Please use this identifier to cite or link to this item:
|Title:||Volatility Estimation via Hidden Markov Models|
|Authors:||ROSSI ALESSANDRO; GALLO Giampiero|
|Citation:||Journal of Empirical Finance vol. 13 no. 2 p. 203-230|
|Type:||Articles in periodicals and books|
|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.|
|JRC Institute:||Space, Security and Migration|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.