Optimization Of The GARCH Model Parameters Using A Genetic Algorithm

<p>Financial time series are often characterized by nonlinearity and volatility bunching. Standard regression analysis models cannot capture changing volatilities, potentially leading to erroneous results. The need to more completely model the characteristic volatilities inherent to financial time series eventually led to the creation of the GARCH model. Typical GARCH parameters are (1,1) incorporating a 1-period lag of the regression residual as well as a 1-period lag of the regression volatility. The primary question investigated in this paper is whether the typical GARCH(1,1) parameters are in fact optimal over all time periods and attempts to improve on the typical parameters by minimizing a modified AIC value using a genetic algorithm.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Economics & Finance

Year

2013

Author dc:creator

• Cummings, Jonathon Patrick

Contributors dc:contributor

• David T. Flynn

• GARCH, genetic algorithm, optimization

Repository record dc:identifier

https://commons.und.edu/theses/1411

OAI identifier oai:identifier

oai:commons.und.edu:theses-2412