• ISSN: 2301-3567
    • Frequency: Quarterly (2013-2014); Monthly (Since 2015)
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Chloe Wu
    • Abstracting/ Indexing: Engineering & Technology Library,  Electronic Journals Library, Ulrich's Periodicals Directory, MESLibrary, Google Scholar, Crossref, and ProQuest.
    • E-mail: joebm@ejournal.net
JOEBM 2015 Vol.3(5): 493-497 ISSN: 2301-3567
DOI: 10.7763/JOEBM.2015.V3.234

Modeling and Forecasting Corporate Default Counts Using Hidden Markov Model

Lu Li and Jie Cheng
Abstract—In this paper, a Hidden Markov Model is employed to fit global, U.S. and European annual corporate default counts. The Expectation-Maximization algorithm is applied to calibrate all parameters while the standard errors of the estimated parameters are conducted by Monte Carlo method. Parametric bootstraps are used to compute the nonlinear forecasts. The empirical results show that the Hidden Markov model is useful in distinguishing the periods of expansion from the periods of recession (relative to the points identified by the NBER). Moreover, it obtains relatively satisfactory forecasts especially in capturing the state switching while incorporating more original observations.

Index Terms—Corporate default counts, expectation-maximization algorithm, hidden Markov model, parametric bootstrap.

Lu Li and Jie Cheng are with the Xi’an Jiaotong-Liverpool University, China (e-mail: lu.li1002@student.xjtlu.edu.cn).

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Cite: Lu Li and Jie Cheng, "Modeling and Forecasting Corporate Default Counts Using Hidden Markov Model," Journal of Economics, Business and Management vol. 3, no. 5, pp. 493-497, 2015.

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