, Havard & Lopes Moreira Da Veiga, María Helena, 2021. "Integrated nested Laplace approximations for threshold stochastic volatility models," DES - Working Papers. Statistics and Econometrics. WS 31804, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. P. de Zea Bermudez & J. Miguel Marín & Helena Veiga, 2020. "Data cloning estimation for asymmetric stochastic volatility models," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 1057-1074, November.
    3. Mao, Xiuping & Ruiz, Esther & Veiga, Helena, 2017. "Threshold stochastic volatility: Properties and forecasting," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1105-1123.

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