Aggregate Credit Risk, Firm Characteristics and Conditional Factor Model

2012 ◽  
Author(s):  
Hwagyun Kim ◽  
Arvind Mahajan ◽  
Alex Petkevich
Keyword(s):  
Credit Risk ◽  
Factor Model ◽  
Firm Characteristics

scholarly journals Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 562
Author(s):  
Pawel Siarka
Keyword(s):  
Risk Management ◽  
Distribution Function ◽  
Credit Risk ◽  
Factor Model ◽  
Capital Requirements ◽  
Total Loss ◽  
Credit Risk Management ◽  
Loan Portfolios ◽  
Risk Management Process ◽  
Market Factors

This paper addresses the problem of modeling credit risk for multi-product and global loan portfolios. The authors presented an improved version of the Basel Committee’s one-factor model for capital requirements calculation. They examined whether latent market factors corresponding to distinct portfolios are always highly correlated within the global portfolio and how this correlation impacts total losses distribution function. Historical losses of top-tier banks (JPMorgan Chace, Bank of America, Citigroup, Wells Fargo, US Bancorp) were analyzed. Furthermore, the estimation of the correlations between latent market factors was conducted, and its impact on the total loss distribution function was assessed. The research was performed based on consolidated financial statements for holding companies - FR Y-9C reports provided by the Federal Reserve Bank of Chicago. To verify the improved model, the authors analyzed two distinct loan portfolios for each bank, i.e., credit cards and commercial and industrial loans. They showed that the correlation between latent market factors could be significantly lower than one and disregarding this conclusion may lead to overestimating total unexpected losses. Hence, capital requirements calculated according to the IRB (Internal Ratings Based Approach) formula as a sum of individual VaR999 estimates may be biased. According to this finding, the enhanced one-factor model seems to be more accurate while calculating unexpected total loss for global portfolios. The authors proved that the active credit risk management process aiming to lower market factors’ correlation results in less volatile total losses. Therefore, financial institutions could be more resistant to macroeconomic downturns.

scholarly journals Calculating credit risk capital charges with the one-factor model

2005 ◽  
Vol 7 (2) ◽  
pp. 85-101 ◽  
Author(s):  
Susanne Emmer ◽  
Dirk Tasche
Keyword(s):  
Credit Risk ◽  
Factor Model ◽  
Risk Capital ◽  
The One

scholarly journals Copula-based factor model for credit risk analysis

2016 ◽  
Vol 49 (4) ◽  
pp. 949-971 ◽  
Author(s):  
Meng-Jou Lu ◽  
Cathy Yi-Hsuan Chen ◽  
Wolfgang Karl Härdle
Keyword(s):  
Risk Analysis ◽  
Credit Risk ◽  
Factor Model ◽  
Credit Risk Analysis

Test of Five-factor Asset Pricing Model in India

2018 ◽  
Vol 22 (2) ◽  
pp. 153-162 ◽  
Author(s):  
A. Balakrishnan ◽  
Moinak Maiti ◽  
Pradiptarathi Panda
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Factor Model ◽  
Five Factor Model ◽  
Firm Characteristics ◽  
Asset Pricing Model ◽  
Average Return ◽  
Asset Pricing Models ◽  
Indian Stock Market ◽  
Shed Light

In this article, we examine whether stock returns are related with important firm characteristics and fundamentals such as size, value, profitability and investment. We also evaluate whether the existing asset pricing models of Fama–French three-factor (FFTF) model and Fama–French five-factor model can capture the average returns on portfolios constructed based on the above characteristics and fundamentals. We find from the results that average return pattern clearly shows that Indian stock market is strongly influenced by the factors mentioned above. Asset pricing results also shed light that FFTF model clings on to its efficiency at capturing the average returns on portfolios, while Fama–French five-factor model does a plausible job.

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