Parameter estimation of an asset price model driven by a weak hidden Markov chain
AbstractWe introduce a weak hidden Markov model (WHMM) in an attempt to capture more accurately the evolution of a risky asset. The log returns of assets are modulated by a weak or higher-order Markov chain with finite-state space. In particular, the optimal estimates of the second-order Markov chain and parameters of the model are given in terms of the discrete-time filters for the state of the Markov chain, the number of jumps, occupation time and auxiliary processes. We provide a detailed implementation of the model to a dataset of financial time series along with the analysis of the h-day ahead forecasts. The results of our error analysis suggest that within the dataset studied and considering longer predictive horizons, WHMM gives a better forecasting performance than the traditional HMM.
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Bibliographic InfoArticle provided by Elsevier in its journal Economic Modelling.
Volume (Year): 28 (2011)
Issue (Month): 1-2 (January)
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Web page: http://www.elsevier.com/locate/inca/30411
Higher-order Markov chain Filtering Regime-switching model Parameter estimation Change of reference probability technique Gaussian mixture model;
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