published_or_final_version / Mathematics / Master / Master of Philosophy

Chiu Chun Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 112-114). / Abstracts in English and Chinese. / Abstract --- p.i / 論文摘要 --- p.ii / Acknowledgment --- p.iii / List of Figures n --- p.i / List of Tables n --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.4 / Chapter 3 --- Review of the Minimax Model --- p.7 / Chapter 4 --- Portfolio Optimization with Shorting --- p.13 / Chapter 4.1 --- Formulation of Minimax Model with Shorting --- p.13 / Chapter 4.2 --- A Simple Optimal Investment Strategy --- p.14 / Chapter 4.2.1 --- All Assets Are Risk --- p.14 / Chapter 4.2.2 --- Some Assets Are Riskless --- p.31 / Chapter 4.3 --- Tracing Out the Efficient Frontier --- p.34 / Chapter 4.3.1 --- No Riskless Assets Are Involved --- p.34 / Chapter 4.3.2 --- Riskless Assets Are Involved --- p.43 / Chapter 4.4 --- Chapter Summary --- p.44 / Chapter 5 --- Portfolio Optimization with Investment Limit --- p.50 / Chapter 5.1 --- Formulation of Minimax Model with Investment Limit --- p.51 / Chapter 5.2 --- Optimal Solution to POI(λ) --- p.52 / Chapter 5.2.1 --- All Assets Are Risky --- p.52 / Chapter 5.2.2 --- Some Assets Are Riskless --- p.67 / Chapter 5.3 --- Chapter Summary --- p.71 / Chapter 6 --- Numerical Analysis --- p.72 / Chapter 6.1 --- Data Analysis --- p.72 / Chapter 6.2 --- Experiment Description and Discussion --- p.75 / Chapter 6.2.1 --- Short-Selling is Allowed --- p.75 / Chapter 6.2.2 --- Comparison Between the Cases With Short-Selling and Without Short-Selling --- p.77 / Chapter 6.3 --- Chapter Summary --- p.79 / Chapter 7 --- Conclusion --- p.39 / Chapter A --- List of Companies Included in Numerical Analysis --- p.82 / Chapter B --- Graphical Result of Section 6.21 --- p.84 / Chapter C --- Graphical Result of Section 6.22 --- p.93 / Bibliography --- p.112

Yung Yan Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 144-147). / Chapter Chapter 1: --- Introduction --- p.1 / Chapter 1.1 --- Review on Portfolio Theory --- p.3 / Chapter - 1.1.1 --- Expected Return and Risk of a Security --- p.3 / Chapter -1.1.2 --- Expected Return and Risk of a Portfolio --- p.4 / Chapter -1.1.3 --- The Feasible Set --- p.5 / Chapter - 1.1.4 --- Assumptions on the Investor --- p.6 / Chapter -1.1.5 --- Efficient Portfolios --- p.6 / Chapter -1.1.5.1 --- Bounds on the Return and Risk of a portfolio --- p.6 / Chapter -1.1.5.2 --- Concavity of the Efficient Set --- p.8 / Chapter -1.1.6 --- The Market Model --- p.9 / Chapter -1.1.7 --- Risk-free Asset --- p.11 / Chapter - 1.1.8 --- Portfolio involving Risk-free Asset --- p.12 / Chapter -1.1.9 --- The Sharpe Ratio --- p.14 / Chapter 1.2 --- Review on Some Trading Models --- p.19 / Chapter -1.2.1 --- Buy and Hold Model --- p.19 / Chapter -1.2.2 --- Trading Model with Prediction Criteria --- p.20 / Chapter -1.2.2.1 --- Two School of Theories --- p.20 / Chapter - 1.2.2.2 --- Prediction of the stock price movement --- p.20 / Chapter -1.2.2.3 --- The Use of Neural Network in Prediction --- p.21 / Chapter -1.2.2.4 --- Single Step and Multi-step Prediction --- p.23 / Chapter - 1.2.2.5 --- Trading Model based on Prediction Criteria --- p.25 / Chapter - 1.2.2.6 --- For More Accurate Prediction --- p.25 / Chapter -1.2.3 --- Weigend's Model --- p.26 / Chapter - 1.2.3.1 --- Introduction --- p.26 / Chapter -1.2.3.2 --- The Model Setup --- p.26 / Chapter -1.2.3.3 --- The Objective Functions --- p.27 / Chapter - 1.2.3.4 --- The Gradient Ascending Algorithm --- p.27 / Chapter -1.2.3.5 --- The Gradient of the Sharpe Ratio --- p.27 / Chapter - 1.2.3.6 --- The Training Procedure --- p.28 / Chapter - 1.2.3.7 --- Some Properties of the Sharpe Ratio Training --- p.28 / Chapter -1.2.4 --- Bengio's Model --- p.29 / Chapter -1.2.4.1. --- Overview --- p.29 / Chapter -1.2.4.2. --- The Trading System --- p.29 / Chapter - 1.2.4.3 --- The Objective Function: the Portfolio Return --- p.31 / Chapter - 1.2.4.4. --- The Training Process --- p.32 / Chapter - 1.2.4.5 --- Computer Simulation --- p.34 / Chapter - 1.2.4.6 --- Discussion --- p.36 / Chapter Chapter 2: --- The Naive Sharpe Ratio Model --- p.38 / Chapter - 2.1 --- Introduction --- p.39 / Chapter - 2.2 --- Definition of the Naive Sharpe Ratio --- p.39 / Chapter - 2.3 --- Gradient of Naive Sharpe Ratio with respect to the portfolio weighting: --- p.40 / Chapter - 2.4 --- The Training Process --- p.40 / Chapter - 2.5 --- Analysis of the Gradient --- p.41 / Chapter -2.6 --- Compare with Bengio's and Weigend's Model --- p.42 / Chapter -2.7. --- Computer Simulations --- p.43 / Chapter -2.7.1 --- Experiment 1: How the Sharpe Ratio is Maximized --- p.43 / Chapter -2.7.1.1 --- Experiment 11 --- p.44 / Chapter -2.7.1.2 --- Experiment 12 --- p.45 / Chapter -2.7.1.3 --- Experiment 13 --- p.46 / Chapter -2.7.2 --- Experiment 2: Reducing the Unique Risk --- p.49 / Chapter -2.7.3 --- Experiment 3: Apply to the Stock Market --- p.52 / Chapter -2.8 --- Redefining the Naive Sharpe ratio with down-side risk --- p.56 / Chapter -2.8.1 --- Definitions --- p.56 / Chapter -2.8.2 --- Gradient of the Downside Nai've Sharpe Ratio --- p.57 / Chapter -2.8.3 --- Analysis of the gradient of the new Sharpe ratio --- p.57 / Chapter -2.8.4 --- Experiment: Compared with Symmetric Risk --- p.59 / Chapter -2.8.4.1 --- Experimental Setup --- p.59 / Chapter -2.8.4.2 --- Experimental Result --- p.60 / Chapter -2.8.4.3 --- Discussion --- p.62 / Chapter - 2.9 --- Further Discussion --- p.63 / Chapter Chapter 3: --- The Total Sharpe Ratio Model --- p.64 / Chapter - 3.1 --- Introduction --- p.65 / Chapter -3.2 --- Defining risk of portfolio in terms of component securities' risk --- p.65 / Chapter -3.2.1. --- Return for Each Security and the Whole Portfolio at Each Time Step --- p.65 / Chapter -3.3.2. --- Covariance of the Individual Securities' Returns --- p.66 / Chapter -3.2.3. --- Define the Sharpe Ratio and the Objective Function --- p.66 / Chapter -3.2.3.1. --- The Excess Return --- p.66 / Chapter -3.2.3.2. --- The Risk --- p.67 / Chapter -3.2.3.3. --- The Sharpe Ratio at Time t --- p.67 / Chapter -3.2.3.4. --- The Objective Function: the total Sharpe ratio --- p.67 / Chapter -3.2.3.5. --- The Training Process --- p.68 / Chapter -3.3 --- Calculating the Gradient of the Total Sharpe Ratio --- p.69 / Chapter -3.4. --- Analysis of the Total Sharpe Ratio Gradient --- p.70 / Chapter -3.4.1 --- The Gradient Vector of the Sharpe Ratio at a Particular Time Step --- p.70 / Chapter -3.4.2 --- The Gradient Vector of the Risk --- p.70 / Chapter - 3.5 --- Computer Simulation: --- p.72 / Chapter -3.5.1 --- Apply to the Stock Market1 --- p.72 / Chapter -3.5.1.1 --- Objective --- p.72 / Chapter - 3.5.1.2 --- Experimental Setup --- p.72 / Chapter -3.5.1.3 --- The Experimental Result --- p.73 / Chapter -3.5.2 --- Apply to the Stock Market2 --- p.78 / Chapter -3.5.2.1 --- Objective --- p.78 / Chapter -3.5.2.2 --- Experimental Setup --- p.78 / Chapter -3.5.2.3 --- The Experimental Result --- p.79 / Chapter -3.6 --- Defining the Total Sharpe Ratio in