Essays on finance, learning, and macroeconomics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2012. === Cataloged from PDF version of thesis. === Includes bibliographical references (p. 189-198). === This thesis consists of four essays on finance, learning, and macroeconomics. The first essay studies whether learning...

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Main Author: Doyle, Joseph Buchman, Jr
Other Authors: Anna Mikusheva and Ricardo Caballero.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2013
Subjects:
Online Access:http://hdl.handle.net/1721.1/77791
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spelling ndltd-MIT-oai-dspace.mit.edu-1721.1-777912019-05-02T16:19:45Z Essays on finance, learning, and macroeconomics Doyle, Joseph Buchman, Jr Anna Mikusheva and Ricardo Caballero. Massachusetts Institute of Technology. Dept. of Economics. Massachusetts Institute of Technology. Dept. of Economics. Economics. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 189-198). This thesis consists of four essays on finance, learning, and macroeconomics. The first essay studies whether learning can explain why the standard consumption-based asset pricing model produces large pricing errors for U.S. equity returns. I prove that under learning standard moment conditions need not hold in finite samples, leading to pricing errors. Simulations show that learning can generate quantitatively realistic pricing errors and a substantial equity risk premium. I find that a model with learning is not rejected in the data, producing pricing errors that are statistically indistinguishable from zero. The second essay (co-authored with Anna Mikusheva) studies the properties of the common impulse response function matching estimator (IRFME) in settings with many parameters. We prove that the common IRFME is consistent and asymptotically normal only when the horizon of IRFs being matched grows slowly enough. We use simulations to evaluate the performance of the common IRFME in a practical example, and we compare it with an infrequently used bias corrected approach, based on indirect inferences. Our findings suggest that the common IRFME performs poorly in situations where the sample size is not much larger than the horizon of IRFs being matched, and in those situations, the bias corrected approach with bootstrapped standard errors performs better. The third essay (co-authored with Ricardo Caballero) documents that, in contrast with their widely perceived excess return, popular carry trade strategies yield low systemicrisk- adjusted returns. In contrast, hedging the carry with exchange rate options produces large returns that are not a compensation for systemic risk. We show that this result stems from the fact that the corresponding portfolio of exchange rate options provides a cheap form of systemic insurance. The fourth essay shows that the documented overbidding in pay-as-you-go auctions relative to a static model can be explained by the presence of a small subset of aggressive bidders. I argue that aggressive bidding can be rational if users are able to form reputations that deter future competition, and I present empirical evidence that this is the case. In auctions without any aggressive bidders, there is no evidence of overbidding in PAYGA. by Joseph Buchman Doyle, Jr. Ph.D. 2013-03-13T15:47:27Z 2013-03-13T15:47:27Z 2012 2012 Thesis http://hdl.handle.net/1721.1/77791 828101327 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 198 p. application/pdf Massachusetts Institute of Technology
collection NDLTD
language English
format Others
sources NDLTD
topic Economics.
spellingShingle Economics.
Doyle, Joseph Buchman, Jr
Essays on finance, learning, and macroeconomics
description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2012. === Cataloged from PDF version of thesis. === Includes bibliographical references (p. 189-198). === This thesis consists of four essays on finance, learning, and macroeconomics. The first essay studies whether learning can explain why the standard consumption-based asset pricing model produces large pricing errors for U.S. equity returns. I prove that under learning standard moment conditions need not hold in finite samples, leading to pricing errors. Simulations show that learning can generate quantitatively realistic pricing errors and a substantial equity risk premium. I find that a model with learning is not rejected in the data, producing pricing errors that are statistically indistinguishable from zero. The second essay (co-authored with Anna Mikusheva) studies the properties of the common impulse response function matching estimator (IRFME) in settings with many parameters. We prove that the common IRFME is consistent and asymptotically normal only when the horizon of IRFs being matched grows slowly enough. We use simulations to evaluate the performance of the common IRFME in a practical example, and we compare it with an infrequently used bias corrected approach, based on indirect inferences. Our findings suggest that the common IRFME performs poorly in situations where the sample size is not much larger than the horizon of IRFs being matched, and in those situations, the bias corrected approach with bootstrapped standard errors performs better. The third essay (co-authored with Ricardo Caballero) documents that, in contrast with their widely perceived excess return, popular carry trade strategies yield low systemicrisk- adjusted returns. In contrast, hedging the carry with exchange rate options produces large returns that are not a compensation for systemic risk. We show that this result stems from the fact that the corresponding portfolio of exchange rate options provides a cheap form of systemic insurance. The fourth essay shows that the documented overbidding in pay-as-you-go auctions relative to a static model can be explained by the presence of a small subset of aggressive bidders. I argue that aggressive bidding can be rational if users are able to form reputations that deter future competition, and I present empirical evidence that this is the case. In auctions without any aggressive bidders, there is no evidence of overbidding in PAYGA. === by Joseph Buchman Doyle, Jr. === Ph.D.
author2 Anna Mikusheva and Ricardo Caballero.
author_facet Anna Mikusheva and Ricardo Caballero.
Doyle, Joseph Buchman, Jr
author Doyle, Joseph Buchman, Jr
author_sort Doyle, Joseph Buchman, Jr
title Essays on finance, learning, and macroeconomics
title_short Essays on finance, learning, and macroeconomics
title_full Essays on finance, learning, and macroeconomics
title_fullStr Essays on finance, learning, and macroeconomics
title_full_unstemmed Essays on finance, learning, and macroeconomics
title_sort essays on finance, learning, and macroeconomics
publisher Massachusetts Institute of Technology
publishDate 2013
url http://hdl.handle.net/1721.1/77791
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