Optimisation of Complex Financial Models Using Nature-Inspired Techniques

This paper discusses applications of nature-inspired computational techniques in optimisation problems encountered in portfolio selection and applied econometrics. By means of an empirical study, we show how particle swarm intelligence can be effectively used in the estimation of a GARCH and an EGARCH model, two popular econometric parametrisations for the volatility of financial prices. We discuss several issues emerging from the application of nature-inspired techniques in financial optimisation