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 propose a novel conceptual approach to characterizing the credit market equilibrium in economies with multi-dimensional borrower heterogeneity. Our method is centered around a micro-founded representation of borrowers' aggregate demand correspondence for bank capital. The framework yields closed-form expressions for the composition and pricing of credit, including a sufficient statistic for the provision of bank loans. Our analysis sheds light on the roots of compositional shifts in credit toward risky borrowers prior to the most recent crises in the U.S. and Europe, as well as the macroprudential effects of bank regulations, policy interventions, and financial innovations providing alternatives to banks.
I examine large shareholders' externalities on other claim holders when firms are financially distressed. To this end, I develop a tractable dynamic model of the interplay between blockholders and regular equity holders. Blockholders' information acquisition and investment decisions play a pivotal role in distressed firms' access to finance, affecting both total firm value and its distribution across claims. The impact on distress costs is non-monotone --- whereas blockholders' information exacerbates debt overhang for intermediate levels of distress, it increases firms' survival chances in deep distress. As a result, frictions delaying block acquisitions to "last minute" rescue interventions tend to be efficiency-enhancing.
We study security issuers' decision whether to pool assets when facing counterparties endowed with market power, as is common in over-the-counter markets. Unlike in competitive markets, pooling assets may be suboptimal in the presence of market power --- both privately and socially --- in particular, when the potential gains from trade are large. In these cases, pooling assets reduces the elasticity of trade volume in the relevant part of the payoff distribution, exacerbating inefficient rationing associated with the exercise of market power. Our results shed light on recently observed time-variation in the prevalence of pooling in financial markets.
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.