The Effect of Minority History on Racial Disparities in the Mortgage Market: A Case Study of Durham and New Haven
By Jisoo Yoon
In the aftermath of the housing market crash, the concentration of subprime mortgage loans in minority neighborhoods is a current and long-standing issue. This study investigates the presence of racial disparities in mortgage markets by examining two cities with contrasting histories of African American and Hispanic establishment: Durham, North Carolina and New Haven, Connecticut. This study examines data by the Home Mortgage Disclosure Act (HMDA), and distills the effect of minority legacy on the perception of racial risk by using econometric instruments to separate the behavior of national lenders and local lenders. The econometric methods allow national lenders to reflect objective risk measures and neighborhood race dynamics, while local lenders reflect subjective attitudes towards certain races. With its longer history of African American presence, Durham shows a positive attitude towards Black borrowers at the local level, while New Haven shows a more favorable attitude towards its Hispanic residents. Nonetheless, racial legacy also materializes as a negative factor in the form of increased residential segregation and spillover effects. Furthermore, a temporal variation analysis of pre- and post-mortgage market reform data affirms the disappearance of racial bias and continued presence of spillover risk in Durham.
Advisor: Christopher Timmins | JEL Codes: C01, G21, J15, R21, R23, R31 | Tagged: Econometrics, Mortgages, Economics of Minorities, Races, Census, Migration, Population, Neighborhood Characteristics, Housing Supply and Market
By Andrew Bentley
Conventional measurements of equity return volatility rely on the asset’s previous day closing price to infer the current level of volatility and fail to incorporate information concerning intraday influntuctuations. Realized measures of volatility, such as the realized variance, are able to integrate intraday information by utilizing high-frequency data to form a very accurate measure of the asset’s return volatility. These measures can be used in parallel with the traditional definition of the Capital Asset Pricing Model (CAPM) beta to better predict the time-varying systematic risk of an asset. In this analysis, realized measures were added to the General Autoregressive Conditional Heteroskedastic (GARCH) framework to form a predictive model of beta that can quickly respond to rapid changes in the level of volatility. The ndings suggest that this predictive beta is better able to explain the stylized characteristics of beta and is a more accurate forecast of the realized beta than the GARCH model or the benchmark Autoregressive Moving-Average (ARMA) model used as a comparison.
JEL Codes: C0, C3, C03, C32, C53, C58 | Tagged:
By Jeffery Shih-kai Shen
This paper seeks to investigate the effects of vertical integration on the cable industry. There are two main goals that the research paper will attempt to address. The first is to build upon existing research on favoritism shown by multichannel video programming distributors (MVPDs) to affiliated video programming networks. Second, the paper will use 2007 and 2010 industry data to investigate the possible existence of “quid pro quo” among vertically integrated MVPD cable providers. After evaluating the data with multivariate OLS Regressions, the evidence suggests that MVPD cable providers do tend to carry their own affiliated programming networks. Furthermore, the evidence supports the hypothesis that reciprocity relationships exist among major vertically integrated cable providers.
JEL Codes: C01, D22, K21 | Tagged:
By Kunal Jain
Conventional models of volatility estimation do not capture the persistence in high-frequency market data and are not able to limit the impact of market micro-structure noise present at very finely sampled intervals. In an attempt to incorporate these two elements, we use the beta-metric as a proxy for equity-specific volatility and use finely sampled time-varying conditional forecasts estimated using the Heterogeneous Auto-regressive framework to form a predictive beta model. The findings suggest that this predictive beta is better able to capture persistence in financial data and limit the effect of micro-structure noise in high frequency data when compared to the existing benchmarks.
Advisor: George Tauchen | JEL Codes: C01, C13, C22, C29, C58 | Tagged: