Hidden Markov Models can be considered as an extension of mixture models, which allows for dependent observations and makes them suitable for financial applications. In a hierarchical Bayesian framework, we show how reversible jump Markov chain Monte Carlo techniques can be used to estimate the parameters of the model, as well as the number of regimes. An application to exchange rate dynamics modeling is presented.

Bayesian hidden Markov models for financial data

CASTELLANO, Rosella;SCACCIA, LUISA
2007-01-01

Abstract

Hidden Markov Models can be considered as an extension of mixture models, which allows for dependent observations and makes them suitable for financial applications. In a hierarchical Bayesian framework, we show how reversible jump Markov chain Monte Carlo techniques can be used to estimate the parameters of the model, as well as the number of regimes. An application to exchange rate dynamics modeling is presented.
2007
9788860560209
File in questo prodotto:
File Dimensione Formato  
cladag2007Rosellashort.pdf

accesso aperto

Tipologia: Documento in post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: DRM non definito
Dimensione 112.7 kB
Formato Adobe PDF
112.7 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11393/42648
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact