Miroslav Gabrovski

An increasing share of corporate loans, a critical source of firm credit, are sold off of banks' balance sheets and actively traded in a secondary over-the-counter market. We develop a microfounded equilibrium search-theoretic model with labor, credit, and financial markets to study the impact of this secondary loan market on the real economy. Our analysis highlights a policy-relevant trade-off: the market reduces the steady-state level of unemployment by 0.21pp, but it also amplifies unemployment's response to a 1% productivity drop by 0.07pp. Trading delays in the secondary market matter significantly: if trade were instantaneous, steady-state unemployment and its volatility would decline by 1.49pp and by 0.24pp, respectively.

A large literature in macroeconomics concludes that disruptions in financial markets have large negative effects on output and (un)employment. Though diverse, papers in this literature share a common characteristic: they all employ frameworks where money is not explicitly modeled. This paper argues that the omission of money may hinder a model's ability to evaluate the real effects of financial shocks, since it deprives agents of a payment instrument that they could have used to cope with the resulting liquidity disruption. In a carefully calibrated New-Monetarist model with frictional labor, product, and financial markets, we show that the existence of money dampens or even nearly eliminates the real impact of financial shocks, depending on the nature of the shock. We also show that the propagation of financial shocks to the real economy depends on the inflation level: high inflation regimes magnify the real effects of adverse financial shocks.

This paper studies the effects of financial frictions in construction on housing market dynamics. To this end, we build a search-theoretic model of the housing market in which there is endogenous entry of buyers and developers face credit constraints. We capture credit frictions by assuming that developers must search for financing before building a home à la Wasmer and Weil (2004). Our model explores a novel channel that links credit frictions faced by developers to the housing market. We calibrate the model to quantify the size of the credit channel during the 2012–2019 housing market recovery. Through a series of counterfactuals, our model predicts that the credit channel had a large impact on housing liquidity, construction, and the vacancy rate. Furthermore, it accounts for around half of the rise in prices during the 2012-2019 housing market recovery.

A salient feature of over-the-counter (OTC) markets is intermediation: dealers buy from and sell to customers as well as other dealers. Traditionally, the search-theoretic literature of OTC markets has rationalized this as a consequence of random meetings and ex post bargaining between investors. We show that neither of these are necessary conditions for intermediation. We build a model of a fully decentralized OTC market in which search is directed and sellers post prices ex ante. Intermediation arises naturally as an equilibrium outcome for a broad class of matching functions commonly used in the literature. We further explore, both analytically and numerically, how the extent of intermediation depends on the nature of frictions and model primitives. Our numerical exercises also contrast the model's equilibrium implications to those of a benchmark model with random meetings and ex post bargaining.

This paper develops a business cycle model of the housing market with search frictions and entry of both buyers and sellers. The housing market exhibits a well-established cyclical component, which features three stylized facts: prices move in the same direction as sales and the number of houses for sale, but opposite to the time it takes to sell a house. These stylized facts imply that in the data housing vacancies and the number of buyers are positively correlated, i.e. that the Beveridge Curve is upward sloping. A baseline search and matching model of the housing market is unable to match these stylized facts because it inherently generates a downward sloping Beveridge Curve. With free entry of both buyers and sellers, our model reproduces the positive correlation between prices, sales and vacancies, and matches the stylized facts qualitatively and quantitatively.