In this paper, I examine asset pricing in a multisector model with sectors connected through an input‐output network. Changes in the network are sources of systematic risk reflected in equilibrium asset prices. Two characteristics of the network matter for asset prices: network concentration and network sparsity. These two production‐based asset pricing factors are determined by the structure of the network and are computed from input‐output data. Consistent with the model predictions, I find return spreads of 4.6% and −3.2% per year on sparsity and concentration beta‐sorted portfolios, respectively.

We develop an endogenous network formation model, in which agents form connections to acquire information. Our model features complementarity in actions as agents care not only about accuracy of their decision-making but also about the actions of other agents. In equilibrium, the information structure is a hierarchical network, and, under weakly convex cost of forming links, the equilibrium network is core-periphery. Although agents are ex ante identical, there is ex post heterogeneity in payoffs and actions.

Size and value premia comove strongly with one another at low frequencies, but they are both negatively related to long-run movements in the equity premium. We explain these patterns in an investment-based asset pricing model featuring persistent micro and macro uncertainty. Micro uncertainty generates size and value premia waves, while macroeconomic uncertainty produces equity premium waves. The negative correlation between micro and macro uncertainty at low frequencies explains why the equity premium is a long-term hedge for size and value premia. Persistent micro uncertainty is also a source of instability for size and value factors in short samples.

We show that firms׳ idiosyncratic volatility obeys a strong factor structure and that shocks to the common idiosyncratic volatility (CIV) factor are priced. Stocks in the lowest CIV-beta quintile earn average returns 5.4% per year higher than those in the highest quintile. The CIV factor helps to explain a number of asset pricing anomalies. We provide new evidence linking the CIV factor to income risk faced by households. Our findings are consistent with an incomplete markets heterogeneous agent model. In the model, CIV is a priced state variable because an increase in idiosyncratic firm volatility raises the average household׳s marginal utility. The calibrated model matches the high degree of co-movement in idiosyncratic volatilities, the CIV-beta return spread, and several other asset price moments.

Firm volatilities comove 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 customer-supplier links depend on customers’ size. The model produces distributions of firm volatility, size, and customer concentration consistent with the data.

We study the effect of dealer exit on prices and quantities in a model of an over-the-counter market featuring a core-periphery network with bilateral trading costs. The model is calibrated using regulatory data on the entire U.S. credit default swap (CDS) market between 2010 and 2013. Prices depend crucially on the risk-bearing capacity of core dealers, yet unlike standard models featuring a dealer sector, we allow for heterogeneity in dealer risk-bearing capacity. This heterogeneity is quantitatively important. Depending on how well dealers share risk, the exit of a single dealer can cause credit spreads to rise by 8 % to 24%.