We propose a stochastic volatility model where the conditional variance of asset returns switches across a potentially large number of discrete levels, and the dynamics of the switches are driven by a latent Markov chain. A simple parameterization overcomes the commonly encountered problem in Markov-switching models that the number of parameters becomes unmanageable when the number of states in the Markov chain increases. This framework presents some interesting features in modelling the persistence of volatility, and that, far from being constraining in data fitting, it performs comparably well as other popular approaches in forecasting short-term volatility.
Volatility Estimation via Hidden Markov Models / A. ROSSI; G. GALLO. - In: JOURNAL OF EMPIRICAL FINANCE. - ISSN 0927-5398. - STAMPA. - 13:(2006), pp. 203-230. [10.1016/j.jempfin.2005.09.003]
Volatility Estimation via Hidden Markov Models
GALLO, GIAMPIERO MARIA
2006
Abstract
We propose a stochastic volatility model where the conditional variance of asset returns switches across a potentially large number of discrete levels, and the dynamics of the switches are driven by a latent Markov chain. A simple parameterization overcomes the commonly encountered problem in Markov-switching models that the number of parameters becomes unmanageable when the number of states in the Markov chain increases. This framework presents some interesting features in modelling the persistence of volatility, and that, far from being constraining in data fitting, it performs comparably well as other popular approaches in forecasting short-term volatility.File | Dimensione | Formato | |
---|---|---|---|
rossigallo_joEF_print.pdf
Accesso chiuso
Tipologia:
Versione finale referata (Postprint, Accepted manuscript)
Licenza:
Tutti i diritti riservati
Dimensione
325.68 kB
Formato
Adobe PDF
|
325.68 kB | Adobe PDF | Richiedi una copia |
I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.