Using RISKOptimizer/Evolver in Determining Optimal Swaps for Risk Mitigation

Determining optimal levels of financial derivatives to mitigate risk oftentimes involve normal or lognormal distribution along with a standard deviation. In this free live webcast, Roy Nersesian proposes that simulating future prices can be done by examining historical daily or weekly price changes to obtain a histogram of absolute price changes. The histogram is used to set the 50% and 100% cumulative probability points with RISKOptimizer or Evolver used to obtain the optimal values for A, B, and C for the following equations:

Price change = (A^B^C(.5) – A) for the 50% cumulative distribution and (A^B^C(1.0) – A) for the 100% cumulative probability. Once obtaining the A, B and C values, price changes can be generated using the formula:

Price change  = (A^B^C(Rand()) – A)

After establishing upper and lower bounds for future prices, it is possible to simulate future prices for any stock or commodity. This webcast will demonstrate this process for determining the optimal level of swaps for an oil project where low oil prices present a financial risk. This application will be expanded to include interest and currency exchange rates. RISKOptimizer will be used to obtain the optimal level of oil, interest and currency rate swaps. The webcast will end by modeling IBM stock price changes and then using simulated prices of IBM to determine the breakeven call option premium for covered and naked calls.