Mahyar Kargar

Customers in over-the-counter (OTC) markets must find a counterparty to trade. Little is known about this process, however, because existing data consist of transaction records, which only reveal the outcome of a search. Using data from a trading platform for corporate bonds, we unpack the search process. We analyze how long it takes customers to trade and how dealers' offers evolve across repeated inquiries. We estimate that it takes 2-3 days to complete a transaction after an unsuccessful attempt, with substantial variation across trade and customer characteristics. Our analysis offers insights into the sources of trading delays in OTC markets.

Classical asset pricing models predict that optimizing investors exhibit extremely high demand elasticities, while empirical estimates are significantly lower—by three orders of magnitude. To reconcile this disparity, we introduce a novel decomposition of investor demand elasticity into two key components: "price pass-through," which captures how price movements forecast returns, and "unspanned returns," reflecting a stock's lack of perfect substitutes. In a factor model framework, we show that unspanned returns become significant when models include "weak factors." Classical models overestimate demand elasticity by assuming both very low unspanned returns and high price pass-throughs, assumptions that are inconsistent with empirical evidence. Once empirically estimated return processes are accounted for, even optimizing investors exhibit relatively inelastic demand.

We examine effects of state pension windfalls on property prices near state borders, where theory suggests real estate reflects the value of additional public resources. Windfalls have grown to represent more than one-third of state revenues and provide plausibly well-identified variation in fiscal conditions. We find that one dollar of exogenous variation in pension asset returns increases border house prices by approximately two dollars, suggesting governments allocate additional funds towards high value projects or tax abatement rather than wasting incremental resources. Evidence of larger effects in financially constrained municipalities highlights how fiscal resources amplify welfare effects of economic shocks.

Fluctuations in investor demand significantly affect firms' valuation and access to capital. To quantify their real effects, we develop a dynamic investment model, incorporating both the demand and supply sides of capital. Strong investor demand relaxes financial constraints and facilitates equity issuance and investment, while weak demand encourages opportunistic share repurchases, crowding out investment. We estimate the model using indirect inference, matching the endogenous relationship between investor demand and firm policies. Our estimation reveals that demand fluctuations are important drivers of firm-level investment and economy-wide capital misallocation, accounting for 26.9% of dispersion in MPK and 23.4% of productivity losses.

We develop a search-theoretic model of a dealer-intermediated over-the-counter market. The key departure from the literature is the assumption that when a customer meets a dealer, the dealer can sell only assets that it already owns. Hence, in equilibrium, dealers choose to hold inventory. We derive the equilibrium relationship between dealers' costs of holding assets on their balance sheets, their optimal inventory holdings, and various measures of liquidity, including bid-ask spreads, trade size, volume, and turnover. Using transaction-level data from the corporate bond market, we calibrate the model to quantitatively assess the impact of post-crisis regulations on dealers' inventory costs, liquidity, and welfare.

We investigate how much students benefit from student loan subsidies by exploiting a natural experiment: a demand shock due to the 2011 tightening of credit standards in the PLUS program. We establish that the Bennett hypothesis is best explained by colleges charging large markups over their marginal costs, rather than by advantageous selection. We estimate that students plausibly capture less than 60 cents of each dollar of resources expended on loan subsidies.

We study liquidity conditions in the corporate bond market during the COVID-19 pandemic and the effects of unprecedented Federal Reserve interventions. At the height of the crisis, weights in mid-March 2020, liquidity conditions deteriorated substantially, as dealers appeared unwilling to absorb corporate debt onto their balance sheets, with the cost of risky-principal trades increasing by a factor of five, forcing traders to shift to slower, agency trades. The announcements of the Federal Reserve's interventions coincided with substantial improvements in trading conditions, with dealers beginning to "lean against the wind" and bid-ask spreads declining. To study the causal impact of the interventions on market liquidity, we exploit eligibility requirements for bonds to be purchased through the Fed's corporate credit facilities.

The composition of the financial sector has important asset pricing implications beyond the health of the aggregate financial sector. To assess the impact of massive balance sheet adjustments within the intermediary sector during the Great Recession and resolve conflicting asset pricing evidence, I propose a dynamic asset pricing model with heterogeneous intermediaries facing financial frictions. Asset flows between intermediaries are quantitatively important for both the level of and variation in the risk premium. An empirical measure of the composition of the intermediary sector negatively forecasts future excess returns and is priced in the cross-section with a positive price of risk.

We develop an asset-pricing model with heterogeneous investors and search frictions. The model nests standard asset pricing and competitive search models as special cases. Trade is intermediated by risk-neutral dealers subject to capacity constraints. Risk-averse investors can direct their search towards dealers based on price and execution speed. Order flows affect the risk premium, volatility, and equilibrium interest rate. Large negative shocks lead to portfolio reallocations and increased trading volume, bid-ask spreads, and trading delays. Simultaneously, the model generates increased risk premium and volatility and a reduction in interest rates, consistent with asset-pricing and trading behavior during the COVID-19 crisis.

We develop a model of a dealer-intermediated over-the-counter market designed to study three major changes in the structure of the U.S. corporate bond market: the increase in dealers' balance sheet costs, the emergence of electronic trading platforms, and the growing presence of bond mutual funds and ETFs. Our model provides a unified analysis of these changes, clarifies the economic channels at play, and allows us to quantify their effects on a variety of market outcomes. Our quantitative analysis suggests that, while electronic trading significantly reduced the cost of raising capital in the corporate bond market, these gains were almost completely offset by the combined effects of balance sheet costs and of changes in the demand for liquidity. We find that electronic trading also caused a meaningful decline in the bid-ask spread, whereas other changes in the market structure had little effect on transaction costs.

We study aggregate stock market elasticity in a general equilibrium model with heterogeneous investors, passive demand, and financial constraints. Without frictions, the elasticity of the endowment claim is infinite, as interest rate and risk premium responses offset each other. With frictions, price impact for the endowment claim remains modest (about 0.7 in our calibration). In contrast, the equity (dividend) claim exhibits large price impact (above 8), consistent with empirical evidence, as frictions dampen interest rate responses while leverage amplifies risk premia responses to portfolio flows. We introduce a state-global perturbation method that yields closed-form, state-dependent elasticities. Calibrated to match the magnitude of portfolio flows, the model simultaneously matches the equity premium, return volatility, and the level and countercyclical dynamics of price impact.

We study how to recover and use demand elasticities in dynamic asset markets. In demand system asset pricing, any price movement is accompanied by changes in future expected prices, returns, and risk, so there is no single context-free elasticity. The instrumented elasticity, which captures the reduced-form response to a shock that moves prices together with the expected path of future returns, is shock-specific and depends on the attributes of the identifying variation. The structural elasticity, which holds all future objects fixed, is transportable across shock environments. We show that the inverse of the instrumented elasticity is the price multiplier under a weak exclusion restriction and characterize how multipliers vary with shock persistence, volatility, systematic exposure, and surprise content. We then develop a recovery theory that uses the impulse responses of flows and prices to extract the transportable structural demand coefficients from shock-specific reduced-form estimates. In weekly data, recovered structural elasticities of 4.3 to 6.2 far exceed the naive inverse-multiplier benchmark of 0.3 to 0.4.