2026-09-18T19:15:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1391282022-01-15T03:26:35Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Lo, Andrew W.
Hansen, Peter G.
Sloan School of Management
2022-01-14T14:51:37Z
2022-01-14T14:51:37Z
2021-06
2021-06-03T18:05:07.524Z
https://hdl.handle.net/1721.1/139128
https://orcid.org/0000-0001-6351-0540
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.
Ph.D.
Massachusetts Institute of Technology
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http://rightsstatements.org/page/InC-EDU/1.0/
Essays in Financial Economics
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Doctor of Philosophy
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<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>
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