Understanding and Applying Financial Risk Modeling Techniques
Financial risk modeling is at the intersection of two hot trends: Fintech and Big Data. This course covers three financial risk modeling techniques: covariance matrices, factor models, and value-at-risk.
What you'll learn
Financial risk modeling is back in the limelight these days because of its place at the intersection of two hot trends: Fintech and Big Data. Enthusiasm about the intersection of technology and finance is tempered by caution born from past financial risk management failures, such as those witnessed during the Subprime Crisis. In this course, Understanding and Applying Financial Risk Modeling Techniques, you'll learn the details of three related financial risk modeling techniques: covariance matrices, factor models, and value-at-risk. First, you'll discover risk, uncertainty, and standard deviation. Next, you'll explore the role of covariance matrices in modeling risk. Then, you'll go through building scenario-based stress tests using factor models. Finally, you'll learn how to implement a robust risk modeling approach using Excel, VBA, R, and Python. By the end of this course, you'll have a good understanding of how financial risks of all types can be quantified and modeled.
Table of contents
- An Approach to Risk Management 4m
- Portfolios as Sums of Random Variables 5m
- Covariance Matrices in Measuring Portfolio Variance 3m
- The Intuition Behind Factor Models 3m
- The Math Behind Factor Models 5m
- The Intuition Behind Value-at-risk 4m
- The Math Behind Value-at-risk 5m
- Advantages of VaR 3m
- Disadvantages of VaR 5m