Archive | FRM Exam

RSS feed for this section

FRM Exam Study Plan in Excel

FRM is a difficult exam with a huge list of readings prescribed by GARP. FRM Part 1 alone has 52 readings for teh current syllabus. Along with a job and With limited time to study it is important that you keep track of your studies and have a study plan to ensure that you finish […]

Key Dimensions that Characterize Acceptable Data

Organizing the rules of data quality into dimensions not only improves the specification and measurement of the data quality, it also provides the framework under which quality can be measured and reported. This in turn enables better governance of data quality. Tools can then be built around this to determine the minimum levels required to […]

Standard Error in Linear Regression

A simple (two-variable) regression has three standard errors: one for each coefficient (slope, intercept) and one for the predicted Y (standard error of regression). While the population regression function (PRF) is singular, sample regression functions (SRF) are plural. Each sample produces a different SRF. So, the coefficients exhibit dispersion (sampling distribution). The standard error is […]

Type I and Type II Errors

When drawing an inference (from a sample statistic, about a population parameter), there can be two types of errors: Type I and Type II. Type I error, also known as error of the first kind, occurs when the null hypothesis is true, but is rejected. Type II error, also known as the error of the […]

Extreme Value Theory

Extreme value theory (EVT) aims to remedy a deficiency with value at risk (i.e., it gives no information about losses that exceed the VaR) and glaring weakness of delta normal value at risk (VaR): the dreaded-fat tails. The key is the idea that the tail has it’s own “child” distribution. This video explains the extreme […]

How to Forecast Volatility Using GARCH (1,1)

This video discusses how to use GARCH(1,1) to forecast future volatility. The key parameter is persistence (alpha + beta): high persistence implies slow decay toward the long run average. GARCH models were developed by Robert Engle to deal with the problem of auto-correlated residuals (which occurs when you have volatility clustering for example) in time-series […]