Infinite-state Markov-switching for dynamic volatility and correlation models

Arnaud Dufays

No 2012043, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: Dynamic volatility and correlation models with fixed parameters are restrictive for time series subject to breaks. GARCH and DCC models with changing parameters are specified using the sticky infinite hidden Markov-chain framework. Estimation by Bayesian inference determines the adequate number of regimes as well as the optimal specification (Markov-switching or change-point). The new estimation algorithm is studied in terms of mixing properties and computational time. Applications highlight the flexibility of the model.

Keywords: Bayesian inference; Markov-switching; GARCH; DCC; infinite hidden Markov model; Dirichlet process (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C58 (search for similar items in EconPapers)
Date: 2012-11-22
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2012043

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