—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.
—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: firstname.lastname@example.org).
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.