Parameter estimation of an asset price model driven by a weak hidden Markov chain

Xiaojing Xi and Rogemar Mamon

Economic Modelling, 2011, vol. 28, issue 1, 36-46

Abstract: We 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.

Keywords: Higher-order Markov chain; Filtering; Regime-switching model; Parameter estimation; Change of reference probability technique; Gaussian mixture model (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:28:y:2011:i:1:p:36-46

DOI: 10.1016/j.econmod.2010.10.002

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