Quantitative Analysis
From now until the time our FRM Study Notes ship (June 25), I'd like to provide candidates with a detailed look at each section of the FRM exam. I will be writing five posts in all, which outline the five risk-related disciplines that you will encounter on this year's exam. My objective is to demystify the FRM curriculum and to get you thinking about how much study time you wish to allocate to each topic area over the coming months. The first risk-related discipline I'll examine is Quantitative Analysis, which is covered in Book 1 of our FRM Study Notes.
Quantitative Analysis will account for 10% of the questions on this year's FRM exam and is divided into three sections:
- Quantitative Fundamentals
- Statistical Properties and Forecasting of Correlation, Covariance, and Volatility
- Monte Carlo Simulation and Extreme Value Theory
Quantitative Fundamentals
Key topic areas in this section include: probability, statistics, and regression analysis.
For probability, the main focus is on probability terminology and different types of probability distributions. Also examined are the characteristics of probability distributions (e.g., expected value and variance). Understanding the properties of the normal distribution is imperative here. Another important aspect of this topic area is differentiating between population and sample data.
For statistics, the main focus is on estimation and hypothesis testing. You will learn when to use z-stats and when to use t-stats and how to create confidence intervals to examine population parameters. Hypothesis testing is an important part of this section. Here you will learn how to compare test statistics to critical values and how to determine whether to reject or fail to reject a null hypothesis.
Regression analysis is broken down into two areas: simple regression and multiple regression. Here you will learn how to fit a regression line to data and how to determine if independent variable(s) explain changes in a dependent variable. Hypothesis testing also comes into play in regression analysis when evaluating the statistical significance of regression coefficients.
Statistical Properties and Forecasting of Correlation, Covariance, and Volatility
The one reading from this section deals mainly with how to estimate volatility in value at risk (VAR) models. Those of you who are not that familiar with the concept of VAR may wish to first review the VAR section in Book 2 of our Study Notes to understand why volatility estimates are so important. Our Study Notes are set up to follow the order of the assigned readings set forth by GARP in their FRM study guide; however, this is one example of where following the readings out of order may prove to be beneficial.
Monte Carlo Simulation and Extreme Value Theory
Like the previous section, this section only includes one assigned reading. The reading serves mainly as an introduction to the Monte Carlo simulation method of estimating VAR and how to examine the tails of a normal distribution via extreme value theory (EVT). Again, it is advised that you have an understanding of the main VAR concepts discussed in Book 2 before studying this topic.
I hope this outline of Quantitative Analysis was helpful! If you have taken Levels 1 and/or 2 of the CFA exam you will find this material very similar to what you have already encountered. Much of the probability and statistics material is identical to Level 1 quant and the regression material is, for the most part, right out of Level 2 quant. As a final note, I'd like to point out that we have included, in Book 1 of our Study Notes, some background material on the time value of money (TVM) as well as an introduction to forecasting volatility and correlation. There is not a specific assigned reading that deals with TVM, but you will be required to know how to use your financial calculator to calculate TVM problems in the fixed income section in Book 2 of our FRM Study Notes.