Value at Risk (VaR) is one of the most important mark risk measures. Risk managers use VaR to calculate and monitor the level of risk that is undertaken and to ensure that it is in limits. It gives the probability of losing more than the amount in a portfolio. While any company may use Value at Risk to measure its risk exposure, commercial and investment banks most frequently use it to capture the potential loss of value of their traded portfolios from adverse market fluctuations over a period of time.
There are three key elements to describe a value of risk, a time period, the dollar amount of VAR, and a given normal market condition (or confidence interval). For example, say you have a $100 million portfolio, the confidence interval is 95% and for a month horizon.
To calculate VaR, there are three methods; the historical method, the variance-covariance method, and the Monte Carlo method. True historical returns are actually re-organized by the historical method, placing them in order from worst to best. It then assumes that history, from the perspective of risk, will repeat itself. This approach assumes that stock returns are distributed normally. In other words, we need to estimate only two variables that enable us to plot a normal distribution curve: an expected (or average) return and a standard deviation. The third method involves creating a formula for potential returns on stock prices and conducting many hypothetical trials through the model. A simulation of Monte Carlo applies to any process that produces trials randomly, but does not tell us anything about the underlying technique on its own.
Using the VaR system helps investors control market risk. Active management of the portfolio alters the fund's risk appearance. For instance, if the investor realizes an untimely rise in the fund's VaR, it is important to recognize the potential cause of such a change.
Using a firm-wide VaR evaluation makes it possible to calculate the combined risks from aggregated positions held within the institution by various trading desks and departments. Financial institutions may assess, using the data generated by VaR modeling, whether they have adequate capital reserves in place to cover losses or whether they need higher-than-acceptable risks to reduce concentrated holdings.
Written By Makisha Rahim