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Forecasting net charge-off rates of banks: What model works best?

1 Lowder Eminent Scholar in Finance, Raymond J. Harbert College of Business, 316 Lowder Hall, Auburn University, Auburn, AL 36849, USA
2 Assistant Professor in Business Analytics, Raymond J. Harbert College of Business, 413 Lowder Hall, Auburn University, Auburn, AL 36849, USA
3 Ph.D. Student in Finance, Raymond J. Harbert College of Business, 306 Lowder Hall, Auburn University, Auburn, AL 36849, USA
4 Assistant Professor in Business Analytics, Raymond J. Harbert College of Business, 424 Lowder Hall, Auburn University, Auburn, AL 36849, USA
5 Senior Vice President and Head of Quantitative Risk Analytics, Regions Bank, 1900 5th Avenue North, Birmingham, AL 35203, USA
6 Vice President and Risk Quantitative Analyst, Regions Bank, 1900 5th Avenue North, Birmingham, AL 35203, USA

Special Issues: Systemic Risk Measurement

The purpose of this paper is to focus on the losses of two very big banks, Citigroup (Citi) and Wells Fargo & Company (Wells Fargo), and two very small banks, First Busey Corporation (Busey) and Capital City Bank Group (Capital), over the period 1991–2016. The federal government actually bailed out the two big banks, as measured by total assets, whereas neither of the two small banks required a bail out. Clearly, if one is able to use a variety of predictor variables to forecast accurately the losses of banks of various sizes, in different geographical locations, and operating a variety of business models, this may help identify potential causes of future banking problems and thereby lessen, if not eliminate, the need for future bailouts. This is important for both the banks and the bank regulatory authorities. In particular, those banks expected to suffer significant losses on loans may be in a position to increase their provisioning and thus loan loss allowances. If such banks are unable to take this type of action or other corrective action to address expected losses, regulatory action may become necessary in response to this situation. The motivation for our paper is this very issue: can one obtain accurate forecasts of losses, or the net charge-off rates, of banks? We provide an answer to this question by examining the four banks mentioned using several hundred predictor variables and several different forecast techniques.
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Keywords forecasting; banking; predictive models; ridge estimator; partial least squares

Citation: James R. Barth, Sumin Han, Sunghoon Joo, Kang Bok Lee, Stevan Maglic, Xuan Shen. Forecasting net charge-off rates of banks: What model works best?. Quantitative Finance and Economics, 2018, 2(3): 554-589. doi: 10.3934/QFE.2018.3.554

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This article has been cited by

  • 1. Atanu Das, Performance evaluation of modified adaptive Kalman filters, least means square and recursive least square methods for market risk beta and VaR estimation, Quantitative Finance and Economics, 2019, 3, 1, 124, 10.3934/QFE.2019.1.124

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