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Introduction to Quantitative Risk Management (iQRM) Seminar, Presented by the ERM Program

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September 9 – December 9, 2023
Time for all workshops: 9:00 – 10:30 am ET

The Introduction to Quantitative Risk Management (iQRM) Seminar workshops tour 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 here at 9:00 am ET on the day of your desired workshop to join virtually. Each workshop ends at 10:30 am ET.

Workshop info

Saturday, September 9 | 9:00 to 10:30 am ET

In our first iQRM workshop we cover Excel. Excel is an amazing tool widely used for analytics, modeling, trading, and communication. This workshop starts with the basics and quickly moves on to more complicated Excel functions.

Saturday, September 16 | 9:00 to 10:30 am ET
Financial Math 1

Time value of money calculations are everywhere… Our personal plans and professional projects both span time. You must understand basic financial math to understand risk and value over time.

Saturday, September 23 | 9:00 to 10:30 am ET
Financial Math 2

We continue building a foundation with concepts such as normal distributions for stock returns and lognormal distributions for stock levels; continuous compounding, expected returns and the variance of returns.

Saturday, September 30 | 9:00 to 10:30 am ET
Probability 1

To manage risk, we must measure it. In this session we take our first steps in the mathematics of chance. We explore expected value, variance, and the variance of averages.

Saturday, October 7 | 9:00 to 10:30 am ET
Probability 2

In this session we continue building our foundation in probability by exploring the impact of correlation on the estimation of variance.

Saturday, October 14 | 9:00 to 10:30 am ET
Statistics 1

Statistics is the application of probability theory to data. Using statistical descriptions of data, you can determine the probability of events. We can also use the distributions that we understand to decide whether to reject hypotheses.

Saturday, October 21 | 9:00 to 10:30 am ET
Statistics 2

In this session, to solidify our understanding of hypothesis testing distributions, we construct those distributions and see how they are derive from the normal distribution.

Saturday, October 28 | 9:00 to 10:30 am ET

Simulation is a tool in risk management where we synthesize data we can’t get in order to make inferences about the variability of that data. Simulating data can also help us better understand the data we do have.

Saturday, November 4 | 9:00 to 10:30 am ET
Differential Calc

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?

Saturday, November 11 | 9:00 to 10:30 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.

Saturday, November 18 | 9:00 to 10:30 am ET
Matrix Algebra

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.

Saturday, December 2 | 9:00 to 10:30 am ET
Model 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.

Saturday, December 9 | 9:00 to 10:30 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.