Introduction to Credit Risk
Introduction to Credit Risk focuses on analysis of credit risk, derivatives, equity investments, portfolio management, quantitative methods, and risk management. In terms of application, this book can be used as an important tool to explain how to generate data rows of expected exposure to counterparty credit risk. The book also directs the reader on how to visualize, in real time, the results of this data, generated with a Java tool.
- Uses an in-depth case study to illustrate multiple factors in counterparty credit risk exposures
- Suitable for quantitative risk managers at banks, as well as students of finance, financial mathematics, and software engineering
- Provides the reader with numerous examples and applications
Giulio Carlone has an MBA, a PhD, and a Master’s degree in Computer Science from the University of Italy. He is a member of the software system engineering staff of the Department of Computer Science at University College London. He has 20 years of practical experience in technical software engineering and quantitative finance engineering in the commercial sector. His research interests include the use of communication strategies and the implementation of plans and projects using financial software for requirement specifications, requirements analysis, and architectural design.
1. Background of credit risk and Java visualization for expected exposure. 2. Theoretical phase of a real-world case study. 3. Real-world case of the practical phase for generating exposure regulatory measures in a specific bank with an internal model method. 4. Theoretical approach of the real-world case phase related to the methodology of scenario simulation used for generating exposure regulatory measures. 5. Generation of a simulation of a real-world case for generating exposures regulatory measures. 6. Compute exposure by counterparty. 7 First quantitative analysis of portfolio exposure profiles. 8. Further analysis on portfolio exposure profiles using zero rate vector 0.03. 9. Further analysis on portfolio exposure profiles with zero rate vector 0.06. 10. Generalization of analysis on portfolio exposure profiles with zero rate vectors 0.01, 0.03, and 0.06. 11. Risk perspective of credit valuation adjustment. 12. Further work. 13. Matlab source code strategy further analysis of generation of time step. 14. Expected exposure visualization list of Java Code Packages. 15. Expected exposure visualization list of UML diagram. 16 Credit Models using Google Cloud.