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A Flexible State-Space Model with Application to Stochastic Volatility

Author

Listed:
  • Christian Gouriéroux

    (CREST and University of Toronto)

  • Yang Lu

    (Aix-Marseille University)

Abstract

We introduce a general state-space (or latent factor) model for time series and panel data. The state process has a polynomial expansion based dynamics that can approximate any Markov dynamics arbitrarily well, and has a latent, endogenous switching regime interpretation. The resulting state-space model is associated with simulation-free, recursive formulas for prediction and ltering, as well as the maximum composite likelihood estimation method, with an extremely low computational cost. When applied to the stochastic volatility (SV) of asset returns, the model can capture, in a uni ed framework, stylized facts such as heavy tailed return, volatility feedback, as well as time irreversibility. The methodology is illustrated using Apple stock return data, which con rms the improvement of our model with respect to a benchmark SV model.

Suggested Citation

  • Christian Gouriéroux & Yang Lu, 2016. "A Flexible State-Space Model with Application to Stochastic Volatility," Working Papers 2016-39, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2016-39
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    More about this item

    Keywords

    Endogenous regime switching; polynomial expansion; composite likelihood; time irreversibility; volatility feedback; copula.;
    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
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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