How do network frictions in OTC derivatives markets affect equilibrium prices and risk-sharing? This paper provides quantitative answers, with a particular focus on the role of central dealers. We develop an equilibrium model of an OTC derivatives market. Detailed data provided to the Office of Financial Research (OFR) by the Depository Trust & Clearing Corporation (DTCC), along with the structural pricing and allocation equations from our model, allow us to estimate the model’s key parameters. Using our model at estimated parameters, along with the empirical core-periphery network structure, we provide quantitative estimates of: (1) The effect of network frictions on the level of OTC derivatives prices (2) The key determinants of cross sectional dispersion in bilateral prices; (3) The effects of the elimination of a central dealer on prices and risk reallocation.

We study how networks of informational flows are formed when agents acquire information through peers. We develop an endogenous network formation model, in which agents care not only about accuracy of their decision making but also about the actions other agents will take. We show that any strict equilibrium information structure is a hierarchical network. In addition, if the marginal cost of forming links is weakly increasing, the equilibrium network is core-periphery. Even if agents are ex-ante identical, the equilibrium information structure generates ex-post heterogeneity in payoffs and actions. In any equilibrium, agents are sorted into layers of influence.  Some individuals endogenously become opinion makers and have pervasive influence over the society, although they may not have superior information. Finally, we study how individual characteristics determine agents’ role in the network.

Firm volatilities co-move strongly over time, and their common factor is the dispersion of the economy-wide firm size distribution. In the cross section, smaller firms and firms with a more concentrated customer base display higher volatility. Network effects are essential to explaining the joint evolution of the empirical firm size and firm volatility distributions. We propose and estimate a simple network model of firm volatility in which shocks to customers influence their suppliers. Larger suppliers have more customers and the strength of a customer-supplier link depends on the size of the customer. The model produces distributions of firm volatility, size, and customer concentration that are consistent with the data.

Standard risk factors can be hedged with minimal reduction in average return. This is true for “macro” factors such as industrial production, unemployment, and credit spreads, as well as for “reduced form” asset pricing factors such as value, momentum, or profitability. Low beta versions of the factors perform close to as well as high beta versions, hence a long short portfolio can hedge factor exposure with little reduction in expected return. For the reduced form factors this mismatch between factor exposure and expected return generates large alphas. For the macroeconomic factors, hedging the factors also hedges business cycle risk by significantly lowering exposure to consumption, GDP, and NBER recessions. We study implications both for optimal portfolio formation and for understanding the economic mechanisms for generating equity risk premiums.

This paper examines the link between microeconomic uncertainty and the size premium across different frequencies in an investment model with heterogeneous firms. We document that the observed time-varying dispersion in firm-specific productivity can account for a large size premium in the 1960’s and 1970’s, the disappearance in the 1980’s and 1990’s, and reemergence in the 2000’s. Periods with a large (small) size premium coincide with high (low) microeconomic uncertainty. During episodes of high productivity dispersion, small firms increase their exposure to macroeconomic risks. Our model can also explain the strong positive low-frequency co-movement between size and value factors, but a negative relation with the market factor.