{"id":{"repo_id":"brock","oai_identifier":"oai:brocku.scholaris.ca:10464/5122"},"canonical_url":"https://search.dev.ndltd.org/etd/brock/oai:brocku.scholaris.ca:10464/5122","repository":{"repo_id":"brock","name":"Brock University","base_url":"https://brocku.scholaris.ca/server/oai/request"},"display":{"title":"Forecasting the Yield Curve of Government Bonds: A Comparative Study","abstract":"For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields","abstract_html":"For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields","abstract_has_math":false,"creators":["He, Chao"],"institution":"Brock University","degree_name":"M.Sc. Management","degree_level":"Masters","degree_discipline":"Faculty of Business","degree_department":"Faculty of Business Programs","school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-11-21","date_published":"2013-11-21","updated_at":"2026-07-24T01:23:09Z","subjects":["yield curve","dynamic model","Kalman filter","Nelson and Siegel model"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10464/5122","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Faculty of Business Programs"]},{"key":"dc:creator","label":"Author","values":["He, Chao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2013-11-21T20:51:56Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2013-11-21T20:51:56Z"]},{"key":"dc:date.issued","label":"Date","values":["2013-11-21"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Faculty of Business"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.Sc. 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Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields"]},{"key":"dc:title","label":"Title","values":["Forecasting the Yield Curve of Government Bonds: A Comparative Study"]}]}],"canonical_facts":{"dc:contributor.department":["Faculty of Business Programs"],"dc:creator":["He, Chao"],"dc:date.accessioned":["2013-11-21T20:51:56Z"],"dc:date.available":["2013-11-21T20:51:56Z"],"dc:date.issued":["2013-11-21"],"dc:description.abstract":["For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields"],"dc:identifier.uri":["http://hdl.handle.net/10464/5122"],"dc:language.iso":["eng"],"dc:subject":["yield curve","dynamic model","Kalman filter","Nelson and Siegel model"],"dc:title":["Forecasting the Yield Curve of Government Bonds: A Comparative Study"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Faculty of Business"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.Sc. Management"],"thesis:institution_name":["Brock University"]},"updated_at":"2026-07-24T01:23:09Z"}