A Multilevel Monte Carlo Method for the Valuation of Swing Options

In this study, we propose a novel approach for the valuation of swing options. Swing options are a kind of American options with multiple exercise rights traded in energy markets. Longstaff and Schwartz have suggested a regression-based Monte Carlo method known as the least-squares Monte Carlo (LSMC) method to value American options. In this work, first we introduce the LSMC method for the pricing of swing options. Then, to achieve a desired accuracy for the price estimation, we combine the idea of LSMC with multilevel Monte Carlo (MLMC) method. Finally, to illustrate the proper behavior of this combination, we conduct numerical results based on the Black–Scholes model. Numerical results illustrate the efficiency of the proposed approach.

A Partial Element Equivalent Circuit-metamodel combination for fast tolerance analysis of electromagnetic systems

We propose a combination of the Partial Element Equivalent Circuit method with metamodelling in order to achieve fast tolerance analysis of electromagnetic systems. The proposed model combination can be interpreted as a multifidelity modelling approach. <br>This technique is inspired by the Multilevel Monte Carlo method and provides great benefits in terms of computational resources.

A Partial Element Equivalent Circuit-metamodel combination for fast tolerance analysis of electromagnetic systems

We propose a combination of the Partial Element Equivalent Circuit method with metamodelling in order to achieve fast tolerance analysis of electromagnetic systems. The proposed model combination can be interpreted as a multifidelity modelling approach. <br>This technique is inspired by the Multilevel Monte Carlo method and provides great benefits in terms of computational resources.

Multilevel Monte Carlo by using the Halton sequence

Monte Carlo (MC) simulation depends on pseudo-random numbers. The generation of these numbers is examined in connection with the Brownian motion. We present the low discrepancy sequence known as Halton sequence that generates different stochastic samples in an equally distributed form. This will increase the convergence and accuracy using the generated different samples in the Multilevel Monte Carlo method (MLMC). We compare algorithms by using a pseudo-random generator and a random generator depending on a Halton sequence. The computational cost for different stochastic differential equations increases in a standard MC technique. It will be highly reduced using a Halton sequence, especially in multiplicative stochastic differential equations.