Essays on Financial Economics

In this thesis, I develop two sets of methods to help understand two distinct but also

related issues in financial economics.

First, representative agent models have been successfully applied to explain asset

market phenomenons. They are often simple to work with and appeal to intuition by

permitting a direct link between the agent's optimization behavior and asset market

dynamics. However, their particular modeling choices sometimes yield undesirable

or even counterintuitive consequences. Several diagnostic tools have been developed by the asset pricing literature to detect these unwanted consequences. I contribute to this literature by developing a new continuum of nonparametric asset pricing bounds to diagnose representative agent models. Chapter 1 lays down the theoretical framework and discusses its relevance to existing approaches. Empirically, it uses bounds implied by index option returns to study a well-known class of representative agent models|the rare disaster models. Chapter 2 builds on the insights of Chapter 1 to study dynamic models. It uses model implied conditional variables to sharpen asset pricing bounds, allowing a more powerful diagnosis of dynamic models.

While the first two chapters focus on the diagnosis of a particular model, Chapter

3 and 4 study the joint inference of a group of models or risk factors. Drawing on

multiple hypothesis testing in the statistics literature, Chapter 3 shows that many of

the risk factors documented by the academic literature are likely to be false. It also

proposes a new statistical framework to study multiple hypothesis testing under test

correlation and hidden tests. Chapter 4 further studies the statistical properties of

this framework through simulations.