Simulation estimators, such as indirect inference or simulated maximum likelihood, are successfully employed for … variance suffers from an additional component, which depends on the stochastic simulation involved in the estimation procedure …. To reduce this undesirable effect, one could increase the number of simulations (or the length of each simulation) and …

In Longstaff and Schwartz (2001) a method for American option pricing using simulation and regression is suggested, and … since then the method has rapidly gained importance. However, the idea of using regression and simulation for American …

Algorithms for simulation of a Lévy process X(t) are discussed, with particular emphasis on two algorithms … perform substantially better. Other algorithms are briefly surveyed and we sketch a new one for simulation of a tempered …

Kriging provides metamodels for deterministic and random simulation models. Actually, there are several types of … estimation of the trend in the input-output data of the underlying simulation model; this estimation deteriorates the Kriging … replications that varies with the input combination of the simulation model. To compare the performance of intrinsic Kriging and …

In this short note we derive an exact simulation scheme for the joint distribution of (r(t),N(t)), where r denotes the … generate biases if the model uses larger simulation time-steps.Large time-step simulation of short rate models and the American …

We present a stochastic simulation model for estimating forward-looking corporate probability of default and loss given … identify the default condition, and solve the model by Monte Carlo simulation. First, we present the model; then we show how to …