Category: Quantitative Analysis

In this video, we explore the fundamentals of the beta distribution, a continuous probability distribution defined between 0 and 1. Using various values for the parameters alpha and beta, we demonstrate how the shape, skewness, and concentration of the distribution change. Whether you’re new to statistics or brushing up on your knowledge, this visual explanation provides a clear and concise understanding of how the beta distribution behaves under different scenarios.

In this video from the FRM Part 1 curriculum, we take a look at the “memoryless” property of exponential distribution.

In this video from FRM Part 1 curriculum, we take a look a the antithetic variates technique to reduce the standard error of our Monte Carlo based estimator. This technique simulates the chosen variable as a pair of values – with the members of the pair having a negative dependence amongst themselves.

In this video, we take a look at the information provided by the plot of Autocorrelation vs Lag (also called Correlogram). Such a plot provides a very handy visual tool to detect the type of process / specification followed by the time series (as per the Box Jenkins methodology).

In this video from FRM Part 1 curriculum, we take a look at Random Walks and their generalizations which are Unit Root Processes. We compare the properties of Random Walks with stationary AR(1) processes.

In this video from FRM Part 1 curriculum, we do a quick review of the reading on Sample Moments (Book 2, Quantitative Analysis, Chapter 5). All learning objectives for this reading are covered.

This video from FRM Part 1 curriculum does a solved example covering the learning objectives: “Distinguish the key properties and identify the common occurrences of the Binomial Distribution. Characterize the quantile function and quantile-based estimators.”

In this video from FRM Part 1 (Quantitative Analysis section), we explore the concept of a one-sided confidence interval – how such intervals are constructed and how they are used for the purpose of hypothesis testing.

In this video from FRM Part 1 curriculum, we define a very important building block of any time series: white noise. We define white noise, explore it’s properties and distinguish between independent white noise and normal or Gaussian white noise.

In this topic taken from FRM Part 1 curriculum, we explore the concept of “Conditional Independence” and how it differs from (unconditional) independence.