<p>In this thesis, I develop two sets of methods to help understand two distinct but also</p><p>related issues in financial economics.</p><p>First, representative agent models have been successfully applied to explain asset</p><p>market phenomenons. They are often simple to work with and appeal to intuition by</p><p>permitting a direct link between the agent's optimization behavior and asset market</p><p>dynamics. However, their particular modeling choices sometimes yield undesirable</p><p>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.</p><p>While the first two chapters focus on the diagnosis of a particular model, Chapter</p><p>3 and 4 study the joint inference of a group of models or risk factors. Drawing on</p><p>multiple hypothesis testing in the statistics literature, Chapter 3 shows that many of</p><p>the risk factors documented by the academic literature are likely to be false. It also</p><p>proposes a new statistical framework to study multiple hypothesis testing under test</p><p>correlation and hidden tests. Chapter 4 further studies the statistical properties of</p><p>this framework through simulations.</p> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/9108 |
| Date | January 2014 |
| Creators | Liu, Yan |
| Contributors | Harvey, Campbell R |
| Source Sets | Duke University |
| Detected Language | English |
| Type | Dissertation |
Page generated in 0.0021 seconds