This paper demonstrates that it is possible to improve significantly on the estimated call prices obtained with the regression and simulation-based least-squares Monte Carlo method by using put-call symmetry. The results show that, for a large sample of options with characteristics of relevance...

Recently it was shown that the estimated American call prices obtained with regression and simulation based methods can be significantly improved on by using put-call symmetry. This paper extends these results and demonstrates that it is also possible to significantly reduce the variance of the...

This paper considers estimation of price sensitivities, so-called Greeks, for American style options using flexible simulation methods combined with initial dispersed state variables. The asymptotic properties of the estimators are studied and convergence of the method is established under mild...

This paper demonstrates that it is possible to improve significantly on the estimated call prices obtained with the regression and simulation based Least-Squares Monte-Carlo method of Longstaff & Schwartz (2001) by using put-call symmetry. Results show that the symmetric method performs much...

This paper proposes a new method for pricing American options that uses importance sampling to reduce estimator bias and variance in simulation-and-regression based methods. Our suggested method uses regressions under the importance measure directly, instead of under the nominal measure as is...

It contains an introduction to how simulation methods can be used to price American options and a discussion of various existing methods. An application using one of these methods, the regression based method, to the GARCH option pricing model is also provided