Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group


First Advisor

Joao Gomes

Second Advisor

Amir Yaron


This dissertation consists of two chapters. In the first chapter, I study both theoretical and quantitative implications of the counter-cyclical capital buffers introduced with the Basel Accord III. The proposed adjustment effectively translates into capital charges that vary over time. To this end, I develop a tractable general equilibrium model and use it to solve for optimal state-dependent capital requirements. An optimal policy trades off reduced inefficient lending with reduced liquidity provision. Quantitatively, I find that the optimal Ramsey policy requires pro-cyclical capital ratios that mostly vary between 4% and 6% and depend on the output and bank credit growth, as well as the liquidity premium. Specifically, a one standard deviation increase in GDP (bank credit) translates into 0.6% (0.1%) increase in the capital charges, while a one standard deviation increase in liquidity premium leads to a 0.2% drop. The welfare gain of implementing this Ramsey policy is relatively large.

In the second chapter, I, jointly with Scott Richard, Ivan Shaliastovich and Amir Yaron, investigate the channels of asset price variation when a representative agent owns the entire corporate sector. Utilizing novel market data on corporate bonds we measure the aggregate market value of U.S. corporate assets and their payouts to investors. Total asset payouts are very volatile, turn negative when corporations raise capital, and in contrast to procyclical cash payouts are acyclical. This challenges the notion of risk and return since the risk premium on corporate assets is comparable to the standard equity premium. To reconcile this evidence, we show that aggregate net issuances, which are acyclical and highly volatile, mask a strong exposure of total payouts’ cash components to low-frequency growth risks. We develop an asset-pricing framework to quantitatively assess this economic channel.