Robust Regression with Optimisation Heuristics
Anthony Brabazon, Michael O’Neill and Dietmar Maringer, eds., NATURAL COMPUTING IN COMPUTATIONAL FINANCE, Volume 3, Springer 2010
26 Pages Posted: 13 Jul 2009 Last revised: 20 Oct 2010
Date Written: July 8, 2009
Linear regression is widely-used in finance. While the standard method to obtain parameter estimates, Least Squares, has very appealing theoretical and numerical properties, obtained estimates are often unstable in the presence of extreme observations which are rather common in financial time series. One approach to deal with such extreme observations is the application of robust or resistant estimators, like Least Quantile of Squares estimators. Unfortunately, for many such alternative approaches, the estimation is much more difficult than in the Least Squares case, as the objective function is not convex and often has many local optima. We apply different heuristic methods like Differential Evolution, Particle Swarm and Threshold Accepting to obtain parameter estimates. Particular emphasis is put on the convergence properties of these techniques for fixed computational resources, and the techniques’ sensitivity for different parameter settings.
Keywords: Optimisation heuristics, Robust Regression, Least Median of Squares
JEL Classification: C61, C13
Suggested Citation: Suggested Citation