We evaluate approaches to estimating demand elasticities in dynamic asset markets, both theoretically and empirically. We establish strict, necessary conditions that the dynamics of instrumented asset price variation must satisfy for valid identification. We illustrate these insights in a general equilibrium model of dynamic trade and derive the magnitude of biases that arise when these conditions are violated. Estimates based on static IO models are severely biased when the instrumented price variation is persistent or predictable. Empirically, we show that commonly used instruments yield elasticity estimates that are off by orders of magnitude, or even have the wrong sign. In contrast to standard multiplier calculations, our theory characterizes the dynamic asset market interventions required to sustain a given price path support process, with direct implications for policies such as Quantitative Easing (QE).

We investigate market power in U.S. equity securities lending and assess its effects on distinct investor groups. Our data reveal high market concentration, non-competitive fees, and an excess supply of lendable stock inventory throughout the cross-section. Motivated by these findings, we develop a dynamic model that shows how intermediation and market power emerge as responses to short sellers' concerns over information leakage. Our quantitative analysis suggests that both short sellers and asset owners likely prefer the current intermediated market structure to a counterfactual centralized alternative—especially for smaller, less liquid stocks—since information leakages that would arise in a centralized venue undermine the very foundation of short sellers' business model. We estimate that, across stocks, non-competitive fee income increases lenders' valuations by between 1.4% and 90%.

Economies committed to environmental goals, such as mitigating global warming, face the challenge that pollution and emissions originate to a large extent from activities in foreign countries. While governments may attempt to tax domestic firms' sourcing of inputs from brown international firms, such approaches often have to rely on voluntary disclosures by foreign trade partners. Addressing this issue, we examine international supply chain partners' disclosure choices and their interplay with a domestic government's optimal regulatory policies. Such disclosures are key to SCOPE-3 emissions calculations and meaningful ESG ratings. Contrary to regulations implemented via carbon credit markets, optimal policies in our environment account for endogenous information asymmetries and the exercise of market power. Our analysis characterizes how domestic households' exposures to global externalities shape both a government's optimal regulations and the precision of foreign firms' voluntary disclosures.

I develop a tractable dynamic model of financially distressed firms' interactions with large equity investors. The framework quantitatively matches key empirical facts related to distressed equity issuances, including the provision of substantial discounts in private placements of public equity. The model captures the pivotal signaling role played by large distressed equity investors and facilitates counterfactual analyses that distill blockholders' externalities on other claimants. The analysis reveals that blockholders' impact on inefficient default is generically non-monotone. Whereas inefficiencies are exacerbated for intermediate levels of distress, they are alleviated in deep distress, when blocks are acquired in last-minute rescue interventions. The setting proposes a novel set of modeling choices that yield global solutions in environments with optimal default and learning.

We present a novel modeling approach for granular general equilibrium economies with persistent heterogeneity that yields exact global solutions. A key feature of our approach is the use of stochastic lumpy adjustment (SLA) technologies. The associated stochastic structure can capture any degree of granularity in adjustments of asset positions, and is thus more flexible than standard technologies. We show how SLA technologies can be employed in the context of both capital investment and the trading of financial assets. As our approach does not impose any restrictions on the shape of the state variable distribution, it can also be used to evaluate the conditions under which previous solution methods are likely to succeed. Obtaining exact solutions in these granular economies primarily involves inverting sparse matrices, a computational operation that can take full advantage of recent advances in high-performance parallel computing architectures.

We analyze the effectiveness of preventive investments aimed at increasing agents' life expectancy, with a focus on influenza and COVID-19 mitigation. Maximizing overall life expectancy requires allocating resources across hazards so as to equalize investments' marginal effectiveness. Based on estimates for the marginal effectiveness of influenza vaccines, we determine the level of COVID-19 mitigation investments that would imply such equalization. Given current projections for COVID-19 mitigation costs, our results suggest that wide-spread influenza vaccination would be an effective life-preserving investment.

This paper studies intertemporal information acquisition by agents that are rational Bayesian learners and that dynamically optimize over consumption, investment in capital, and investment in information. The model predicts that investors acquire more information in times when future capital productivity is expected to be high, the cost of capital is low, new technologies are expected to have a persistent impact on productivity, and the scalability of investments is expected to be high. My results shed light on the economic mechanisms behind various dynamic aspects of information production by the financial sector, such as the sources of variation in returns on information acquisition for investment banks or private equity funds.

Episodes of boom-bust cycles tend to occur in sectors with recent arrivals of new technologies and are often related to excessive funding by the financial sector. In this paper, I develop a dynamic general equilibrium model consistent with a role for the financial sector in propagation during such episodes. I extend a standard Schumpeterian growth model by incorporating (a) a monopolistically competitive financial sector and (b) time-varying technological conditions in real sectors. I identify two propagation channels. The first operates through financial firms' acquisition of sector-specific knowledge (skill channel); financial firms chase "hot sectors" and thereby amplify fluctuations. The second channel originates in an interaction between competition in the financial sector and patent races in product markets (competition channel). Financial firms' temporary competitive advantages in access to new ventures imply market segmentation: financial firms maximize the surplus generated by the client firms they can currently attract, anticipating competing financial firms' future screening and funding decisions. Relative to the Pareto optimum, the competition channel generates overinvestment in sectors with temporarily improved technological conditions; excessively high growth in these sectors comes at the cost of lower growth in the economy as a whole. The model links financial propagation to time variation in the cross section of asset prices. Exposures to aggregate risk dampen amplification effects.