Value at Risk is a risk measure that conveniently expresses as a single number the answer to the question “*What is your worst case loss, over a certain period of time and given a certain level of probability?*” There are a number of methodologies used for calculating the measure such as the Variance Covariance approach, the Historical Simulation approach and the Monte Carlo simulation approach.

## What are the prerequisites?

Prior to gaining an understanding of the Value at Risk Concept a useful introduction to understanding risk is our online course:

## What topics are covered?

Proceeding from this introduction the following courses review the calculation methodology of Value at risk (VaR) and provide an example of its use as a risk measurement tool via a case study on margin requirements determination for the Oil and Petrochemical Industry:

## What are the additional topics I can read up on?

Other applications of the VaR measure are:

- Its incorporation within various Asset Liability Management tools such as in determining the fall in Market Value of Equity,
- In setting market risk and counterparty (PSR) Limits,
- In calibrating Stop Loss Limits, etc.

These are discussed in the following courses:

- The ALM Crash course and survival guide
- Setting Counterparty Limits, Market Risk Limits & Liquidity and Interest Rate Risk Limits

## Premium Content

**PDF & EXCEL**

**Online Courses**

- Calculating Value at Risk (VaR) – Package
- Calculating VaR – Includes case study
- Calculating VaR – EXCEL Example
- Calculating VaR for Futures and Options – EXCEL Example
- Collateral Valuation in Credit Risk Management
- Comparing Value at Risk – Model, Methods and Metrics – EXCEL
- Portfolio Risk Metrics – EXCEL Example
- Portfolio VaR – EXCEL Example
- Setting Counterparty Limits
- Setting Limits – EXCEL Example
- Setting Counterparty Limits – Package
- Sample Counterparty Limit Proposal
- Value at Risk Example for Fixed For Floating Interest Rate Swaps – EXCEL
- Value at Risk with Liquidity Premium
- Value at Risk using the Monte Carlo simulation with Historical Returns approach