Mixture additive hazards cure model with latent variables: Application to corporate default data

Qi Yang, Haijin He, Bin Lu and Xinyuan Song

Computational Statistics & Data Analysis, 2022, vol. 167, issue C

Abstract: A mixture additive hazards cure model with latent variables is proposed to investigate the risk factors of the corporate default issue with a sample of corporate bonds from the Chinese financial market. The proposed model combines confirmatory factor analysis, additive hazards, and cure models to characterize latent attributes, such as profitability, liquidity, and operating capacity, through multiple manifest variables and investigate the effects of observed covariates and latent factors on the hazards of corporate default and the probability of nonsusceptibility to default. An expectation-maximization algorithm is developed to conduct statistical inference. The satisfactory performance of the suggested method is demonstrated by simulation studies. Application to the corporate default data illustrates the utility of the proposed methodology and its superiority over conventional methods. The empirical results reveal that defaulted companies usually have low profitability, high debt level, and poor operating capacity. The findings also help differentiate between groups that are susceptible and nonsusceptible to default and provide new insights into the warning signs and effective strategies for preventing defaults.

Keywords: Additive hazards model; Corporate default; Cured proportion; Latent factors; Maximum likelihood approach (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:167:y:2022:i:c:s0167947321001997

DOI: 10.1016/j.csda.2021.107365

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