Credit Default Prediction Based on Multivariate Regression Article Swipe
Yingzi Sun
,
Lirui Yang
,
Ruonan Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2991/978-94-6463-142-5_3
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2991/978-94-6463-142-5_3
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.
Related Topics To Compare & Contrast
Concepts
Credit default swap
Credit derivative
Multivariate statistics
Credit risk
iTraxx
Credit default swap index
Order (exchange)
Econometrics
Derivative (finance)
Regression
Logistic regression
Computer science
Actuarial science
Credit valuation adjustment
Business
Machine learning
Finance
Economics
Credit reference
Statistics
Mathematics
Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.2991/978-94-6463-142-5_3
- https://www.atlantis-press.com/article/125986734.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376525920
All OpenAlex metadata
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No additional metadata available.