terms of Downside Risk --- p.84 / Chapter - 3.6.1. --- Introduction --- p.84 / Chapter -3.6.2. --- Covariance of the individual securities' returns --- p.84 / Chapter -3.6.3. --- Define the Downside Risk Sharpe ratio and the objective function --- p.85 / Chapter -3.6.3.1. --- The Excess Return --- p.85 / Chapter -3.6.3.2. --- The Downside Risk --- p.85 / Chapter -3.6.3.3. --- The Sharpe ratio at time T --- p.85 / Chapter -3.6.3.4. --- The Objective function --- p.85 / Chapter -3.6.4. --- The Training Process --- p.85 / Chapter -3.7 --- Total Sharpe Ratio involving Transaction Cost --- p.86 / Chapter -3.7.1 --- Introduction --- p.86 / Chapter -3.7.2 --- Return for each stock and the whole portfolio at each time step --- p.86 / Chapter -3.7.3 --- Linear Approximation of the Portfolio's return --- p.88 / Chapter -3.7.4 --- Covariance of the individual securities' returns --- p.89 / Chapter -3.7.5 --- Define the Sharpe ratio and the objective function --- p.90 / Chapter -3.7.5.1 --- The Excess Return --- p.90 / Chapter -3.7.5.2 --- The Risk --- p.90 / Chapter -3.7.5.3 --- The Sharpe Ratio at time T --- p.90 / Chapter -3.7.5.4 --- The Objective Function --- p.90 / Chapter -3.7.6 --- Calculation of the gradient of the Total Sharpe ratio --- p.91 / Chapter -3.7.7. --- Analysis of the Total Sharpe Ratio Gradient --- p.94 / Chapter -3.7.7.1 --- The Gradient Vector of the Sharpe Ratio at a Particular Time Step --- p.94 / Chapter -3.7.7.2 --- The Gradient Vector of the Risk --- p.94 / Chapter -3.7.8 --- Experiment 1: Compare with Buy and Hold Method --- p.96 / Chapter -3.7.8.1 --- Experiment 11 --- p.96 / Chapter -3.7.8.2. --- Experiment 12 --- p.102 / Chapter -3.7.9 --- Experiment 2: Compared with Naive Sharpe Ratio --- p.108 / Chapter -3.7.9.1 --- Objective --- p.108 / Chapter -3.7.9.2. --- Experimental Setup --- p.108 / Chapter -3.7.9.3. --- The Experimental Result --- p.109 / Chapter - 3.7.10 --- Experiment 3: Compared with other models --- p.113 / Chapter - 3.7.10.1 --- Experiment 31 --- p.113 / Chapter - 3.7.10.2. --- Experiment 32 --- p.117 / Chapter -3.7.11 --- Experiment 4: Apply to the Stock Market --- p.121 / Chapter -3.7.11.1 --- Objective --- p.121 / Chapter - 3.7.11.2. --- Experimental Setup --- p.121 / Chapter -3.7.11.3. --- The Experimental Result --- p.121 / Chapter Chapter 4: --- Conclusion --- p.126 / Appendix A --- p.130 / Appendix B --- p.139 / Appendix C --- p.141 / Appendix D --- p.142 / Reference --- p.144

Sen Sui. / Thesis submitted in: August 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 88-92). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.iv / Dedication --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objective --- p.1 / Chapter 1.2 --- Contributions --- p.4 / Chapter 1.3 --- Thesis Organization --- p.5 / Chapter 2 --- Background of Risk Management --- p.7 / Chapter 2.1 --- Measuring Return --- p.8 / Chapter 2.2 --- Objectives of Risk Measurement --- p.11 / Chapter 2.3 --- Simple Statistics for Measurement of Risk --- p.15 / Chapter 2.4 --- Methods for Value-at-Risk Measurement --- p.16 / Chapter 2.5 --- Conditional VaR --- p.18 / Chapter 2.6 --- Portfolio VaR Methods --- p.18 / Chapter 2.7 --- Coherent Risk Measure --- p.20 / Chapter 2.8 --- Summary --- p.22 / Chapter 3 --- Selection of Independent Factors for VaR Computation --- p.23 / Chapter 3.1 --- Mixture Convolution Approach Restated --- p.24 / Chapter 3.2 --- Procedure for Selection and Evaluation --- p.26 / Chapter 3.2.1 --- Data Preparation --- p.26 / Chapter 3.2.2 --- ICA