https://studiegids.vu.nl/en/courses/2025-2026/E_FIN_CCSRAt the end of the course, students will have a good understanding of quantitative risk management methods used by financial institutions and the challenges currently worked upon in the industry.Credit risk management is of vital importance for banks, insurers and pension funds. Realistic modelling techniques are required for suitably managing credit risk exposures in loans, bonds and credit-risky securities. Explicit attention will go out to recent developments for the pricing and risk management of credit risk, such as default modelling, credit valuation adjustments and funding. Tools for mitigating counterparty credit risk discussed, among others collateral management and central clearing parties. In this course, we will also learn about systemic risk, which is the risk associated with the financial system as a whole (rather than risk associated with one particular financial institution). We will investigate how complex relationships between financial system participants influence systemic risk, how it can be measured and which tools are there to control this risk. We will address as a specific example of systemic risk issues related to central clearing of derivatives and the resulting relations between credit and liquidity risk. Finally, throughout all components of the course, model risks are discussed and general tools are discussed how to deal with model and parameter uncertainty in practice.Lectures and tutorialsWritten exam plus assignmentsBackground literature (optional): Jon Gregory (2015): The xVA Challenge: Counterparty Credit Risk, Funding, Collateral, and Capital, 3rd Edition, ISBN: 978-1-119-10941-9. Brigo et al. (2013): Counterparty Credit Risk, Collateral and Funding: With Pricing Cases For All Asset Classes, ISBN: 978-0-470-74846-6. Various papers on complexity & systemic risk (references / links will be provided during the course)Quantitative Finance, Econometrics and Operations Research.This course is meant for students pursuing a career in risk management at financial institutions such as banks, insurers and pension funds. Explicit links between academic models and their practical applicability will be discussed in the lectures and through real-life case studies. Note: this course is quite quantitative and requires a good knowledge of probability and statistics, as well as good programming skills (e.g. in Python) for the case studies.Good knowledge of probability and statistics. Good programming skills for the assignments (e.g. in Python). Basic knowledge of derivatives.