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�Markov Switching Models for Volatility: Filtering, Approximation and Duality�

Monica Billio () and Maddalena Cavicchioli ()
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Maddalena Cavicchioli: Department of Economics, University Of Venice C� Foscari, Italy

No 2013:24, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

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.

Keywords: Markov Switching; MS-GARCH model; MS-SV model; estimation; auxiliary model; Kalman Filter. (search for similar items in EconPapers)
JEL-codes: C01 C13 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Date: 2013
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