
Be the first to like this
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Published on
Monte Carlo simulation is one of the most important numerical methods in financial derivative pricing and risk management. Due to the increasing sophistication of exotic derivative models, Monte Carlo becomes the method of choice for numerical implementations because of its flexibility in highdimensional problems. However, the method of discretization of the underlying stochastic differential equation (SDE) has a significant effect on convergence. In addition the choice of computing platform and the exploitation of parallelism offers further efficiency gains. We consider here the effect of higher order discretization methods together with the possibilities opened up by the advent of programmable graphics processing units (GPUs) on the overall performance of Monte Carlo and quasiMonte Carlo methods.
Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.
Be the first to comment