The term Value at Risk, by its acronym VaR (Value at Risk) refers to a statistical technique that measures the financial riskof an investment. It will indicate the probability of suffering a loss of an investment during a specific period of time (usually 1 day, 1 week or 1 month). As for the values that it usually takes, it is usually between 1% or 5%).

In other words, the VaR will inform us of the maximum loss that an investment can suffer in a specific time horizon, for a confidence level that is either 95 or 99%. The variables necessary to be able to calculate this parameter are the amount of the loss, the probability of the loss and the time.

## How to calculate the Value at Risk (VaR)?

There are 3 ways to calculate Value at Risk (VaR):

- Parametric VaR: takes data from profitability estimates and assumes a normal distribution of profitability.
- Historical VaR: uses historical data.
- VaR by Monte Carlo: It is calculated by a computer program that generates hundreds or thousands of possible results based on the data entered by a user in the beginning.

## Main pros and cons of VaR

VaR has the ability to measure the financial risk of an investment, having one of its main applications in the world of finance, allowing the calculation of losses for a financial asset or a portfolio. Thanks to VaR, companies can estimate benefits that they will have on their investments compared to their VaR, in order to invest more money in those that are worthwhile.

Regarding the advantages of VaR, we highlight:

- It allows us to know the risk of an investment through a single number, assessing different risk possibilities
- Standardized risk measure
- If the correlation between different investments is less than 1, the set of VaR will be less than the sum of the VaR

On the other hand, we have a series of drawbacks when using VaR:

- If the data entered is incorrect, the VaR result will also be incorrect
- VaR does not consider all worst possible scenarios
- Some methods to calculate it can be expensive, difficult to apply and the results can be different