Using JADE --- p.27 / Chapter 3.2.3 --- Factor Statistics --- p.28 / Chapter 3.2.4 --- Factor Selection --- p.29 / Chapter 3.2.5 --- Reconstruction and VaR Computation --- p.30 / Chapter 3.3 --- Result and Comparison --- p.30 / Chapter 3.4 --- Problem of Using Kurtosis and Skewness --- p.40 / Chapter 3.5 --- Summary --- p.43 / Chapter 4 --- Mixture of Gaussians and Value-at-Risk Computation --- p.45 / Chapter 4.1 --- Complexity of VaR Computation --- p.45 / Chapter 4.1.1 --- Factor Selection Criteria and Convolution Complexity --- p.46 / Chapter 4.1.2 --- Sensitivity of VaR Estimation to Gaussian Components --- p.47 / Chapter 4.2 --- Gaussian Mixture Model --- p.52 / Chapter 4.2.1 --- Concept and Justification --- p.52 / Chapter 4.2.2 --- Formulation and Method --- p.53 / Chapter 4.2.3 --- Result and Evaluation of Fitness --- p.55 / Chapter 4.2.4 --- Evaluation of Fitness using Z-Transform --- p.56 / Chapter 4.2.5 --- Evaluation of Fitness using VaR --- p.58 / Chapter 4.3 --- VaR Estimation using Convoluted Mixtures --- p.60 / Chapter 4.3.1 --- Portfolio Returns by Convolution --- p.61 / Chapter 4.3.2 --- VaR Estimation of Portfolio Returns --- p.64 / Chapter 4.3.3 --- Result and Analysis --- p.64 / Chapter 4.4 --- Summary --- p.68 / Chapter 5 --- VaR for Portfolio Optimization and Management --- p.69 / Chapter 5.1 --- Review of Concepts and Methods --- p.69 / Chapter 5.2 --- Portfolio Optimization Using VaR --- p.72 / Chapter 5.3 --- Contribution of the VaR by ICA/GMM --- p.76 / Chapter 5.4 --- Summary --- p.79 / Chapter 6 --- Conclusion --- p.80 / Chapter 6.1 --- Future Work --- p.82 / Chapter A --- Independent Component Analysis --- p.83 / Chapter B --- Gaussian Mixture Model --- p.85 / Bibliography --- p.88

Chen Li. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 76-78). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iii / Table of Contents --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature review --- p.4 / Chapter 3 --- The semi-mean target tracking model --- p.9 / Chapter 3.1 --- Introduction --- p.9 / Chapter 3.2 --- The robust optimization problem --- p.11 / Chapter 3.3 --- Portfolio selection methods --- p.14 / Chapter 3.3.1 --- Jensen's inequality approach --- p.15 / Chapter 3.3.2 --- The robust optimization approach --- p.17 / Chapter 3.3.3 --- Empirical method --- p.22 / Chapter 3.4 --- How to evaluate a portfolio? --- p.24 / Chapter 3.4.1 --- Tight bounds --- p.24 / Chapter 3.4.2 --- The semidefinite programming bounds --- p.25 / Chapter 3.4.3 --- Conclusions --- p.28 / Chapter 3.5 --- Numerical results --- p.29 / Chapter 3.5.1 --- The analysis of the data --- p.29 / Chapter 3.5.2 --- Jensen's inequality approach --- p.31 / Chapter 3.5.3 --- The robust optimization approach --- p.34 / Chapter 3.5.4 --- The empirical linear programming method --- p.34 / Chapter 3.6 --- Comparisons and conclusions --- p.39 / Chapter 4 --- The semi-variance target tracking model --- p.45 / Chapter 4.1 --- Introduction --- p.45 / Chapter 4.2 --- The portfolio selection methods --- p.46 / Chapter 4.2.1 --- The robust optimization method --- p.47 / Chapter 4.2.2 --- The empirical method --- p.50 / Chapter 4.3 --- Evaluating a selected portfolio --- p.52 / Chapter 4.3.1 --- Computing SDP bounds --- p.52 / Chapter 4.3.2 --- Conclusions --- p.55 / Chapter 4.4 --- Numerical results --- p.55 / Chapter 4.4.1 --- The robust optimization method --- p.56 / Chapter 4.4.2 --- The empirical second order cone programming method --- p.61 / Chapter 4.4.3 --- Comparisons and conclusions --- p.61 / Chapter 4.5 --- Summary and future work --- p.69 / Appendix A --- p.70 / Bibliography --- p.76

