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Markov Switching Models for Volatility: Filtering, Approximation and Duality

Author

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  • Monica Billio

    () (Department of Economics, University Of Venice C� Foscari, Italy)

  • Maddalena Cavicchioli

    () (Department of Economics, University Of Venice C� Foscari, Italy)

Abstract

This paper is devoted to show duality in the estimation of Markov Switching (MS) processes for volatility. It is well-known that MS-GARCH models suffer of path dependence which makes the estimation step unfeasible with usual Maximum Likelihood procedure. However, by rewriting the MS-GARCH model in a suitable linear State Space representation, we are able to give a unique framework to reconcile the estimation obtained by the Kalman Filter and with some auxiliary models proposed in the literature. Reasoning in the same way, we present a linear Filter for MS-Stochastic Volatility (MS-SV) models on which different conditioning sets yield more flexibility in the estimation. Estimation on simulated data and on short-term interest rates shows the feasibility of the proposed approach.

Suggested Citation

  • Monica Billio & Maddalena Cavicchioli, 2013. "Markov Switching Models for Volatility: Filtering, Approximation and Duality," Working Papers 2013:24, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2013:24
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    References listed on IDEAS

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    More about this item

    Keywords

    Markov Switching; MS-GARCH model; MS-SV model; estimation; auxiliary model; Kalman Filter.;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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