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Evaluating Asset Bubbles within Cryptocurrencies using the LPPL Model

By Rafal Rokosz

The advent of blockchain technology has created a new asset class named cryptocurrencies that have experienced tremendous price appreciation leading to speculation that the asset class is experiencing an asset bubble. This paper examines the novelty and functionality of cryptocurrencies and potential factors that may lead to conclude the existence of an asset bubble. To empirically evaluate whether the asset class is experiencing an asset bubble the LPPL model is used. The LPPL model was able to successfully identify two of the four crashes within the data set signifying that cryptocurrencies are within an asset bubble.

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Advisors: Ed Tiryakian and Grace Kim | JEL Codes: G12, Z00, C60

An Analysis of Passive and Active Bond Mutual Fund Performance

By Michael J. Kiffel

The literature on the performance differential between passively and actively managed equity mutual funds is thorough: passively managed funds generally outperform their active counterparts except in the rare presence of highly-skilled managers. However, there exists limited academic research regarding fixed income mutual funds. This study utilizes the Fama-French bond risk factors, TERM and DEF, in a dual-step multivariate linear regression analysis to determine this performance differential between passively and actively managed bond mutual funds. The funds are comprised of either corporate or government bonds, spanning three categorizations of average maturities. Overall, it is determined that passively managed bond funds offer higher net returns than those offered by actively managed funds. Additionally, the regressions demonstrated that DEF possesses a high degree of predictive power and statistical

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Advisor: Edward Tower | JEL Codes: C55, G10, G11

What Fosters Innovation? A CrossSectional Panel Approach to Assessing the Impact of Cross Border Investment and Globalization on Patenting Across Global Economies

By Michael Dessau and Nicholas Vega

This study considers the impact of foreign direct investment (FDI) on innovation in high income, uppermiddle  income and lowermiddle income countries. Innovation matters because it is a critical factor for economic growth. In a panel setting, this study assesses the degree to which FDI functions as a vehicle for innovation as proxied by scaled local resident patent applications. This study considers research and development (R&D), domestic savings, imports and exports, and quality of governance as factors which could also impact the effectiveness of FDI on innovation. Our results suggest FDI is most effective as inward direct investment in countries outside the technological frontier possessing adequate existing domestic investment capital and R&D spending to convert foreign investment capital and technological spillover into innovation. Nonetheless, FDI was not a consistent indicator for innovation; rather, the most consistent indicators across this study were R&D and domestic savings. Differences amongst income groups are highlighted as well as their varying responses to our array of causal factors.

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Advisor: Lori Leachman | JEL Codes: A10, B22, C82, E00, E02, O10, O11, O30, O31, O32, O33, O34, O43

The Effect of Federal Regulations on the Outcomes of Auctions for Oil and Gas Leaseholds

By Artur Shikhaleev

This thesis attempts to analyze the impact of the differences in regulatory frameworks that govern state-owned and federally-owned lands on the outcomes of auctions for oil and natural gas leaseholds in the state of New Mexico. The analysis tries to isolate the effect of ownership by controlling for auction structure, leasehold characteristics, and prices of underlying resources. Given past research, the hypothesis is that stricter regulations carry a heavier cost to buyers, so the expectation is that federally-owned leaseholds, which are more regulated, are traded at a discount to state-owned leaseholds. However, the result of this thesis is contradictory to the hypothesis. The conclusion is that stricter regulations do not lead to a discounted auction price for an oil and gas leasehold.

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Advisor: James Roberts, Kent Kimbrough | JEL Codes: C12, C21, Q35, Q58 | Tagged: Auction, Education, environment, federal, natural gas, Oil, Regulation, State

BIDDING FOR PARKING: The Impact of University Affiliation on Predicting Bid Values in Dutch Auctions of On-Campus Parking Permits

By Grant Kelly

Parking is often underpriced and expanding its capacity is expensive; universities need a better way of reducing congestion outside of building costly parking garages. Demand based pricing mechanisms, such as auctions, offer a possible solution to the problem by promising to reduce parking at peak times. However, faculty, students, and staff at universities have systematically different parking needs, leading to different parking valuations. In this study, I determine the impact university affiliation has on predicting bid values cast in three Dutch Auctions of on-campus parking permits sold at Chapman University in Fall 2010. Using clustering techniques crosschecked with university demographic information to detect affiliation groups, I ran a log-linear regression, finding that university affiliation had a larger effect on bid amount than on lot location and fraction of auction duration. Generally, faculty were predicted to have higher bids whereas students were predicted to have lower bids.

