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
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
Volatility and Correlation Modeling for Sector Allocation in International Equity Markets
By Melanie Fan
Reliable estimates of volatility and correlation are crucial in asset allocation and risk management. This paper investigates Static, RiskMetrics, and Dynamic Conditional Correlation (DCC) models for estimating volatility and correlation by testing them in an asset allocation context. Optimal allocation weights for one year found using estimates from each model are carried to the subsequent year and the realized Sharpe ratio is computed to assess portfolio performance. We also study cumulative risk-adjusted returns over the entire sample period. Our ndings indicate that DCC does not consistently have an advantage over the other two models, although it is optimal in certain scenarios.
Advisor: Aino Levonmaa, Emma Rasiel | JEL Codes: C32, C51, G11, G15 | Tagged: Asset Allocation, Dynamic Correlation, Emerging Markets, Volatilita
Beta Estimation Using High Frequency Data
By Angela Ryu
Using high frequency stock price data in estimating nancial measures often causes serious distortion. It is due to the existence of the market microstructure noise, the lag of the observed price to the underlying value due to market friction. The adverse eect of the noise can be avoided by choosing an appropriate sampling frequency. In this study, using mean square error as the measure of accuracy in beta estimation, the optimal pair of sampling frequency and the trailing window was empirically found to be as short as 1 minute and 1 week, respectively. This surprising result may be due to the low market noise resulting from its high liquidity and the econometric properties of the errors-in-variables model. Moreover, the realized beta obtained from the optimal pair outperformed the constant beta from the CAPM when overnight returns were excluded. The comparison further strengthens the argument that the underlying beta is time-varying.
Advisor: George Tauchen | JEL Codes: C51, C58, G17 | Tagged: Beta estimation, Beta Trailing Window, High-Frequency Data, Market Microstructure Noise, Optimal Sampling Interval, Realized Beta
