Credit Risk Management Essentials
Master essential credit risk analysis techniques and develop effective management strategies to protect financial institutions against potential defaults.
Master essential credit risk analysis techniques and develop effective management strategies to protect financial institutions against potential defaults.
Dive into the world of credit risk management with this comprehensive course. Designed for finance professionals and students, this program provides a solid foundation in credit risk concepts, models, and regulatory frameworks. You'll explore the implications of credit risk for banks and financial institutions, delve into risk measures like Value-at-Risk, and study key credit risk models. The course covers Basel II and III regulations, credit ratings, probability of default calculations, and introduces Credit Default Swaps. By the end, you'll have practical skills to understand and manage credit risk in today's complex financial landscape.
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What you'll learn
Understand the definition and implications of credit risk for financial institutions
Explain recent risk regulations for banks, including Basel II and III
Compute and interpret basic risk measures like Value-at-Risk and Expected Shortfall
Understand the use of credit ratings and define the probability of default
Apply important credit risk models such as Merton's model and the Moody's KMV model
Describe the basics of Credit Default Swaps (CDS)
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive introduction to credit risk management, focusing on its importance in modern economies. Participants will learn about credit risk definition, implications for financial institutions, and key risk management tools and models. The curriculum covers recent bank risk regulations (Basel II and III), basic risk measures like Value-at-Risk and Expected Shortfall, credit ratings, and probability of default calculations. Students will explore important credit risk models including Merton's model, the Moody's KMV model, CreditMetrics™, and Credit Risk Plus™. The course also introduces Credit Default Swaps (CDS) and the concept of stress-testing. By the end of the course, students will have a solid understanding of credit risk management principles and practices, enabling them to apply these concepts in real-world financial scenarios.
Fee Structure
Instructors
1 Course
Leading Expert in Risk Analysis and Applied Probability
Pasquale Cirillo currently serves as Professor of Data Science at ZHAW School of Management and Law, following his role as Associate Professor at TU Delft from 2012 to 2020. After earning his PhD in Statistics from Bocconi University and Habilitation in Applied Statistics from the University of Bern, he has established himself as a leading authority in risk modeling and financial mathematics. His research focuses on risk analysis, particularly tail risk, quantitative risk management, and machine learning applications. He has received multiple prestigious awards, including Marie Skłodowska-Curie Actions grants and a DIZH Fellowship. His work has significantly influenced the field of risk analysis, with notable publications in Pattern Recognition and the International Journal of Forecasting. Beyond academia, he maintains an active role as a statistical consultant for international institutions, banks, and insurance companies. His teaching portfolio spans both technical and applied courses, including Machine Learning, Data Analysis, and Applied Forecasting, reflecting his commitment to bridging theoretical knowledge with practical applications in risk management and data science
1 Course
Expert in Computational Finance and Risk Modeling
Fang Fang serves as a part-time Assistant Professor at TU Delft's Department of Applied Mathematics while maintaining an active role in the financial industry. After earning her PhD in Computational Finance from TU Delft in 2010, she developed the groundbreaking "COS method," which has become a widely adopted standard in the field with over 800 citations. Her research focuses on quantitative finance, particularly the fast calculation of portfolio-level financial risks, efficient numerical methods for derivative pricing, and machine learning applications in time series prediction. Since 2009, she has worked as a quantitative analyst in the banking sector, developing and validating risk models and derivative pricing models. In 2021, she returned to academia part-time at TU Delft, where she teaches undergraduate and graduate courses while supervising MSc theses. Her work bridges theoretical mathematics with practical applications in financial risk management, combining academic rigor with industry expertise.
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4.4 course rating
7 ratings
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