Tang Hoi-man = 受抵押品保護的公司債券的估值 / 鄧凱文. / Thesis submitted in: December 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 69-72). / Text in English; abstracts in English and Chinese. / Tang Hoi-man = Shou di ya pin bao hu de gong si zhai quan de gu zhi / Deng Kaiwen. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.3 / Chapter 2.1 --- Review of Merton Model [24] --- p.4 / Chapter 2.1.1 --- Refinement of the Structural Model --- p.6 / Chapter 2.2 --- "Hui, Lo, Huang and Lee model" --- p.8 / Chapter 2.3 --- Recent models consider the PD-RR relationship --- p.11 / Chapter 2.3.1 --- Frye model --- p.11 / Chapter 2.3.2 --- Jokivuolle and Peura model --- p.12 / Chapter 3 --- The Valuation of Collateralized Corporate Bond --- p.13 / Chapter 3.1 --- The framework of the Model --- p.13 / Chapter 3.2 --- The Valuation of Collateralized Corporate Bond --- p.17 / Chapter 3.2.1 --- Derivation of Collateralized Corporate Bond --- p.18 / Chapter 3.2.2 --- "Relation between Proposed Model and Hui, Lo, Huang and Lee Model" --- p.22 / Chapter 4 --- The Study of the Closed-form Solution --- p.24 / Chapter 4.1 --- Applications --- p.24 / Chapter 4.1.1 --- Probabilities of Default --- p.25 / Chapter 4.1.2 --- Expected Loss-given-default --- p.26 / Chapter 4.2 --- Closed-form solution and Monte-Carlo Simulation --- p.29 / Chapter 4.2.1 --- Closed-form Solution --- p.29 / Chapter 4.2.2 --- Monte-Carlo Simulation --- p.31 / Chapter 4.2.3 --- Comparison between Closed-form Solution and Monte- carlo Simulation --- p.33 / Chapter 5 --- Data Analysis and Discussion --- p.38 / Chapter 5.1 --- Effects on ELGD of Different Parameters --- p.38 / Chapter 5.1.1 --- "Effect of Initial Collateral Value, S0" --- p.39 / Chapter 5.1.2 --- "Effect of Collateral's Volatility, σs" --- p.39 / Chapter 5.1.3 --- "Effect of Correlation between Firm's Leverage Ratio and Collateral, pLS" --- p.40 / Chapter 5.1.4 --- "Effect of residue recovery rate, δ" --- p.40 / Chapter 5.1.5 --- "Effect of Maturity, T" --- p.41 / Chapter 5.2 --- Initial Setting of Parameters --- p.42 / Chapter 5.3 --- Effects on ELGD for Different Rated Firms --- p.43 / Chapter 5.3.1 --- Effects on ELGD of Different S0 and σs --- p.45 / Chapter 5.3.2 --- Effect on ELGD of Different S0 and pLS --- p.47 / Chapter 5.3.3 --- Effect on ELGD of Different S0 and δ --- p.49 / Chapter 5.3.4 --- Effect on ELGD of Different S0 and T --- p.51 / Chapter 5.3.5 --- Effect on ELGD of Different and pLS --- p.53 / Chapter 5.3.6 --- Effect on ELGD of Different σs and δ --- p.55 / Chapter 5.3.7 --- Effect on ELGD of Different and T --- p.57 / Chapter 5.3.8 --- Effect on ELGD of Different pLS and δ --- p.59 / Chapter 5.3.9 --- Effect on ELGD of Different pLS and T --- p.61 / Chapter 5.3.10 --- Effect of on ELGD Different 6 and T --- p.63 / Chapter 5.4 --- Summary --- p.65 / Chapter 6 --- Conclusion --- p.67 / Bibliography --- p.69 / Chapter A --- Derivation of Pricing Equation --- p.73

