Credit Default Prediction Based on Multivariate Regression

Yingzi Sun (), Lirui Yang () and Ruonan Zhao ()
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Yingzi Sun: University of Arizona
Lirui Yang: Guangzhou Foreign Language School ISA Wenhua IB Programme
Ruonan Zhao: Tianjin University of Finance and Economics, Pearl River College

A chapter in Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023), 2023, pp 16-23 from Springer

Abstract: Abstract Credit default is a wide-spread credit derivative instrument. As it becomes more and more popular, an appropriate supervision system has to be established. In this paper, a multiple factor regression models are constructed in order to investigate the feasibility for credit default prediction based on R program. Since risks are unavoidable, some measures should be taken to predict them in order to help the banks that sell credit default swaps to minimize their risks. According to the analysis, a model is successfully created. These results shed light on guiding further exploration focusing on credit default prediction.

Keywords: Credit Default; risk; logistic regression (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-142-5_3

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DOI: 10.2991/978-94-6463-142-5_3

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