VAR Methodology Used for Exchange Risk Measurement and Prevention

In this article we discuss one of the modern risk measuring techniques Value-at-Risk (VaR). Currently central banks in major money centers, under the auspices of the BIS Basle Committee, adopt the VaR system to evaluate the market risk of their supervised banks. Banks regulators ask all commercial banks to report VaRs with their internal models. Value at risk (VaR) is a powerful tool for assessing market risk, but it also imposes a challenge. Its power is its generality. Unlike market risk metrics such as the Greeks, duration and convexity, or beta, which are applicable to only certain asset categories or certain sources of market risk, VaR is general. It is based on the probability distribution for a portfolio’s market value. Value at Risk (VAR) calculates the maximum loss expected (or worst case scenario) on an investment, over a given time period and given a specified degree of confidence. There are three methods by which VaR can be calculated: the historical simulation, the variance-covariance method and the Monte Carlo simulation. The variance-covariance method is easiest because you need to estimate only two factors: average return and standard deviation. However, it assumes returns are well-behaved according to the symmetrical normal curve and that historical patterns will repeat into the future. The historical simulation improves on the accuracy of the VAR calculation, but requires more computational data; it also assumes that “past is prologue”. The Monte Carlo simulation is complex, but has the advantage of allowing users to tailor ideas about future patterns that depart from historical patterns.