As machine learning and AI continue to transform the financial landscape, it’s more important than ever to effectively measure and manage various risks. If you’re looking to turn your quantitative background into a fulfilling career in finance and banking, this course is designed to pave your way into financial risk management.
The course covers fundamental theories, quantitative methods, and industry practices for identifying, measuring, managing various types of risk, such as market risk, credit risk, liquidity risk, operational risk, and investment risk.
Unlike other similar courses, it teaches the concepts and methods specifically from the perspective of practical risk management approaches. Key approaches include, but are not limited to: diversifying risk through portfolio optimization, neutralizing expected risk through risk-based pricing and loss reserves, offsetting risk through hedging strategies, transferring risk through securitization, hedging risk through credit default swaps, and managing unexpected risk through economic and regulatory capital.
As traditional statistical and econometric methods struggle to keep pace with the going complexity and sheer volume of financial data, machine learning emerges as a game-changer. This course is specifically designed to explore how machine learning and AI are transforming risk management practices.
Major Modules
This course consists of the following five modules.
Module One: Introduction to Financial Institutions and Risk Concepts
- Provides an overview of banking products, financial derivatives (options, futures, and swaps), basic measures of risk, and practical approaches to risk management.
Module Two: Market and Interest Rate Risk
- Focuses on managing market risk using tools such as VaR, expected shortfall, volatility models (EMMA, GARCH), and the Greeks. Also covers duration and convexity for interest rate risk mitigation.
Module Three: Credit Risk in Banking and Securitization
- Introduces techniques for estimating credit risk and default probabilities (Merton model, hazard rate, copulas, and single factor models). It also covers managing credit risk with credit derivatives (CDS), securitization, risk-based pricing, and loss reserves.
Module Four: Enterprise Risk Management
- Covers operational, liquidity, and model risk, as well as stress testing and capital adequacy. Provides case studies on the failure of major financial institutions and market failures.
Module Five: Contemporary Challenges in Financial Risk Management
- Examines current challenges posed by decentralized finance (DeFi), block chain, cryptocurrencies, artificial intelligence (AI), and issues related to failing banks and market failure.
Class Projects
- To help students master risk management concepts, methods, and techniques, students are expected to work in groups on well-designed, real-world financial risk management cases.
- Hands-on class projects will account for the major part of a student’s final grade.
Lecturers
- Hengzhong Liu, Executive in Residence, former managing director and SVP at Bank of America and Citigroup
- David Ye, Research Associate Professor, former Chief Risk Officer for Nomura Americas and State Street Global Markets.
- Massimo Cutuli, Senior Fellow, currently Chief Financial Risk Officer, Options Clearing Corp.
Prerequisites
- College-level calculus, Linear Algebra (e.g., MATH 216, 218 0r 211), probability and statistics (e.g., MATH/STA 230, MATH 340/STA 231 or Math 238L/EGR 238L).
- Basic programming skills in Python, R or SAS
- While prior knowledge in finance is not required, some basic understanding of economics, finance, and financial institutions is preferred. Intellectual curiosity and independent study habits are strongly desired.