A study of credit risk of Chinese listed companies: ZPP versus KMV

ABSTRACT

The Zero-Price Probability (ZPP) model is applied to evaluate the credit risk of listed companies in China and its performance is compared to that of the Kealhofer-McQuown-Vasicek (KMV) model. The sample includes 34 financially distressed companies and a comparison group of 34 financially healthy companies. The performances of ZPP and KMV models are compared using various descriptive statistics and statistical tests. The empirical analyses show that the ZPP model is superior to the KMV model in terms of discriminatory power. Compared to the KMV model, the ZPP model performs much better in distinguishing between financially challenged and healthy firms. Among different specifications of the ZPP model, the naïve constant variance zero-price probability model outperforms those with generalized autoregressive conditional heteroskedasticity specifications. This article is among the very first studies that provide evidence on the performance of the ZPP model.

KEYWORDS:

• Zero-price probability (ZPP) model
• credit risk
• Monte Carlo simulation
• expected default frequency

JEL CLASSIFICATION:

• G30
• G33

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ The difference between ST and *ST companies is not relevant for our research question. So in the remaining of the article, we will generally refer to them as ST companies.

² AIC is employed for selecting the order of the autoregressive model. GARCH(1,1) is adopted for simplicity and its ability to capture the volatility clustering that is present in the data.

Additional information

Funding

Financial supports from the National Natural Science Foundation of China (grant numbers 71273148, 71101024 and 71571038) and the Ministry of Science and Technology of China (grant number 2014BAK01B04-2) are gratefully acknowledged.