We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic...

We describe general multilevel Monte Carlo methods that estimate the price of an Asian option monitored at m fixed dates. For a variety of processes that can be simulated exactly, we prove that, for the same computational cost, our method yields an unbiased estimator with variance lower than the...

In this study, we use Neural Networks (NNs) to price American put options. We propose two NN models-a simple one and a more complex one-and we discuss the performance of two NN models with the Least-Squares Monte Carlo (LSM) method. This study relies on American put option market prices, for...

American Monte Carlo is a solution to the puzzle of calculating the value of derivatives with the right to an early exercise, when using Monte Carlo simulation. One of the technique uses regression of some suitable basis functions, which is a bit arbitrary, and could if made wrong render in...

In Monte Carlo simulation, Latin hypercube sampling (LHS) [McKay et al. (1979)] is a well-known variance reduction technique for vectors of independent random variables. The method presented here, Latin hypercube sampling with dependence (LHSD), extends LHS to vectors of dependent random...