Essays on Bank Performance, Strategic Behavior, and Community Development with a Focus on Minority Depository Institutions and the Political Economy of Forgiving Student Loans

I explore questions related to financial institutions, the accessibility of financial services, community development, and the political economy of forgiving student loans using the ideas from spatial economics, industrial organization, and public choice. Increased access to financial services improves the economic outcomes of individuals and firms in the community. Understanding sources leading to a lower accessibility of financial services is integral for an effective endeavor to lower barriers to financial services with far-reaching policy and economic implications. The economic literature provides evidence of the beneficial effects of increased access to financial services as well as the adverse effects of diminished access to credit and banking services. These effects are particularly pronounced for low-income individuals, minorities, and small businesses. The research also shows that, despite the technological advances, credit and depository service markets are local. Hence considering the geographic aspects of these services is essential. My research explores the role of geography in bank exit and entry, which directly affects the spatial accessibility of financial services within a community. Similarly, a rapid increase in student-loan debt has drawn much attention form public, scholars, and politicians. The sharp increase of defaults on student loans accompanied by growing student loan debt during the Great Recession led to proposals of forgiving student loans and making some public higher education tuition-free. To understand the political economy of such proposals, I explore the circumstances that motivate the implementation of the student-loan debt forgiveness policy in a two-period model of schooling and unemployment insurance with search costs

A Bayesian Approach to Risk Management in a World of High-Frequency Data

A Realised Volatility GARCH model using high-frequency data is developed within a Bayesian framework for the purpose of forecasting Value at Risk and Conditional Value at Risk. A Skewed Student-t return distribution is combined with a Student-t distribution in the measurement equation in a GARCH framework. Realised Volatility GARCH models show a marked improvement compared to ordinary GARCH. A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio tail risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using traditional GARCH-copula models, and GARCH on an aggregated portfolio. Copula models implementing a Realised Volatility GARCH framework show an improvement over traditional GARCH models. A Bayesian detection of regime changes utilizing high-frequency data is developed, once again for the purpose of forecasting portfolio tail risk. The use of high-frequency data improves the accuracy of regime change detection compared to daily data. Monte Carlo sampling schemes are employed for the estimation of these models

A Bayesian Approach to Risk Management in a World of High-Frequency Data

A Realised Volatility GARCH model using high-frequency data is developed within a Bayesian framework for the purpose of forecasting Value at Risk and Conditional Value at Risk. A Skewed Student-t return distribution is combined with a Student-t distribution in the measurement equation in a GARCH framework. Realised Volatility GARCH models show a marked improvement compared to ordinary GARCH. A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio tail risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using traditional GARCH-copula models, and GARCH on an aggregated portfolio. Copula models implementing a Realised Volatility GARCH framework show an improvement over traditional GARCH models. A Bayesian detection of regime changes utilizing high-frequency data is developed, once again for the purpose of forecasting portfolio tail risk. The use of high-frequency data improves the accuracy of regime change detection compared to daily data. Monte Carlo sampling schemes are employed for the estimation of these models

Integration, diversification, and spillover : an assessment of the emerging markets using American Depository Receipts (ADRs)

The focus of this thesis is on the emerging markets. It assesses intra- and inter-market mean and volatility spillover, investigates the impact of the Mexican currency crisis on international portfolio diversification, and employ international asset pricing to test the integration of the emerging markets