Estimation procedure for a hidden Markov chain model with applications to finance, climate data and earthquake analysis
نویسندگان
چکیده
In this work we present a methodology for estimating the variability of a signal modeled as a continuous time stochastic process observable only at discrete times with variability modeled by a continuous time Markov chain. The methodology estimates the parameters of the hidden Markov chain. The methodology is new however the major contribution of this work comes in the realm of applications. The methodology is applied to three domains: Finance (high frequency data), Climate studies (temperature data) and Geophysics (earthquake data). In all the applications the methodology provides insight into features of the signal which are hard to detect otherwise.
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