An alternative to the standardized approach for assessing credit risk under the Basel Accords

Abstract

The current standardized approach for assessing credit risk under Basel III depends on ratings assigned by credit rating agencies (CRAs). However, this approach presents three problems. First, the definitions of ratings used by CRAs to assess the likelihood of default and recovery rates are not uniform. Second, because CRAs assign ratings according to through-the-cycle ratings, their ratings are less accurate in predicting near-term defaults and react slowly to credit events. Third, CRAs have assigned ratings to few Japanese companies. To improve the standardized approach under Basel III, we propose a new method for the evaluation of credit risk without CRAs. We analyse the influence of companies’ financial and non-financial attributes on default and how a default probability model is constructed using annual reports of companies listed on the Tokyo Stock Exchange spanning fiscal 2003–2009. Results indicate that our model predicts default as accurately as CRAs.

Keywords:

• Basel Accord
• credit rating
• credit risk
• default prediction
• standardized approach

AMS Subject Classifications:

• G21
• G24
• G28

Public Interest Statement

The standardized approach for assessing credit risk under Basel III depends on ratings assigned by credit rating agencies (CRAs). Therefore, we have constructed a new default prediction model that is independent of CRA ratings. Comparison with the historical record reveals that our model anticipates defaults as accurately as credit ratings. The findings are as following. First, our method requires only publicly available data and is accessible to any analyst familiar with routine financial information. Second, it overcomes two acknowledged deficiencies in credit ratings. Third, unlike the existing CRA models, our model pertains to companies that do not issue equity and debt and presents a way of differentiating unrated companies. Fourth, it demonstrates the importance of non-financial indicators like employee tenure for anticipating defaults. Fifth, our method can reveal firm-specific factors that lead to default and allows investors and regulators to identify firms by likelihood of default.

Notes

1 Japanese Bankers Association (2013) Analysis of financial statements all banks 2012.

2 For details regarding indicators, see Japan Credit Rating Agency (2012), Moody’s Investors Service (2014), Standard & Poor’s Financial Services (2013), Fitch Ratings (2013) and Rating and Investment Information (2012).

3 See Winkelmann and Boes (2009, Chapter 3) for details about AIC.

4 Hereafter, we indicate the fiscal year when we predict defaults. We judged whether defaults actually occurred in the year following the indicated year.

Additional information

Funding

This study was supported by a grant-in-aid from Zengin Foundation for Studies on Economics and Finance [grant number 1002].

Notes on contributors

Yukiko Konno

Yukiko Konno is an assistant professor at the Institute of Innovation Research, Hitotsubashi University. Her research interests include credit risk management, financial management and quantitative analysis of company activities.

Yuki Itoh

Yuki Itoh is an associate professor at the Graduate School of Social Science, Yokohama National University. His research interests include credit risk management and quantitative risk management.