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...

In this paper we propose a jump-diffusion Libor model with jumps in a high-dimensional space (m) and test a stable non-parametric calibration algorithm that takes into account a given local covariance structure. The algorithm returns smooth and simply structured Levy densities, and penalizes...

In this paper we present a Markov-Functional hybrid interest rate/foreign exchange model that allows calibration to given market volatility surfaces in both dimensions simultaneously. This is achieved by extending the approach introduced by Fries and Rott by a functional for the foreign exchange...

The aim of this paper is to provide an assessment of alternative frameworks for the fair valuation of life insurance contracts with a predominant financial component, in terms of impact on the market consistent price of the contracts, the embedded options, and the capital requirements for the...

Financial time series have two features which, in many cases, prevent the use of conventional estimators of volatilities and correlations: leptokurtotic distributions and contamination of data with outliers. Other techniques are required to achieve stable and accurate results. In this paper, we...

Kolodko and Schoenmakers (2006) and Bender and Schoenmakers (2006) introduced a policy iteration that allows the achievement of a tight lower approximations of the price for early exercise options via a nested Monte Carlo simulation in a Markovian setting. In this paper we enhance the algorithm...

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...

For financial risk management it is of vital interest to have good estimates for the correlations between the stocks. It has been found that the correlations obtained from historical data are covered by a considerable amount of noise, which leads to a substantial error in the estimation of the...