Publications

Trading Costs and Informational Efficiency with Eduardo Dávila, Journal of Finance, forthcoming
We study the effect of trading costs on information aggregation and acquisition in financial markets. For a given precision of investors' private information, an irrelevance result emerges when investors are ex-ante identical: price informativeness is independent of the level of trading costs. When investors are ex-ante heterogeneous, a change in trading costs can increase or decrease price informativeness, depending on the source of heterogeneity. Our results are valid under quadratic, linear, and fixed costs. Through a reduction in information acquisition, trading costs reduce price informativeness. We discuss how our results inform the policy debate on financial transaction taxes/Tobin taxes.
Collateralizing Liquidity**,  Journal of Financial Economics 131 (2019), pp. 299-322
I develop a dynamic model of optimal funding to understand why financial assets are used as collateral instead of being sold to raise funds. Firms need funds to invest in risky projects with non-observable returns. Since holding these assets allows firms to raise these funds, investing firms value the asset more than non-investing ones. When assets are less than perfectly liquid and investment opportunities are persistent, collateralized debt minimizes asset transfers from investing to non-investing firms and, thus, is optimal. Frictions in asset markets lead to an illiquidity discount and a collateral premium, which increase with the asset's illiquidity.​

**Previously titled " Equilibrium Collateral Constraints".
Fragility in Money Market Funds: Sponsor Support and Regulation*,  Journal of Financial Economics 121 (2016), pp. 595-623
Money Market Funds (MMFs), which are crucial to short-term funding markets, rely on voluntary support of fund sponsors to maintain stable share values. I develop a general equilibrium model of MMFs to study how sponsor support affects the industry’s fragility and regulation. Adverse asset-quality shocks lead MMFs to liquidate assets. When liquidity in asset markets is limited, asset prices are lower if more funds liquidate. Lower asset prices, in turn, make sponsor support costlier and even more liquidations occur. This feedback leads to complementarities in sponsors’ support decisions. Based on the model’s insights, I derive implications for the regulation of MMFs.                                                                                                                                                                
                                                                                                                                                                                                                                     [Presentation UWFC]
* Previously titled "The Regulation of Money Market Funds: Adding Discipline to the Policy Debate".  
Modernization of Tax Administrations and Optimal Fiscal Policies ​with Martin Besfamille, Journal of Public Economic Theory 11 (6), 2009, 897 - 926, December 
Since Sandmo (1981), many articles have analyzed optimal fiscal policies in economies with tax evasion. All share a feature: they assume that the cost of enforcing the tax law is exogenous. However, governments often invest resources to reduce these enforcement costs. In a very simple model, we incorporate such investments in the analysis of an optimal fiscal policy. We characterize their optimal level and we show numerically how they interact with the other dimensions of the optimal fiscal policy. Finally, we highlight the differences between our results and those obtained in a model without investment in the tax administration. 

Working papers

dentifying Price Informativeness with Eduardo Dávila                                                                                                                                         October  2020
This paper shows how to identify and estimate price informativeness. Starting from i) an asset pricing equation and ii) a stochastic process for asset payoffs, we show how to use regressions of changes in asset prices on changes in asset payoffs to recover an exact measure of price informativeness in a large class of environments. We implement our methodology empirically computing a panel of stock-specific measures of price informativeness for U.S. stocks between 1980 and 2017. In the cross section, we find that large stocks, stocks that with high turnover, and stocks with high institutional ownership have higher price informativeness. In the time series, we find that the median, mean, and standard deviation of the distribution of price informativeness have steadily increased since the mid 1980's.
Replication code and informativeness data  
A Model of Infrastructure Financing with Viral Acharya and Suresh Sundaresan                                                                                           September 2020
Infrastructure projects involve multiple parties: government, private sector firms that build and manage, and outside investors who supply financing. Private sector firms need incentives to implement and maintain the projects well; governments may lack commitment not to extort cash flows (for instance, by limiting user fees) from projects once implemented. This double moral hazard problem limits the willingness of outside investors to fund infrastructure projects. To ameliorate these two moral hazards, we show that the optimal design of infrastructure financing features (I) government guarantees to investors against project failure; (II) bundling of development rights for the private sector and investors; (III) tax subsidies to the private parties out of infrastructure externalities; and, (IV) “general obligation” financing in the form of cross-guarantees between high-quality projects and “revenue only” financing without cross-guarantees for low-quality projects. These features are found to be relevant in the practice of infrastructure financing.
Designing Stress Scenarios with Thomas Philippon                                                                                                                                                         August 2020
We develop a tractable framework to study the optimal design stress scenarios. A risk-averse principal (e.g, a manager, a regulator) seeks to learn about the exposures of a group of agents (e.g., traders, banks) to a set of risk factors. The principal asks the agents to report their outcomes (e.g., credit losses) under a variety of scenarios that she designs. She can then take remedial actions (e.g., mandate reductions in risk exposures). The principal's program has of two parts. For a given set of scenarios, we show how to apply a Kalman filter to solve the learning problem. The optimal design is then a function of what she wants to learn and how she intends to intervene if she uncovers excessive exposures. The choice of optimal scenarios depends on the principal's prior's about risk exposures, the cost of ex-post interventions, and the potential correlation of exposures across agents.
​Volatility and Informativeness with Eduardo Dávila                                                                                                                                                      January 2020
We explore the relation between price volatility and price informativeness in financial markets. We identify two channels (noise reduction and equilibrium learning) through which changes in informativeness are associated with changes in volatility. When informativeness is sufficiently high (low), volatility and informativeness positively (negatively) comove in equilibrium for any change in primitives. We provide conditions on primitives that guarantee that volatility and informativeness comove positively or negatively for any change in parameters. We recover stock-specific primitives for US stocks, and find that most stocks lie in the region of the parameter space in which informativeness and volatility comove negatively.
Strategic Fragmented Markets with Ana Babus                                                                                                                                                            October 2019
We study the determinants of asset market fragmentation in a model with strategic investors that disagree about the value of an asset. Investors choices determine the market structure. Fragmented markets are supported in equilibrium when disagreement between investors is low. In this case, investors take the same side of the market and are willing to trade in smaller markets with a higher price impact to face less competition when trading against a dealer. The maximum degree of market fragmentation increases as investors' priors are more correlated. Dealers can benefit from fragmentation, but investors are always better off in centralized markets.
​Transparency and Bank Runs                                                                                                                                                                                                   April 2015
In a banking model with imperfect information, I find that more precise information increases the economy's vulnerability to bank runs. For low transparency levels, depositors cannot distinguish bad from good states based on their private signals and, absent liquidity shocks, have no incentives to withdraw early. As transparency increases, and private signals become more informative, depositors' incentives to withdraw strengthen and run-proof contracts become costlier in risk-sharing terms: the bank must offer less to early withdrawers to prevent runs. When transparency is high enough, the bank would rather forgo return and hold excess liquidity than choose a run-proof deposit contract.

Work in progress

The Value of Arbitrage with Eduardo Dávila