This financial risk analysis example demonstrates the use of the Corrmat function to correlate multiple @RISK distributions. The distributions are correlated using a matrix of coefficients that specify the relationship between each pair of functions. The coefficients must be between -1 and +1, with a value of +1 indicating a perfect correlation, 0 indicating no correlation, and -1 indicating a perfect negative correlation. In this example three variables, the current US interest rate, the Pound/$ exchange rate, and the Euro/$ exchange rate, are correlated using a 3×3 matrix. Correlation matrix inconsistency occurs when a matrix that is mathematically impossible to realize is entered. If this happens, @RISK will try to adjust your matrix. However, you may have some correlation coefficients that you do not wish to be adjusted, while others are more or less free to be changed. This information may be entered using an adjustment weight matrix.