Essays on finance, learning, and macroeconomics

Abstract

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.