�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"
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
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