Here we develop methods for e±cient pricing multidimensional discrete-time American and Bermudan options by using regression based algorithms together with a new approach towards constructing upper bounds for the price of the option...

Here we develop an approach for efficient pricing discrete-time American and Bermudan options which employs the fact that such options are equivalent to theEuropean ones with a consumption, combined with analysis of the market model over a small number of steps ahead. This approach allows...

In this paper we carry over the concept of reverse probabilistic representations developed in Milstein, Schoenmakers, Spokoiny (2004) for diffusion processes, to discrete time Markov chains. We outline the construction of reverse chains in several situations and apply this to processes which are...

In this paper, we review the most common specifications of discrete-time stochasticvolatility (SV) models and illustrate the major principles of corresponding MarkovChain Monte Carlo (MCMC) based statistical inference. We provide a hands-on approachwhich is easily implemented in empirical...

The calibration of option pricing models leads to the minimization of an error functional. We show that its usual specification as a root mean squared error implies fluctuating exotics prices and possibly wrong prices...

Option pricing models are calibrated to market data of plain vanillas by minimization of an error functional. From the economic viewpoint, there are several possibilities to measure the error between the market and the model. These different specifications of the error give rise to different...