This is one of the most commonly debated questions among students who have made up their mind about furthering their career and adding a designation such as CFA, FRM, or CAIA to their name. Making the right choice can be crucial for a couple of reasons: 1) Each of these certifications are expensive, for example, […]

# 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 […]

# Risk Taking: A Corporate Governance Perspective

This reading is a part of the syllabus for FRM Part 1 Exam in the section ‘Foundations of Risk Management’. In the first two sections the book lays out the scope of risk management by defining risk and exploring risk governance. The next few sections look at measuring and dealing with risk and the different tools used 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 […]

# Bootstrapping Value at Risk (VaR)

This is an illustration, using a simple portfolio of four stocks over one week, of the bootstrap method. Like the Monte Carlo, we want to simulate each stock (in the portfolio) forward in time. If today is time t, then we want to simulate the stock on t+1, t+2, t+3, etc. The key difference is: […]

# 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 Scale Autocorrelated Returns?

We know that the square root rule can be used to scale volatility with time. This rule assumes that the returns are independent and identically distributed. However, this assumption is not very realistic. This video illustrates a scaling factor that adjusts the square root rule for for autocorrelation. This video is developed by David from […]

# 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 […]