UC San Diego

This dissertation is comprised of three free-standing chapters, each focused on topics in the intersection of financial economics and macroeconomics.

Chapter 1 examines the propagation of shocks in economic models with customer-supplier networks. I show that the common assumption of idiosyncratic shocks at the firm or industry levels implies empirically implausible sparsity restrictions on the input-output network structure. Moreover, I provide evidence that substitutability between trade partners is related to technological and product dispersion that is not captured by standard firm and industry definitions, and thus generates non-negligible correlation in shocks. Finally, I show that assets positively exposed to upstream and downstream shocks are useful hedges and earn lower average risk premia than less exposed peers. This is confirmed by statistically significant return spreads and a negative association between correlated shock propagation and aggregate growth.

Chapter 2 studies the role of time-to-build in federal defense when estimating aggregate federal government spending multipliers. We find that the early impact of defense news shocks on GDP is due to a rise in business inventories, as contractors ramp up production for new defense contracts. These contracts do not affect government spending (G) until payment-on-delivery, which occurs 2-3 quarters later. Novel data on defense procurement obligations reveals that contract awards Granger-cause shocks to G identified via Cholesky decomposition, but not defense news shocks. We show that Cholesky shocks to G miss early changes in inventories, and thus result in lower multiplier estimates relative to the narrative method.

Chapter 3 explores the permanent-transitory decomposition of stochastic discount factor (SDF) processes in dynamic asset pricing models, in which the permanent component captures pricing at long payoff horizons. Analytic solutions for the permanent component are limited, and standard numerical methods are not well-suited to solve for them due to the curse of dimensionality and lack of boundary conditions and/or parametric assumptions. We propose a novel algorithm for computing the permanent-transitory decomposition for a general class of asset pricing models without such restrictions. We validate the algorithm's accuracy in several workhorse structural asset-pricing models, and argue that our approach applies to models whose state dynamics follow general and potentially high dimensional Levy processes.