Bayesian Analysis of Banking Policy and Regulation
- Author(s): Sharma, Padma Ranjini
- Advisor(s): Jeliazkov, Ivan
- Richardson, Gary
- et al.
This dissertation contains essays that address questions related to banking policy and regulation by developing original Bayesian econometric methods that address unobserved heterogeneity.
In the first chapter, I study the resolution of failed financial institutions by regulators in the U.S. over the period 1984-1992, which was characterized by concurrent crises in the banking and Savings and Loans (S & L) industries. Economic theory indicates that regulators best serve the public interest when they act to discourage moral hazard and preserve channels of financial intermediation. I determine whether regulators in the two industries conformed to norms of optimal resolution by developing a Bayesian latent class algorithm to uncover their decision rules. The results show that whereas the banking regulator's actions were consistent with optimal resolution norms recommended by theory, the S & L regulator deviated from such norms.
The second chapter examines the moral hazard effects of lax regulatory and resolution standards on thrift institutions during the Savings and Loans crisis. I make use of the natural policy experiment arising from the closure of the Federal Savings and Loans Insurance Corporation (FSLIC) and the Federal Home Loan Bank Board (FHLBB) in 1989 and the ensuing regime of stringent regulation of thrift institutions. The paper develops a Bayesian method for causal inference by incorporating the difference-in-difference strategy into the potential outcome framework. I find that thrifts facing a high probability of failure increase their composition of safe assets and reduce the share of high-risk loans on their balance sheet relative to thrifts at a low probability of failure following the policy change. These results provide evidence of moral hazard in the thrift industry prior to the introduction of enhanced regulation and oversight in 1989.
The third chapter examines the nature of heterogeneity in consumer expectations by utilizing random coefficient models, which are proven to be effective tools to address unobserved heterogeneity in panel data settings. The estimation method utilized in this study extends the method developed by Chen and Dunson (2003) for the selection of random effects in linear models to a model with ordered categorical outcomes. The application of this algorithm on responses pertaining to credit access and financial position from the Survey of Consumer Expectations provides evidence in favor of a random intercept. However, the findings do not support the presence of other random coefficients. This shows that unobserved heterogeneity exists in the expectation-formation mechanism but does not manifest in the responses to changes in demographic and economic characteristics of households.