{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/87440"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/87440","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Three Essays on Empirical Asset Pricing","abstract":"The third chapter estimates the conditional variance of daily stock returns using an extended GARCH model with event-related dummy variables to capture the predictable components of volatility change, such as earnings announcements, macroeconomic announcements, day-of-the-week effects, etc. We examine the out-of-sample forecasting ability and find this model provides a better performance compared to the usual GARCH(1,1) volatility model. In addition, we find that the dependence on the random components increases after we include the predictable components. This implies that modeling volatilities using only past returns without other predictable variables could underestimate the persistence levels of volatilities and thus bias the volatility forecasts, especially those over long horizons.","abstract_html":"The third chapter estimates the conditional variance of daily stock returns using an extended GARCH model with event-related dummy variables to capture the predictable components of volatility change, such as earnings announcements, macroeconomic announcements, day-of-the-week effects, etc. We examine the out-of-sample forecasting ability and find this model provides a better performance compared to the usual GARCH(1,1) volatility model. In addition, we find that the dependence on the random components increases after we include the predictable components. This implies that modeling volatilities using only past returns without other predictable variables could underestimate the persistence levels of volatilities and thus bias the volatility forecasts, especially those over long horizons.","abstract_has_math":false,"creators":["Deng, Qian"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Finance","degree_department":null,"school":null,"contributors":["Pearson, Neil D."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-28T16:03:16Z","date_published":"2015-09-28T16:03:16Z","updated_at":"2026-07-22T22:26:30Z","subjects":["Economics, Finance"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3314760"],"render_values":[{"text":"(MiAaPQ)AAI3314760","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/87440","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Pearson, Neil D."]},{"key":"dc:creator","label":"Author","values":["Deng, Qian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-28T16:03:16Z","10000-01-01","2008"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Finance"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Economics, Finance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/87440","(MiAaPQ)AAI3314760"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The third chapter estimates the conditional variance of daily stock returns using an extended GARCH model with event-related dummy variables to capture the predictable components of volatility change, such as earnings announcements, macroeconomic announcements, day-of-the-week effects, etc. We examine the out-of-sample forecasting ability and find this model provides a better performance compared to the usual GARCH(1,1) volatility model. In addition, we find that the dependence on the random components increases after we include the predictable components. This implies that modeling volatilities using only past returns without other predictable variables could underestimate the persistence levels of volatilities and thus bias the volatility forecasts, especially those over long horizons.","Made available in DSpace on 2015-09-28T16:03:16Z (GMT). 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In addition, we find that the dependence on the random components increases after we include the predictable components. This implies that modeling volatilities using only past returns without other predictable variables could underestimate the persistence levels of volatilities and thus bias the volatility forecasts, especially those over long horizons.","Made available in DSpace on 2015-09-28T16:03:16Z (GMT). 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