Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression
This work addresses the specific problem of yield curve estimation for financial markets in Costa Rica, representing an incremental improvement over existing optimization techniques.
The study tackled the problem of estimating the yield curve for Costa Rica by applying combinatorial optimization metaheuristics to nonlinear regression models, achieving good results with Particle Swarm Optimization and Simulated Annealing that improved upon classical quasi-Newton methods.
The term structure of interest rates or yield curve is a function relating the interest rate with its own term. Nonlinear regression models of Nelson-Siegel and Svensson were used to estimate the yield curve using a sample of historical data supplied by the National Stock Exchange of Costa Rica. The optimization problem involved in the estimation process of model parameters is addressed by the use of four well known combinatorial optimization metaheuristics: Ant colony optimization, Genetic algorithm, Particle swarm optimization and Simulated annealing. The aim of the study is to improve the local minima obtained by a classical quasi-Newton optimization method using a descent direction. Good results with at least two metaheuristics are achieved, Particle swarm optimization and Simulated annealing. Keywords: Yield curve, nonlinear regression, Nelson-