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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Cummings, Jonathon Patrick"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Economics & Finance","degree_department":null,"school":null,"contributors":["David T. Flynn"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-01-01T08:00:00Z","date_published":"2013-01-01T08:00:00Z","updated_at":"2026-07-24T03:26:24Z","subjects":["GARCH, genetic algorithm, optimization"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.und.edu/theses/1411","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["David T. Flynn"]},{"key":"dc:creator","label":"Author","values":["Cummings, Jonathon Patrick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics & Finance"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["GARCH, genetic algorithm, optimization"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.und.edu/theses/1411"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Optimization Of The GARCH Model Parameters Using A Genetic Algorithm"]}]}],"canonical_facts":{"dc:contributor":["David T. Flynn"],"dc:creator":["Cummings, Jonathon Patrick"],"dc:description.abstract":["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.
"],"dc:identifier":["https://commons.und.edu/theses/1411"],"dc:subject":["GARCH, genetic algorithm, optimization"],"dc:title":["Optimization Of The GARCH Model Parameters Using A Genetic Algorithm"],"thesis:degree_discipline":["Economics & Finance"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T03:26:24Z"}