Simultaneous Occurrence of Price Jumps and Changes in Diffusive Price Volatility
By Shunting Wei
This paper uses high frequency financial data to study the changes in diffusive stock price volatility when price jumps are likely to have occurred. In particular, we study this effect on two levels. Firstly, we compare diffusive volatility on jump and non-jump days. Secondly, we study the change in diffusive volatility in local windows before and after 5-minute intervals on which price jumps are likely to have occurred. We find evidence that market price jumps occur simultaneously with a change in diffusive volatility with negative dependence in the direction of the jump and the volatility change. However, a similar relationship is not detectable in individual stock price data.
Advisor: George Tauchen | JEL Codes: C22, G1, G19 | Tagged: Diffusive Volatility, Jump Tests, Realized Volatility, Stock Price Jumps
Possibility of Cost Offset in Expanding Health Insurance Coverage: Using Medical Expenditure Panel Survey 2008
By Catherine Moon
The Patient Protection and Affordable Care Act aims to substantially reduce the number of the
uninsured over time and asserts that the financial burden of extending insurance coverage to the
previously uninsured will be offset by the benefit of the attendant improvement in their health.
Motivated by this policy, I explore whether health-insurance status and type affect one’s likelihood of
improving or maintaining health using the Medical Expenditure Panel Survey data. I build a set of
ordered regression models for health-status transitions under the first-order Markov assumption and
estimate it using maximum likelihood estimation. I perform a series of likelihood ratio tests for pooling to determine whether the latent propensity index is the same between adjacent initial health-status groups. Empirical results imply that expanding health care to the unwillingly uninsured due to severe
economic constraints and extending the scope of public insurance to that of private insurance will lead to improvement or maintenance of health for the relatively healthy population, implying the possibility of cost off-set in the expansion of coverage and the extension of scope.
Advisor: Frank Sloan, Michelle Connolly | JEL Codes: C12, C25, I12, I13, I18 | Tagged: Health Insurance, Health Transition, Ordered Regression Model, Patient Protection and Affordable Care Act (PPACA), Self-Assessed Health Status, Test for Pooling Adjacent Ordinal Categories
Neighborhood Effects and School Performance: The Impact of Public Housing Demolitions on Children in North Carolina
By Rebecca Aqostino
This study explores how the demolitions of particularly distressed public housing units, through the Home Ownership for People Everywhere (HOPE VI) grants program, have affected academic outcomes for children in adjacent neighborhoods in Durham and Wilmington, North Carolina. I measure neighborhood-level changes and individual effects through regression analysis. All students in demolition communities are compared to those in control communities: census blocks in the same cities with public housing units that were not demolished. Those in the Durham experiment community experienced statistically significant gains when compared to those in the control communities; the effect is insignificant in Wilmington.
Advisor: Charles Becker, Helen Ladd, Marjorie McElroy | JEL Codes: C23, H41, H52, H75, I24, I25 | Tagged: Achievement, Demolitions, Distressed Housing, HOPE VI, Neighborhood Effects, Public Housing, School Performance
Time-Varying Beta: The Heterogeneous Autoregressive Beta Model
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: Beta, Financial Markets, Heterogeneous Autoregressive, Persistence
