A decoupling principle for Markov-modulated chains

Qinjing Qiu and Reiichiro Kawai

Statistics & Probability Letters, 2022, vol. 182, issue C

Abstract: Markov modulation has been widely employed in various fields of application, such as finance, economics, information and computer sciences, operations research, healthcare, and bio-medicines, whereas the additional modeling flexibility comes at the cost of demanding computation and complex inference procedure. The aim of this paper is to establish a decoupling principle for Markov-modulated chains, which enables one to represent an expectation on a Markov-modulated chain by a convergent sequence written on a set of ordinary continuous-time Markov chains. The proposed decoupling principle covers a large class of Markov-modulated chains, ranging from a variety of Markov-modulated processes to time-inhomogeneous models without time-discretization of the generator matrices, and has great potential for easing the computation around Markov-modulated chains.

Keywords: Markov-modulated processes; Markov modulation; Regime switching; Random environments; Weak approximation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spl.2021.109301

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