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