In infinitely repeated games, we also give definitions to risk attitude and reputation. As art infinitely repeated game is a repetition of a constituent strategic game, we transform each single round of an infinitely repeated game as a risk game. We extend the definitions of risk strategies and risk strategy equilibrium to infinitely repeated games. We also research some properties of risk strategy equilibrium and show that players can obtain higher payoffs in risk strategy equilibrium than in pure strategy Nash equilibrium. / In multi-agent systems, agents often need to make decisions, especially under uncertainty. When a decision-maker needs to choose among a number of choices, each having a certain probability to happen, one of the traditional ways discussed in economics is to calculate the expected utility of each choice and choose the one with the maximum expected utility. However, most of the humans do not do so in real situations. Very often, humans choose a choice with a lower expected utility. One of the famous examples is the Allais paradox. / In strategic games, we define risk attitude and reputation, which are factors that decision-makers take into account in making decisions. We transform a strategic game to a risk game. We propose a new kind of strategies, called risk strategies. In the transformed risk game, we find a new kind of equilibrium, called risk strategy equilibrium. We also find out some properties of risk strategy equilibrium. In addition, we find that players can obtain higher payoffs in risk strategy equilibrium than in pure strategy Nash equilibrium. / One of the key properties defining an intelligent agent is social ability. This means that an intelligent agent should be able to interact with other agents or humans. Before designing an intelligent agent for any multi-agent system, we need to first understand how agents should behave and interact in that particular application. / One way to understand how agents should behave in a particular application is to model the application as a game. Besides, many real-life situations can be modeled as games. So, we extend the model of attractiveness maximization and apply the extended model to strategic games and infinitely repeated games. / The reason why most of the people do not maximize the expected utility is that people have different attitudes towards risk in different situations and people are generally risk-averse. To model this human behavior, we propose another way of decision-making, called attractiveness maximization. In this model, every choice has an attractiveness, which is calculated from the risk attitude of the decision-maker, probability, and utility. In making decisions, decision-makers choose the choice with the maximum attractiveness. Using attractiveness maximization, the phenomenon that human do not maximize expected utility can be explained. We also find some properties of the model of attractiveness maximization, which match the human behaviors. / We develop several applications of attractiveness maximization and risk strategies. First, we apply the proposed concepts to the Iterated Prisoner's Dilemma, which is widely used by economists and sociologists to model and simulate many of the human interactions. Simulation shows that agents have improved performance and are reactive as well as pro-active. Second, we construct behavioral predictors and an adaptive strategy for Minority Games, which model many real life situations like the financial market, auctions and resources competitions. Simulations show that the adaptive strategy works much better than previous models. Third, we model a resource allocation problem as a Minority Game and apply the behavioral predictors and the adaptive strategy to the resource allocation problem. Simulations also show that agents with the proposed adaptive strategy are able to make more right decisions and better resource utilization than previous work. / Lam, Ka Man. / "August 2007." / Adviser: Leung Ho Fung. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1107. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 159-173). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

