Publication:

Essays on Asset Pricing and Econometrics

Thumbnail Image

Open/View Files

Jin_gsas.harvard_0084L_11466.pdf (1.7 MB)

Date

2014-06-06

Authors

Published Version

Published Version

Journal Title

Journal ISSN

Volume Title

Publisher

The Harvard community has made this article openly available. Please share how this access benefits you.

Research Projects

Organizational Units

Journal Issue

Citation

Jin, Tao. 2014. Essays on Asset Pricing and Econometrics. Doctoral dissertation, Harvard University.

Abstract

This dissertation presents three essays on asset pricing and econometrics. The first chapter identifies rare events and long-run risks simultaneously from a rich data set (the Barro-Ursua macroeconomic data set) and evaluates their contributions to asset pricing in a unified framework. The proposed model of rare events and long-run risks is estimated using a Bayesian Markov-chain Monte-Carlo method, and the estimates for the disaster process are closer to the data than those in the previous studies. Major evaluation results in asset pricing include: (1) for the unleveraged annual equity premium, the predicted values are 4.8%, 4.2%, and 1.0%, respectively; (2) for the Sharpe ratio, the values are 0.72, 0.66, and 0.15, respectively.

Description

Other Available Sources

Research Data

Keywords

Economics, Asset Pricing, Equity Premium, Long-Run Risks, Power Law, Rare Events, Root Cancellation

Terms of Use

This article is made available under the terms and conditions applicable to Other Posted Material (LAA), as set forth at Terms of Service

URI

http://nrs.harvard.edu/urn-3:HUL.InstRepos:12269843

Collections

FAS Theses and Dissertations

Endorsement

Review

Supplemented By

Related Stories