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Advisor: Alison Hagy, Allan Collard-Wexler, Kent Kimbrough | JEL Codes: C38, C57, D44, R4, R49 | Tagged: Auctions, Parking, University Parking, Bidder Affiliation, Dutch Auction, Clustering

Determining NBA Free Agent Salary from Player Performance

By Joshua Rosen

NBA teams have the opportunity each offseason to sign free agents to alter their rosters. Using only regular season per game statistics, I examine the best method of calculating a player’s appropriate salary value based upon his contribution to a team’s regular season win percentage. I first determine which statistics most accurately predict team regular season win percentage, and then use regression analysis to predict the values of these metrics for individual players. Finally, relying upon predicted statistics, I assign salary values to free agents for their upcoming season on specific teams. My results advise teams to rely heavily on Player Impact Estimate (“PIE”) when predicting their teams’ win percentage, and to seek players whose appropriate salaries would be significantly more than their actual seasonlong salaries if the free agents were to sign.

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Advisor: Kent Kimbrough, Peter Arcidiacon | JEL Codes: C30, Z2, Z22 | Tagged: Free Agents, Salaries, NBA

Variations in Turkey’s Female Labor Market: The Puzzling Role of Education

By Rachael Anderson

Although Turkey ranks among the world’s 20 largest economies, female labor force participation in Turkey is surprisingly low.  Relative to other developed countries, however, the proportion of Turkish women in senior management is high.  One explanation for these contrasting pictures of Turkey’s female labor force is education.  To better understand how women’s education and household characteristics explain variations in Turkey’s female labor market, I use annual Turkish Household Labour Force Survey data from 20042012 to estimate five probabilities: the likelihood that a woman (1) participates in the labor force, or is employed in an (2) agricultural, (3) blue collar, (4) lower white collar, or (5) upper white collar job.  I find that labor force participation is relatively high among female primary school graduates, who are most likely to work in agricultural and blue collar jobs.  Highly educated married women are the most likely group to participate in upper white collar jobs, and families favor sending single daughters over wives to work during periods of reduced household income.

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Advisor: Kent Kimbrough, Timur Kuran | JEL Codes: C51, J21, J23 | Tagged: Employment, Labor-force Participation, Occupation Women

Understanding Financial Incentive Health Initiatives: The Impact of the Janani Suraksha Yojana Conditional Cash Transfer Program on Institutional Delivery Rates and Out-of- Pocket Health Expenditure

By Ritika Jain

Demand-side financing is a policy tool used by nations to incentivize utilization of public institutions, and India’s Janani Suraksha Yojana (JSY) is one of the largest such financial incentive programs in the world. The program pays eligible pregnant women to deliver their babies in health institutions partnered with the program. This paper studies the impact of the JSY on changes in mothers’ health-seeking behavior to deliver in-facility and on the out-of-pocket expenditure (OOPE) for delivery that they incur. Using data from the most recent wave of India’s District-Level Household Survey conducted in 2007-08, this paper finds that the overall introduction of the program in districts in India does not lead to significant changes in institutional delivery or out-ofpocket expenditure outcomes. Further analysis of subpopulations shows that marginalized populations are responsive to JSY introduction in their district with increased probability of delivering in-facility of 1.10 – 3.40 percentage points. Lastly, results show that receiving JSY payments leads to a 1.34 percentage point increase in the probability of incurring OOPE, but a 4.81 percent decrease in the amount of OOPE incurred. The JSY is helping to reduce overall out-of-pocket spending on deliveries. However, the majority of program benefits are not reaching poor pregnant women as the JSY aims, communicating the need for improvement in population targeting.

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Advisor: Alison Hagy, Kent Kimbrough, Manoj Mohanan | JEL Codes: C22, I12, I18 | Tagged: Conditional Cash Transfer, Demand-side Financing, Difference-in-difference-in-differences, Difference-in-differences, Healthcare Reform, Maternal Health

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.

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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

Dealing with Data: An Empirical Analysis of Bayesian Black-Litterman Model Extensions

By Daniel Roeder

Portfolio Optimization is a common financial econometric application that draws on various types of statistical methods. The goal of portfolio optimization is to determine the ideal allocation of assets to a given set of possible investments. Many optimization models use classical statistical methods, which do not fully account for estimation risk in historical returns or the stochastic nature of future returns. By using a fully Bayesian analysis, however, this analysis is able to account for these aspects and also incorporate a complete information set as a basis for the investment decision. The information set is made up of the market equilibrium, an investor/expert’s personal views, and the historical data on the assets in question. All of these inputs are quantified and Bayesian methods are used to combine them into a succinct portfolio optimization model. For the empirical analysis, the model is tested using monthly return data on stock indices from Australia, Canada, France, Germany, Japan, the U.K.
and the U.S.

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Advisor: Andrew Patton | JEL Codes: C1, C11, C58, G11 | Tagged: Bayesian Analysis Global Markets Mean-Variance Portfolio Optimization


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