# VaR Calculation Using Monte Carlo Simulations

Automating calculations of Value at Risk (VaR) to manage portfolio risk, equity and stocks in Python using Monte Carlo Simulation.

### VaR in Financial and Portfolio Risk Management?

VaR is an acronym of ‘Value at Risk’, and is a tool which is used by many firms and banks to establish the level of financial risk within its firm. The VaR is calculated for an investments of a company’s investments or perhaps for checking the riks levels of a portfolio managed by the wealth management branch of a bank or a boutique firm.

The calculation may be thought of as a statistical measure in isolation. It can also be simplified to the following example statement -

VaR is the minimum loss which will be incurred at a certain level of probability (confidence interval) OR the maximum loss which will be realized at a level of probability.

Photo Credit — SP Consulting LLP

The above image shows the maximum loss which can be faced by a company at a α*% *confidence. On a personal level VaR can help you predict or analyse the maximum losses which your portfolio is likely to face — this is something which we will analyse soon.

### Monte Carlo Simulations

The Monte Carlo model was the brainchild of Stanislaw Ulam and John Neumann, who developed the model after the second world war. The model is named after a gambling city in Monaco, due to the chance and random encounters faced in gambling.

The Monte Carlo simulation is a probability model which generates random variables used in tandem with economic factors (expected return, volatility — in the case of a portfolio of funds) to predict outcomes over a large spectrum. While not the most accurate, the model is often used to calculate the risk and uncertainty.

We will now use the Monte Carlo simulation to generate a set of predicted returns for our portfolio of assets which will help us to find out the VaR of our investments.

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