We propose a new methodology for discrete time dynamic hedging with transaction costs that has three key performance features. First, the methodology can accommodate the use of a wide range of objective functions, from the use of many types of utility functions to the more traditional objectives...

This paper generalizes Moody's correlated binomial default distribution for homogeneous (exchangeable) credit portfolios, which was introduced by Witt, to the case of inhomogeneous portfolios. We consider two cases of inhomogeneous portfolios. In the first case, we treat a portfolio whose assets...

This paper examines an alternative approach to interest rate modeling, in which the nonlinear and random behavior of interest rates is captured by a stochastic differential equation evolving on a curved state space. We consider as candidate state spaces the matrix Lie groups; these offer not...

Importance sampling is a promising variance reduction technique for Monte Carlo simulation-based derivative pricing. Existing importance sampling methods are based on a parametric choice of the proposal. This article proposes an algorithm that estimates the optimal proposal non-parametrically...

Spatial dependency has been studied in several research areas, such as environmental criminology, economic geography, environmental sciences, and urban economics. However, it has essentially been overlooked in other subfields of economics and in the field of finance as a whole. A key element at...

This paper introduces new variance reduction techniques and computational improvements to Monte Carlo methods for pricing American-style options. For simulation algorithms that compute lower bounds of American option values, we apply martingale control variates and introduce the local policy...

We present four new methods for approximating the drift in the LIBOR market model when performing very long steps. These are compared with a variety of existing methods, including PPR, Glasserman-Zhao and predictor-corrector. We find that two of them, which use correlation adjustments to better...

We describe a simple Importance Sampling strategy for Monte Carlo simulations based on a least-squares optimization procedure. With several numerical examples, we show that such Least-squares Importance Sampling (LSIS) provides efficiency gains comparable to the state-of-the-art techniques, for...