Female Labor Force Participation in Turkic Countries: A Study of Azerbaijan and Turkey
By Natasha Jo Torrens
Encouraging female labor force participation (FLFP) should be a goal of any country attempting to increase their productive capacity. Understanding the determinants and motivations of labor force participation requires isolating the factors that influence a woman’s decision to enter or leave formal employment. In this thesis, I utilize data from the Demographics and Health Surveys to explain the role of social conservatism in promoting or limiting participation in the labor force. I focus on ever-married women in Azerbaijan and Turkey to provide a lens through which to explain the unexpectedly low FLFP of Turkey. Though most prior research attempts to explain Turkey’s low FLFP rate by comparisons to other OECD countries, my study looks at Turkey through the context of other Turkic cultures to explore cultural factors driving labor force participation for ever-married women. This study finds a negative correlation between conservatism and the likelihood of participating in the labor force for ever married women in Azerbaijan, and a larger, positive relationship in Turkey.
Advisors: Professor Charles Becker, Professor Didem Havlioğlu, Professor Kent Kimrbough | JEL Codes: C50, J16, N95
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
significance.
Advisor: Edward Tower | JEL Codes: C55, G10, G11
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.
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
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 2004–2012 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.
Advisor: Kent Kimbrough, Timur Kuran | JEL Codes: C51, J21, J23 | Tagged: Employment, Labor-force Participation, Occupation Women
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.
Advisor: Andrew Patton | JEL Codes: C1, C11, C58, G11 | Tagged: Bayesian Analysis Global Markets Mean-Variance Portfolio Optimization
Conditional Beta Model for Asset Pricing By Sector in the U.S. Equity Markets
By Yuci Zhang
In nance, the beta of an investment is a measure of the risk arising from exposure to general market movements as opposed to idiosyncratic factors. Therefore, reliable estimates of stock portfolio betas are essential for many areas in modern nance, including asset pricing, performance evaluation, and risk management. In this paper, we investigate Static and Dynamic Conditional Correlation (DCC) models for estimating betas by testing them in two asset pricing context, the Capital Asset Pricing Model (CAPM) and Fama-French Three Factor Model. Model precision is evaluated by utilizing the betas to predict out-of-sample portfolio returns within the aforementioned asset-pricing framework. Our findings indicate that DCC-GARCH does consistently have an advantage over the Static model, although with a few exceptions in certain scenarios.
Advisor: Andrew Patton, Michelle Connolly | JEL Codes: C32, C51, G1, G12, G17 | Tagged: Beta, Asset Pricing, Dynamic Correlation, Equity, U.S. Markets
Corporate Financial Distress and Bankruptcy Prediction in North American Construction Industry
By Gang Li
This paper seeks to explore the application of Altman’s bankruptcy prediction model in the construction industry by measuring its percentage accuracy on a dataset consisting of 108 bankrupt & non-bankrupt firms selected across the timeline of 1985-2013. Another main goal this paper is to explore the predictive power of an expanded variable set tailored to the construction industry and compare the results. Specifically, this measuring process is done using machine learning algorithm based on scikit-learn library that transforms a raw .csv file into clean vectorized dataset. The algorithm provides various classifiers to cross-validate the training set, which produces mixed statistics that favors neither variable set but provides insight into the reliability of the non-linear classifiers.
Advisor: Connel Fullenkamp | JEL Codes: C38, C5, G33, G34 | Tagged: Bankruptcy, Corporate, Discriminant Analysis, Distress, Machine Learning
Forecasting Beta Using Conditional Heteroskedastic Models
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: Beta, GARCH, GARCHX, High-Frequency Data, Realized Varience
Cross-Stock Comparisons of the Relative Contribution of Jumps to Total Price Variance
By Vivek Bhattacharya
This paper uses high-frequency price data to study the relative contribution of jumps to the total volatility of an equity. In particular, it systematically compares the relative contribution of jumps across a panel of stocks from three different industries by computing the cross-correlation of this statistic for pairs of stocks. We identify a number of empirical regularities in this cross-correlation and compare these observations to predictions from a standard jump-diffusion model for the joint price process of two stocks. A main finding of this paper is that this jump-diffusion model, when calibrated to particular pairs of stocks in the data, cannot replicate some of the empirical patterns observed. The model predictions differ from the empirical observations systematically: predictions for pairs of stocks from the same industry are on the whole much less accurate than predictions for pairs of stocks from different industries. Some possible explanations for this discrepancy are discussed.
Advisor: George Tauchen | JEL Codes: C5, C52, C58 | Tagged: Econometric Modeling, Financial Econometrics, High-Fequency Data, Jumps
