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1512. Parallel computing in optimization methods used in estimating risk-neutral densities through option prices

Invited abstract in session TB-32: Nonsmooth optimization and applications, Part II, stream Advances in large scale nonlinear optimization.

Tuesday, 10:30-12:00
Room: 41 (building: 303A)

Authors (first author is the speaker)

1. Antonio Santos
Economics, University of Coimbra
2. Ana Monteiro
School of Economics, University of Coimbra

Abstract

Option pricing is an active area of research in financial economics. The risk-neutral density is a critical element in pricing derivative assets, and it can be estimated using nonparametric kernel methods. Recent research considered large-scale optimization problems, which significantly improved the robustness of estimating risk-neutral densities through observed option prices. In nonparametric estimation methods, kernel bandwidth estimates are crucial elements. However, they also represent the most computational challenge in implementing such methods, namely when large-scale optimization problems are used in curve fitting. Some of the computational challenges can be solved by considering parallel computing algorithms performed using Graphical Processing Units. We propose a tailor-made Cross-Validation criterion function to define optimal bandwidths (two parameters) associated with the risk-neutral estimation problem, defined through an optimization problem with non-convex objective functions. By implementing a grid-search approach within a big data scenario and through a large-scale optimization problem, computational times can be prohibitive for applying these methods in real-time decision-making. Parallel computing methods within a grid-search optimization algorithm substantially reduce computational times, allowing for more effective decision-making processes related to risk-neutral density estimation and option pricing.

Keywords

Status: accepted

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