Multi‐period default probability forecasting

2021 ◽  
Author(s):  
Oliver Blümke
Keyword(s):  
Default Probability ◽  
Probability Forecasting

scholarly journals Validation of default probability models: A stress testing approach

2016 ◽  
Vol 47 ◽  
pp. 70-85 ◽  
Author(s):  
Fábio Yasuhiro Tsukahara ◽  
Herbert Kimura ◽  
Vinicius Amorim Sobreiro ◽  
Juan Carlos Arismendi Zambrano
Keyword(s):  
Stress Testing ◽  
Default Probability ◽  
Probability Models ◽  
Testing Approach

Internal governance mechanism and default probability: evidence from US public firms

2014 ◽  
Vol 5 (1/2) ◽  
pp. 83 ◽  
Author(s):  
Samira Lehlou ◽  
Imen Tebourbi ◽  
Lobna Bouslimi
Keyword(s):  
Default Probability ◽  
Governance Mechanism ◽  
Internal Governance ◽  
Public Firms

scholarly journals The Credit Asset of Enterprise Accounts Receivable Pricing Model

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Deshun Xu ◽  
Junhai Ma
Keyword(s):  
Intrinsic Value ◽  
Default Probability ◽  
Financial Indicators ◽  
Pricing Model ◽  
Loss Given Default ◽  
Accounts Receivable ◽  
Kmv Model ◽  
Management Technology ◽  
Overall Evaluation ◽  
Control Management

Based on the thinking of holism and reductionism, this paper creatively constructed the credit asset pricing model of enterprises’ accounts receivable, namely, the BEST pricing model, and it was demonstrated effectively. The model gave an overall evaluation on the default probability of buyer and environment, as well as buyer loss given default resulting from the factors including Seller (S), Buyer (B), and Environment (E). The model is also utilized with the optimal control management Technology (T) to maximize the intrinsic value of the credit asset. The paper put forward the Duration of accounts receivable aging, measurement method of dynamic free interest rate, and amended the KMV model to solve the default probability of accounts receivable of listed and nonlisted companies. To evaluate the credit asset risk, the following were selected: three effective financial indicators, seven nonfinancial index clusters, and sixty-three specific nonfinancial index variables of the buyer; one index and eight specific indicators of the seller; and one index and fourteen specific indicators of nonsystematic risk of the environment. Five appropriate hedge parameters are used to control the risk.

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