Sensitivity Simulations: A Faster Alternative to Monte Carlo

Daryanani, Gobind
September 2002
Journal of Financial Planning;Sep2002, Vol. 15 Issue 9, p104
Academic Journal
This article introduces a simulation approach that can be used in financial planning that is significantly faster than the Monte Carlo simulation method. The approach, referred to as sensitivity simulation, is based on deriving sensitivities of the result (objective function) to the input random variables (such as rates of return). Because of the inherent speed, this approach lends itself to financial planning analyses that require repeated simulations: for demonstrating the risk-reward trade-offs of a range of strategies, and when searching for optimum solutions. Sensitivity simulation is illustrated with two examples: establishing asset allocations for a retirement plan, and finding the optimum time to exercise and sell employee stock options. The stochastic approach most commonly used by financial planners is Monte Carlo simulation. In this approach the analysis is repeated a few thousand times, using different sets of random numbers, based on the statistics of the inputs. An alternative that also has some acceptance is historical simulations, where the analysis is based on using existing historical data for the asset classes under consideration. Issues that have been raised related to using Monte Carlo simulations include how sensitive the results will be to the input statistics, what kinds of correlations need to be included in the analyses, and the sensitivity of the results to the number of iterations used in the analyses.


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