{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/85682"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/85682","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Essays on Semiparametric Methods in Finance","abstract":"With the proliferation of computing power and storage capabilities, financial data can be collected at shorter intervals than just a few years ago; for example, each transaction from an exchange can be recorded. Unlike previous studies that model the time between transactions completely parametrically, in this paper we use the semiparametric survival model of Kooperberg, Stone, and Troung (1995). The primary objective of this paper is to examine how important trade characteristics are on the prices spacings and a measure of instantaneous volatility using the semiparametric survival model. Graphical methods and specification tests indicate the significant dependence between arrival times can be sufficiently modeled in the semiparametric framework. When the semiparametric model is compared to a theoretical model of geometric Brownian motion, diagnostics reveal the semiparametric model outperforms the hypothetical model. The empirical findings are that information flow variables, such as volume, spreads and trading imbalances, predict more rapid price revisions. Tests of different market microstructure models lends credence to the theoretical assertions that movement in prices are due to informed traders and not liquidity traders.","abstract_html":"With the proliferation of computing power and storage capabilities, financial data can be collected at shorter intervals than just a few years ago; for example, each transaction from an exchange can be recorded. Unlike previous studies that model the time between transactions completely parametrically, in this paper we use the semiparametric survival model of Kooperberg, Stone, and Troung (1995). The primary objective of this paper is to examine how important trade characteristics are on the prices spacings and a measure of instantaneous volatility using the semiparametric survival model. Graphical methods and specification tests indicate the significant dependence between arrival times can be sufficiently modeled in the semiparametric framework. When the semiparametric model is compared to a theoretical model of geometric Brownian motion, diagnostics reveal the semiparametric model outperforms the hypothetical model. The empirical findings are that information flow variables, such as volume, spreads and trading imbalances, predict more rapid price revisions. Tests of different market microstructure models lends credence to the theoretical assertions that movement in prices are due to informed traders and not liquidity traders.","abstract_has_math":false,"creators":["Weikel, Brian Keith"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Economics","degree_department":null,"school":null,"contributors":["Koenker, Roger W."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T22:47:55Z","date_published":"2015-09-25T22:47:55Z","updated_at":"2026-07-22T22:26:25Z","subjects":["Economics, Finance"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI9990188"],"render_values":[{"text":"(MiAaPQ)AAI9990188","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/85682","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koenker, Roger W."]},{"key":"dc:creator","label":"Author","values":["Weikel, Brian Keith"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T22:47:55Z","10000-01-01","2000"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Economics"]},{"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/2142/85682","(MiAaPQ)AAI9990188"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["With the proliferation of computing power and storage capabilities, financial data can be collected at shorter intervals than just a few years ago; for example, each transaction from an exchange can be recorded. 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