EDITORIAL: STOCHASTIC AND COMPUTATIONAL METHODS IN FINANCE

Radial basis function methods in computational finance

Radial basis function (RBF) based approximation methods for numerical solution of partial differential equations are interesting due to their potentially spectral accuracy and due to being meshfree. This could be especially beneficial for high dimensional problems, where meshing is non-trivial. In this work, we present different RBF approaches and evaluate them on a multi-asset option pricing p...

Sparse Grid Quadrature Methods for Computational Finance

Special Issue on Statistical and Computational Methods in Finance

In recent decades major developments in computational methods allowed revolutionary changes to take place in the statistical and econometric analysis of financial processes. Evaluating various forecasts and policy scenarios with their implied risk using advanced computational techniques and modern financial models is becoming more and more standard practice. The contents of this special issue r...

Computational Methods and Stochastic Models in Proteomics

The thesis addresses three problems arising from mass spectrometry (MS) data processing. It describes computational methods for solving them and stochastic models that formalize some of them. The first problem is redundancy elimination in liquid chromatography MS (LC-MS) images of peptides. An algorithm for isotopic envelopes detection based on the sweeping method is presented. It consists of g...

-Monotone Fourier Methods for Optimal Stochastic Control in Finance

Stochastic control problems in finance having complex controls inevitably give rise to low order accuracy, usually at most second order. Fourier methods are efficient at advancing the solution between control monitoring dates, but are not monotone. This gives rise to possible violations of arbitrage inequalities. We devise a preprocessing step for Fourier methods which involves projecting the G...