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Monte-Carlo Methods

Overview

Monte-Carlo simulations are among the most common numerical methods in computational finance. They are used when closed form solutions or other numerical methods are not practical or do not exist. Complex financial problems are often evaluated numerically using Monte-Carlo methods. For example, Monte-Carlo derivative pricing determines the current value based on a large number of random experiments and some form of statistical analysis to obtain the results.

This white paper demonstrates the benefits of using the Xcelerit platform to efficiently run Monte-Carlo simulations using multi-core CPUs and graphics processing units (GPUs). The generic approach for Monte-Carlo simulations using the Xcelerit platform is presented and performance figures are given for the specific examples of pricing European options and pricing a portfolio of LIBOR swaptions.

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