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Assessing Markov property in multistate transition models with applications to credit risk modeling
Author(s) -
Yang Hanyu,
Nair Vijayan N.,
Chen Jie,
Sudjianto Agus
Publication year - 2018
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2336
Subject(s) - markov chain , computer science , markov model , markov process , markov property , variable order markov model , econometrics , null hypothesis , process (computing) , sample (material) , sample size determination , machine learning , mathematics , statistics , chemistry , chromatography , operating system
Abstract Multistate transition models are increasingly used in credit risk applications as they allow us to quantify the evolution of the process among different states. If the process is Markov, analysis and prediction are substantially simpler, so analysts would like to use these models if they are applicable. In this paper, we develop a procedure for assessing the Markov hypothesis and discuss different ways of implementing the test procedure. One issue when sample size is large is that the statistical test procedures will detect even small deviations from the Markov model when these differences are not of practical interest. To address this problem, we propose an approach to formulate and test the null hypothesis of “weak non‐Markov.” The situation where the transition probabilities are heterogeneous is also examined, and approaches to accommodate this case are indicated. Simulation studies are used extensively to study the properties of the procedures, and two applications are to illustrate the results.