Computation of vector sublattices and minimal lattice-subspaces of R^k. Applications in finance

TL;DR

This paper computationally studies algorithms for determining vector sublattices and minimal lattice-subspaces in R^k, highlighting their practical applications in finance such as market completion and portfolio insurance.

Contribution

It provides a computational analysis of Polyrakis algorithms for lattice structures, demonstrating their usefulness in economic financial applications.

Findings

Algorithms effectively determine lattice structures in R^k.

Applications include security market completion and portfolio insurance.

Findings support practical financial modeling improvements.

Abstract

In this article we perform a computational study of Polyrakis algorithms presented in [12,13]. These algorithms are used for the determination of the vector sublattice and the minimal lattice-subspace generated by a finite set of positive vectors of R^k. The study demonstrates that our findings can be very useful in the field of Economics, especially in completion by options of security markets and portfolio insurance.