FRM Level I Probability, Statistics, Regression, and Volatility Topics

Summary

The document lists the quantitative subjects covered in the FRM Level I study guide. Topics include probability distributions, descriptive statistics, statistical inference, parameter estimation, and graphical analysis. It also outlines regression concepts, including ordinary least squares, interpreting coefficients and test statistics, confidence intervals, heteroskedasticity, and multicollinearity.

The remaining topics include simulation, volatility estimation with EWMA and GARCH, and volatility term structures. The material is presented as a curriculum outline rather than a lesson: it names areas candidates may study but does not explain formulas, prerequisites, exam weighting, or how deeply each topic is tested. The list is attributed to the study guide, which the answer says is available through GARP after free registration.

Key ideas

• The listed curriculum includes discrete and continuous probability distributions.
• It covers population and sample statistics, inference, and hypothesis testing.
• Regression topics include OLS, coefficient interpretation, and common model issues.
• Simulation and volatility estimation with EWMA and GARCH are included.
• The outline names topics but does not provide instruction or exam-weight details.

Tags

# What are the math topics involved in FRM 1


# What are the math topics involved in FRM 1












Is there a way to get the math curriculum for FRM level 1 without purchasing the Exam? I wish to take a look at the math topics and see if I have any chance of cracking it. But I could not find an online resource that lists the math topics required for FRM 1.

## Answer by PBD10017 (score 3, accepted)

https://quant.stackexchange.com/a/9678

See below. You can find out more here at the GARP website. You may need to register, but it's free. Download the study guide. The bullets below are from it.

- Discrete and continuous probability distributions

- Population and sample statistics

- Statistical inference and hypothesis testing

- Estimating the parameters of distributions

- Graphical representation of statistical relationships

- Linear regression with single and multiple regressors

- The Ordinary Least Squares (OLS) method

- Interpreting and using regression coefficients, the t-statistic, and other output

- Hypothesis testing and confidence intervals

- Heteroskedasticity and multicollinearity

- Simulation methods

- Estimating correlation and volatility using EWMA and GARCH models

- Volatility term structures

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.