{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139128"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139128","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Essays in Financial Economics","abstract":"Chapter 1 introduces novel preference formulations which capture aversion to ambiguity about unknown and potentially time-varying volatility. These preferences are compared with Gilboa and Schmeidler's maxmin expected utility as well as variational formulations of ambiguity aversion. The impact of ambiguity aversion is illustrated in a simple static model of portfolio choice, as well as a dynamic model of optimal contracting under repeated moral hazard. Implications for investor beliefs, optimal design of corporate securities, and asset pricing are explored. Chapter 2 develops a method informed by data and models to recover information about investor beliefs. This approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, this method allows for the direct use of sparse survey evidence to make these bounds more informative. Within this framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, evidence about investor beliefs is represented as a nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. This method is illustrated with a prototypical example from macro-finance using asset market data to infer belief restrictions for macroeconomic growth rates. Chapter 3 develops diagnostic tools to assess whether individual factor risk premia are identified from return data. We describe a necessary and sufficient condition for population identification, which we call the kernel-orthogonality condition. This condition can be thought of intuitively as the existence of a “true” factor mimicking portfolio, and is weaker than the standard rank condition commonly assumed for linear factor models. Furthermore, this condition remains meaningful even if the factor model is misspecified, as a condition for the identification of the factor risk premium consistent with minimal pricing error. We discuss test procedures to assess identification, and provide a novel test of the kernel-orthogonality condition in reduced-rank models. Finally, we apply our test methodology to assess identification of risk premia associated with consumption growth and intermediary leverage.","abstract_html":"Chapter 1 introduces novel preference formulations which capture aversion to ambiguity about unknown and potentially time-varying volatility. These preferences are compared with Gilboa and Schmeidler's maxmin expected utility as well as variational formulations of ambiguity aversion. The impact of ambiguity aversion is illustrated in a simple static model of portfolio choice, as well as a dynamic model of optimal contracting under repeated moral hazard. Implications for investor beliefs, optimal design of corporate securities, and asset pricing are explored. Chapter 2 develops a method informed by data and models to recover information about investor beliefs. This approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, this method allows for the direct use of sparse survey evidence to make these bounds more informative. Within this framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, evidence about investor beliefs is represented as a nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. This method is illustrated with a prototypical example from macro-finance using asset market data to infer belief restrictions for macroeconomic growth rates. Chapter 3 develops diagnostic tools to assess whether individual factor risk premia are identified from return data. We describe a necessary and sufficient condition for population identification, which we call the kernel-orthogonality condition. This condition can be thought of intuitively as the existence of a “true” factor mimicking portfolio, and is weaker than the standard rank condition commonly assumed for linear factor models. Furthermore, this condition remains meaningful even if the factor model is misspecified, as a condition for the identification of the factor risk premium consistent with minimal pricing error. We discuss test procedures to assess identification, and provide a novel test of the kernel-orthogonality condition in reduced-rank models. Finally, we apply our test methodology to assess identification of risk premia associated with consumption growth and intermediary leverage.","abstract_has_math":false,"creators":["Hansen, Peter G."],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Sloan School of Management","school":null,"contributors":[],"advisors":["Lo, Andrew W."],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-06","date_published":"2021-06","updated_at":"2026-07-22T22:21:51Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/139128","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lo, Andrew W."]},{"key":"dc:contributor.department","label":"Department","values":["Sloan School of Management"]},{"key":"dc:creator","label":"Author","values":["Hansen, Peter G."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-01-14T14:51:37Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-01-14T14:51:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-06"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctoral","Doctor of Philosophy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/139128"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Chapter 1 introduces novel preference formulations which capture aversion to ambiguity about unknown and potentially time-varying volatility. These preferences are compared with Gilboa and Schmeidler's maxmin expected utility as well as variational formulations of ambiguity aversion. The impact of ambiguity aversion is illustrated in a simple static model of portfolio choice, as well as a dynamic model of optimal contracting under repeated moral hazard. Implications for investor beliefs, optimal design of corporate securities, and asset pricing are explored. Chapter 2 develops a method informed by data and models to recover information about investor beliefs. This approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, this method allows for the direct use of sparse survey evidence to make these bounds more informative. Within this framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, evidence about investor beliefs is represented as a nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. This method is illustrated with a prototypical example from macro-finance using asset market data to infer belief restrictions for macroeconomic growth rates. Chapter 3 develops diagnostic tools to assess whether individual factor risk premia are identified from return data. We describe a necessary and sufficient condition for population identification, which we call the kernel-orthogonality condition. This condition can be thought of intuitively as the existence of a “true” factor mimicking portfolio, and is weaker than the standard rank condition commonly assumed for linear factor models. Furthermore, this condition remains meaningful even if the factor model is misspecified, as a condition for the identification of the factor risk premium consistent with minimal pricing error. We discuss test procedures to assess identification, and provide a novel test of the kernel-orthogonality condition in reduced-rank models. Finally, we apply our test methodology to assess identification of risk premia associated with consumption growth and intermediary leverage."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Essays in Financial Economics"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lo, Andrew W."],"dc:contributor.department":["Sloan School of Management"],"dc:creator":["Hansen, Peter G."],"dc:date.accessioned":["2022-01-14T14:51:37Z"],"dc:date.available":["2022-01-14T14:51:37Z"],"dc:date.issued":["2021-06"],"dc:description.abstract":["Chapter 1 introduces novel preference formulations which capture aversion to ambiguity about unknown and potentially time-varying volatility. These preferences are compared with Gilboa and Schmeidler's maxmin expected utility as well as variational formulations of ambiguity aversion. The impact of ambiguity aversion is illustrated in a simple static model of portfolio choice, as well as a dynamic model of optimal contracting under repeated moral hazard. Implications for investor beliefs, optimal design of corporate securities, and asset pricing are explored. Chapter 2 develops a method informed by data and models to recover information about investor beliefs. This approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, this method allows for the direct use of sparse survey evidence to make these bounds more informative. Within this framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, evidence about investor beliefs is represented as a nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. This method is illustrated with a prototypical example from macro-finance using asset market data to infer belief restrictions for macroeconomic growth rates. Chapter 3 develops diagnostic tools to assess whether individual factor risk premia are identified from return data. We describe a necessary and sufficient condition for population identification, which we call the kernel-orthogonality condition. This condition can be thought of intuitively as the existence of a “true” factor mimicking portfolio, and is weaker than the standard rank condition commonly assumed for linear factor models. Furthermore, this condition remains meaningful even if the factor model is misspecified, as a condition for the identification of the factor risk premium consistent with minimal pricing error. 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