Decision, Operations & Information Technologies Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2761
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Item Empirical Analyses on Federal Thrift Savings Plan Portfolio Optimization(2007-11-27) Nestler, Scott T; Fu, Michael C; Madan, Dilip B; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There is ample historical data to suggest that log returns of stocks and indices are not independent and identically distributed Normally, as is commonly assumed. Instead, the returns of financial assets are skewed and have higher kurtosis. To account for skewness and excess kurtosis, it is necessary to have a distribution that is more flexible than the Gaussian distribution and uses additional information that may be present in higher moments. The federal government's Thrift Savings Plan (TSP) is the largest defined contribution retirement savings and investment plan, with nearly 3.6 million participants and over $173 billion in assets. The TSP offers five assets (government bond fund, fixed income fund, large-cap stock fund, small-cap stock fund, and international stock fund) to U.S. government civilian employees and uniformed service members. The limited choice of investments, in comparison to most 401(k) plans, may be disappointing from a participant's perspective; however, it provides an attractive framework for empirical study. In this study, we investigate how the optimal choice of TSP assets changes when traditional portfolio optimization methods are replaced with newer techniques. Specifically, the following research questions are posed and answered: (1) Does use of a non-Gaussian factor model for returns, generated with independent components analysis (ICA) and following the Variance Gamma (VG) process, provide any advantage in constructing optimal TSP portfolios? (2) Can excess TSP portfolio returns be generated through rebalancing to an optimal mix? If so, what frequency of rebalancing provides benefits that offset increased computationalal and administrative burden? (3) How does the use of coherent risk and portfolio performance measures, in place of variance as the traditional the measure for risk and Sharpe Ratio as the usual portfolio performance measure affect TSP portfolio selection? We show through simulation that some of the newer schemes should produce excess returns over conventional (mean-variance optimization with Normally-distributed returns) portfolio choice models. The use of some or all of these methods could benefit the nearly 4 million TSP participants in achieving their retirement savings and investment objectives. Furthermore, we propose two new portfolio performance measures based on recent developments in coherent measures of risk.Item THREE ESSAYS ON MORTGAGE BACKED SECURITIES: HEDGING INTEREST RATE AND CREDIT RISKS(2003-12-05) Chen, Jian; Fu, Michael C.; Decision and Information TechnologiesThis dissertation includes three essays on hedging the interest rate and credit risks of Mortgage-Backed Securities (MBS). Essay one addresses the problem of how to efficiently estimate interest rate sensitivity parameters of MBS. To do this in Monte Carlo simulation, we derive perturbation analysis (PA) gradient estimators in a general setting. Then we apply the Hull-White interest rate model and a common prepayment model to derive the corresponding specific PA estimators, assuming the shock of interest rate term structure takes the form of a trigonometric polynomial series. Numerical experiments comparing finite difference (FD) estimators with our PA estimators indicate that the PA estimators can provide better accuracy than FD estimators, while using much lower computational cost. Using the estimators, we analyze the impact of term structure shifts on various mortgage products. Based these analysis, we propose a new product to mitigate interest rate risk. Essay two addresses the problem of how to measure interest rate yield curve shift more realistically, and how to use these risk measures to hedge the interest rate risk of MBS. We use a Principal Components Analysis (PCA) approach to analyze historical interest rate data, and acquire the volatility factors we need in Heath-Jarrow-Morton interest rate model simulation. Then we propose a hedging algorithm to hedge MBS, based on PA gradient estimators derived upon these PCA factors. Our results show that the new hedging method can achieve much better hedging efficiency than traditional duration and convexity hedging. Essay three addresses the application a new regression method on credit spread data. Previous research has shown that variables in traditional structural model have limited explanatory power in credit spread regression. We argue that this is partially due to the non-constancy of the credit spread gradients to state variables. We use a Random Coefficient Regression (RCR) model to accommodate this problem. The explanatory power increases dramatically with the new RCR model, without adding new independent variables. This is the first work to address the dependence between credit spread sensitivities and state variables of structural in a systematic way. Also our estimates are consistent with prediction from Merton’s structural model.