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Estimating Value at Risk using Python
Measures of exposure to financial risk


Correlation between
two random variables Value at risk (VaR) is a measure of market risk used in the finance, banking and insurance industries. It involves the use of statistical analysis of historical market trends and volatilities to estimate the likelihood that a given portfolio’s losses will exceed a certain amount. It is widely used for risk management and risk limit setting.

We will examine how to estimate VaR using Monte Carlo simulation techniques (also called stochastic simulation methods), analyze the effect of portfolio diversification and correlation between stocks on financial risk, and use copula methods to sample from correlated random variables and estimate portfolio VaR.

This submodule is a part of the risk analysis module.

Learning objectives

Upon completion of this module, you should be able to:

  • Understand how financial risk is modeled, characterized and quantified

  • Understand the impact of correlated risks on risk metrics

  • Implement a Monte Carlo simulation procedure for stochastic estimation of some poorly-known quantity

Course material

Estimating Value at Risk

Lecture slides (PDF)
View on Slideshare

Python notebook on analyzing risk of a stock market portfolio

View Python notebook online

Download Python notebook

Run notebook in MyBinder mybinder

Run notebook in Google Colab

Applications are run using Python and the NumPy and SciPy libraries. These are all free software (both free as in beer and free as in freedom) that you can install on your own computer, or that you can run in the cloud using services such as mybinder.org and Google CoLab.

After reading this material, you may be interested in the submodule on copula methods for representing multivariate dependencies.

Other resources

We recommend the following sources of further information on this topic: