Introduction to Quantitative Risk Management (iQRM) Seminars, presented by the ERM Program
The Introduction to Quantitative Risk Management (iQRM) seminars tours the material and cases that appear in our iQRM class. This allows beginners to get a preview of the class content and advanced quantitative students can see the applications of the tools presented and prepare to test out of the iQRM class by taking the iQRM waiver exam. Enrolled students are also welcome to attend to get a refresher.
How To Join a Session
Click on the Join a Session button at 8:00 am ET on the day of your desired workshop. Each workshop ends at 9:30 am ET.
Nov 6 | 8:00 am to 9:30 am ET
In mathematics, a derivative is a calculation of how much one variable changes when other variables change. In Risk Management, this is a concern that arises all the time… How much will a bond change in value if interest rates change? How much will a stock price change if a major stock index changes? How much will a company's probability of default change if sales change?
Nov 13 | 8:00 am to 9:30 am ET
Regression Analysis is a fundamental tool in science and risk management. In this session we explore the assumptions and mathematics necessary to use this tool.
Nov 20 | 8:00 am to 9:30 am ET
Risk and life comes in systems. What is the variance of multiple risks? What is the variance of a portfolio? Matrix Algebra is the mathematics of systems. In this system we explore the basics of matrix algebra as applied to portfolio risk.
Dec 4 | 8:00 am to 9:30 am ET
Parameter & Process Risk
The terms Parameter risk and Process risk refer to the variability in forecasts that comes from the model itself versus the variability that comes from the process we are modeling. We show how bootstrapping and refitting are used to measure the variability of any model.
Dec 11 | 8:00 am to 9:30 am ET
We review major threads that run through the material such as the construction of distributions and their use in hypothesis testing, differential calculus and the construction of linear and logistic regression, matrix algebra and the modeling of systems.