This issue of The Journal of Computational Finance focuses on different aspects of calibration and optimization. Two papers even have the word "calibration" in their titles.In "Robust calibration of financial models using Bayesian estimators", Alok Gupta and Christoph Reisinger estimate prices of exotic options by attaining, from the Bayesian framework, posterior distributions for model parameters in a local volatility asset model. Numerical insights obtained from this framework are explained in detail.
In his paper "Adjoint algorithmic differentiation: calibration and implicit function theorem", Marc Henrard deals with a practical question arising during the computation of Greeks within a calibration procedure. The paper discusses the use of adjoint algorithmic differentiation in combination with the implicit function theorem in the calibration context.
In "Optimizing the Omega ratio using linear programming", Michalis Kapsos, Steve Zymler, Nicos Christofides and Berç Rustem explain a novel approach for computing the maximum Omega ratio in terms of a convex optimization problem formulation. The Omega ratio is a new performance measure that captures the downside and upside potentials of a constructed portfolio.
The final paper in the issue, "Credit risk contributions under the Vasicek one-factor model: a fast wavelet expansion approximation" by Luis Ortiz-Gracia and Josep J. Masdemont, presents an efficient wavelets valuation method for the value-at-risk and the expected shortfall of a credit portfolio as an alternative to Monte Carlo simulation. By using Haar wavelets, so-called staircase distributions, that arise for portfolios with exposure concentration, can be accurately approximated. The issue's optimization theme can therefore be interpreted for this paper in terms of "optimization of computing time".
I wish you enjoyable reading of this issue of The Journal of Computational Finance.
CWI - Dutch Center for Mathematics and Computer Science, Amsterdam
Credit risk contributions under the Vasicek one-factor model: a fast wavelet expansion approximation