Robust estimation of vector autoregression (VAR) models using genetic algorithms

Authors:
Ronald Hochreiter;Gerald Krottendorfer
Affiliations:
Department of Finance, Accounting and Statistics, WU Vienna University of Economics and Business, Austria;Department of Finance, Accounting and Statistics, WU Vienna University of Economics and Business, Austria
Venue:
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Year:
2013

Citing 8
Cited 0

A new evolutionary method for time series forecasting

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)

Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
New Introduction to Multiple Time Series Analysis

New Introduction to Multiple Time Series Analysis
Using evolutionary neural networks to test the influence of the choice of numeraire on financial time series modeling

EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
A preliminary investigation of overfitting in evolutionary driven model induction: implications for financial modelling

EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Modesty is the best policy: automatic discovery of viable forecasting goals in financial data

EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
An evolutionary approach to pattern-based time series segmentation

IEEE Transactions on Evolutionary Computation
Evolving seasonal forecasting models with genetic programming in the context of pricing weather-derivatives

EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present an implementation of a Vector autoregression (VAR) estimation model using Genetic Algorithms. The algorithm was implemented in R and compared to standard estimation models using least squares. A numerical example is presented to outline advantages of the GA approach.