The least square Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz (2001) is widely used for pricing American options. The LSM estimator contains undesirable look-ahead bias, and the conventional technique of removing it necessitates doubling simulations. We present the...

We propose a new nonparametric method for valuing American options. We extract the risk neutral moments using a set of option data and incorporate them into the entropy framework as constraints to recover the risk-neutral pricing measure. With the recovered risk-neutral measure, we generate...

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...

We present a simple and numerically efficient approach to the calibration of the Heston stochastic volatility model with piecewise constant parameters. Extending the original ansatz for the characteristic function, proposed in the seminal paper by Heston, to the case of piecewise constant...