Business Cycles and the Balance Sheets of the Financial and Non-financial Sectors (Job Market Paper)
I propose a dynamic model of financial intermediation to study the different roles of banks’ and firms’ net worths in real activity. Banks provide risky loans to multiple firms and use their diversified portfolio as collateral to borrow from households. This intermediation process allows funds to flow from households to firms. Banks require net worth for intermediation as they hold aggregate risks. The net worth of banks and firms are both state variables. In normal times, banks’ and firms’ net worths play the same role, and their sum determines the allocation of capital. During financial crises, shocks to banks’ net worth have an additional effect than standard financial frictions’ models: less intermediation implies tighter constraints and lower collateral, which reduces investment and output, even if shocks redistribute net worth from banks to firms. I estimate the model and find that the new mechanism accounts for 40% of the fall in output and 80% of the fall in banks net worth during the Great Recession. Finally, the model is consistent with the different dynamics of the loan share and credit spreads during the recessions of 1990, 2001, and 2008.
Macroprudential Policy with Liquidity Panics (with Daniel Garcia-Macia)
Runner-up of the Ieke van den Burg Prize for Research on Systemic Risk (2016), European Central Bank
We analyze the optimality of macroprudential policies in an environment where the role of the banking sector is to efficiently allocate liquid assets across firms. Informational frictions in the banking sector can lead to an interbank market freeze. Firms react to the breakdown of the banking system by inefficiently accumulating liquid assets by themselves. This reduces the demand for bank loans and bank profits, which further disrupts the financial sector and increases the probability of a freeze, inducing firms to hoard even more liquid assets. Liquidity panics provide a new rationale for stricter liquidity requirements, as this policy alleviates the information frictions in the banking sector and paradoxically can end up increasing aggregate investment. On the contrary, policies encouraging bank lending can have the opposite effect.
Credit Lines under Uncertainty Shocks
I develop a dynamic agency model of financial contracting in a continuous-time setting, where borrowing constraints appear as part of the optimal contract. The novelty of the paper relative to previous work is that volatility is stochastic and exogenous to the agent behavior. A line of credit appears in the optimal long-term contract similarly to DeMarzo and Sannikov (2006). The novelty of the contract is that the credit limit varies over time, as a function of the state of volatility. Credit limit does not vary monotonically over firms. When uncertainty increases, credit limits are reduced for highly constrained firms, while they increase for less indebted firms.
Optimal Lending Contracts with Financial Intermediaries
I develop a model where endogenous inefficient ex-post liquidation and borrowing constraints arise as part of the optimal long-term contract. This is a dynamic model with three agents: entrepreneur, outside investor and bank. Borrowing constraints appear because the entrepreneur faces limited liability and some source of moral hazard. Banks have the ability to monitor firms and alleviate financial frictions. However, banks cannot commit to monitor and are also subject to an enforcement problem. Optimal long-term contracts imply a relation between the capital structure and firm dynamics. I analyze the composition of debt during the life cycle of the firm, and its relation with firm size, age and probability of survival. The model implies a non-monotonic relation between bank credit and firm's size. Small and young firms tend to rely on bank credit. The relation with the bank increases as the firm grows, but up to a point where the firm leaves the bank and rely only on direct credit. I also find that the presence of the bank increases survival probability, reduces borrowing constraints and increases firm's value for all levels of equity, even for firms that do not have banks credit in their capital structure.