Monte Carlo methods are useful in solving a wide range of problems, both stochastic and deterministic, that cannot easily be solved using analytic methods. The authors describe a number of schemes for generating sequences of both pseudo-random and quasi-random numbers from a variety of probability distributions.

They discuss several variance reduction methods aimed at improving the efficiency and the robustness of the simulation process. The text illustrates the application of such methods to practical problems in finance and insurance by presenting several in-depth case studies, including a model for generating stochastic interest rates and issues having to do with the important concept of value at risk, a measure for assessing the risk and/or performance of assets and/or liabilities.