Essays in Macroeconomics and Asset Pricing

Sahay, Ashish (2023) Essays in Macroeconomics and Asset Pricing. Doctoral thesis, University of London: London Business School.

Abstract

This thesis focuses on studying questions at the intersection of asset pricing and macroeconomics, and consists of three research papers that span these two broad topics.

Chapter 1 studies how environmental regulations impact the macroeconomic aggregate dynamics. The research laboratory used are the US counties which are regulated by a central regulatory
body (EPA), based on a regulatory policy dependent on local pollution. Using wind patterns generated marginal pollution as instrument, I find that these regulations impact aggregate manufacturing sector production, unemployment, local GDP and local government's bond yields. The regulations impact smaller manufacturing firms disproportionately more than larger firms. I study these dynamics through the lens of a model of heterogeneous firms in a county economy governed by an exogenous (central) regulatory policy and find that the model can account for most of the empirical findings.

Chapter 2 examines how differences in fiscal policies of the state governments impact their municipal bonds implied credit spreads and credit risk premia. We find that the states which follow stringent fiscal policies have significantly lower credit spreads compared to states which follow lenient fiscal policies. We study these findings through a dynamic equilibrium model of municipal credit risk, where state governments choose the optimal debt and default policy for a
given fiscal rule. The model can explain the empirical stylized relationship between US states' fiscal policy and municipal bond returns.

Chapter 3 develops a simple approach to running inference tests on the out-of-sample performance of asset-pricing models via a split-sample-GMM approach that takes into account a) an
omitted SDF-implied restriction on the benchmark factors, and b) the true estimation uncertainty of the SDF, caused by the prior in-sample estimation of model parameters. In a large-scale simulation
study, we find that the estimation risk has a nontrivial impact on the out-of-sample tests.