Credit risk is an important source of risk for almost all of the financial securities. The frequent and serious financial crisis has made credit risk a sensitive and crucial consideration for financial institutions, corporations, and individual investors. The accurate pricing for credit risk and credit risky assets depends crucially upon the credit risk term structure---it implies the market expectation for the future credit risk. However, the credit risk analysis is still in its very early stages of development. The investigation about the credit risk term structure, especially the empirical exploration, has many blank points. Earlier research on the credit risk term structure mainly concentrates on the slopes, pertaining to the simple linear term structure which is not applicable to the middle credit quality assets. Thus the curvature of the spread curves may infer snore information about the changes of future credit qualities, the credit cycles, and the recurring business cycles. In this thesis, a bond pair approach is developed to study the shape (curvature as well as slope) of individual spread curves, and the relationship among spread curves for bonds with different ratings. We uncover downward sloping spread curves for triple C and double C bonds and upward sloping spread curves for triple A+ and triple A bonds. We also uncover hump-shaped spread curves for middle-graded bonds including double A to single B, and there exist peak points on these spread curves. We document the relationship among spread curves for bonds with different ratings In terms of time to peak and peak spread. We conclude that, in comparing higher rated bonds (say, double A) with lower rated bonds (say, single B), the credit spread is higher and time to peak is shorter for the latter than the former. In particular, these hump-shaped curves are bounded from above by downward sloping spread curves for triple C and double C bonds and bounded from below by upward sloping spread curves for triple A+ and triple A bonds. These findings provide a good explanation for the middle-rated bonds' spread curves. This evidence helps us to better understand the credit risk term structure, to accurately price credit risk and credit risky assets, and to appropriately manage credit risk. / Hu Wen-wei. / "August 2000." / Adviser: Jia He. / Source: Dissertation Abstracts International, Volume: 61-08, Section: A, page: 3284. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (p. 93-111). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Xiong, Ying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 52-54). / Abstracts in English and Chinese. / Abstract --- p.i / Abstract in Chinese --- p.ii / Acknowledgements --- p.iii / Contents --- p.iv / List of Figures --- p.vi / Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Background and Objective --- p.1 / Chapter 1.1.1. --- Risk Measurement --- p.2 / Value-at-Risk --- p.2 / Expected Shortfall --- p.3 / Computing Method based on Simulation --- p.4 / Chapter 1.1.2. --- Monte-Carlo Simulation --- p.5 / Chapter 1.2. --- Literature Review --- p.6 / Chapter 1.3. --- Structure of This Thesis --- p.8 / Chapter 2. --- Problem Formulation and Review of Past Methods --- p.10 / Chapter 2.1. --- Problem Formulation and Basic Setting --- p.10 / Chapter 2.2. --- Risk Measurement --- p.11 / Chapter 2.3. --- Uniform Sampling --- p.15 / Chapter 2.3.1. --- MSE Estimator --- p.16 / Chapter 2.4. --- Sequential Sampling --- p.17 / Chapter 3. --- Methodology: Our Approach --- p.18 / Chapter 3.1. --- Least-Squares Monte-Carlo Approach --- p.18 / Chapter 3.1.1. --- Framework --- p.19 / Chapter 3.2. --- Stochastic Mesh Method in risk measurement --- p.21 / Chapter 3.2.1. --- Framework --- p.21 / Chapter 3.2.2. --- With a series of cash flows --- p.26 / Chapter 3.2.3. --- Derive Marginal Density and Transition Density --- p.27 / Chapter 4. --- Numerical Experiments --- p.29 / Chapter 4.1. --- Experimental Setting --- p.29 / Chapter 4.2. --- Bias Comparison --- p.31 / Chapter 4.3. --- MSE Comparison --- p.33 / Chapter 4.4. --- Modified Least Square method --- p.44 / Chapter 5. --- Conclusion --- p.47 / Chapter A. --- Appendix A --- p.49 / Chapter A.1. --- Proof of Theorem 3.1 --- p.49 / Chapter A.2. --- Proof of Theorem 3.2 --- p.51

Huang Ming Xi = 三因結構模型之公司債劵定價及其應用 / 黃銘浠. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 99-102). / Text in English; abstracts in English and Chinese. / Huang Ming Xi = San yin jie gou mo xing zhi gong si zhai quan ding jia ji qi ying yong / Huang Mingxi. / Abstract --- p.i / Acknowledgements --- p.iii / Contents --- p.iv / List of Figures --- p.vii / List of Tables --- p.xiii / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Structural Models of Credit Pricing --- p.3 / Chapter 2.1 --- Introduction --- p.3 / Chapter 2.2 --- Merton's Model (1974) --- p.4 / Chapter 2.2.1 --- The Framework of the Traditional Contingent Claims Analysis (CCA) --- p.5 / Chapter 2.2.2 --- The Valuation of Corporate Bonds with B-S Option Pric- ing Theory --- p.9 / Chapter 2.2.3 --- The Limitations of Traditional Contingent Claim Ap- proach --- p.12 / Chapter 2.3 --- "Shimko, Tejima and Deventer (1993)" --- p.15 / Chapter 2.3.1 --- The Merton's Model in a Stochastic Interest Rate Frame- work --- p.15 / Chapter 2.4 --- Longstaff and Schwartz (1995) --- p.17 / Chapter 2.4.1 --- A Structure Model of Early Default Mechanism and De- viations from APR --- p.17 / Chapter 2.5 --- Briys and de Varenne (1997) --- p.21 / Chapter 2.5.1 --- A Structure Model of Stochastic Default Barrier --- p.21 / Chapter 2.5.2 --- The Valuation of Risky Zero-Coupon Bonds --- p.22 / Chapter 2.6 --- Stationary-leverage-ratio Models --- p.25 / Chapter 2.6.1 --- Tauren (1999) --- p.25 / Chapter 2.6.2 --- Collin-Dufresne and Goldstein (2001) --- p.27 / Chapter 2.7 --- Summary --- p.29 / Chapter Chapter 3. --- The Valuation Framework of the Three-factor Model --- p.32 / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.2 --- The Framework of the Three-factor Model --- p.35 / Chapter 3.3 --- The Valuation of Risky Bonds --- p.39 / Chapter 3.3.1 --- Imposing an Early Default Mechanism --- p.42 / Chapter 3.3.2 --- Application: The Valuation of Probability of Default --- p.45 / Chapter Chapter 4. --- The Pricing Methodology of the Three-factor Model --- p.46 / Chapter 4.1 --- Simplification of the Problem --- p.47 / Chapter 4.2 --- Methodology of Upper-lower Bound Scheme --- p.48 / Chapter 4.2.1 --- Single-stage Approximation --- p.48 / Chapter 4.2.2 --- Illustrative Examples --- p.53 / Chapter 4.2.3 --- Multistage Approximation --- p.54 / Chapter 4.2.4 --- Summary --- p.58 / Chapter 4.2.5 --- Systematic Multistage Estimation of Bond Price --- p.61 / Chapter 4.3 --- Estimation of Default Probability --- p.63 / Chapter Chapter 5. --- Numerical Results and Discussion --- p.69 / Chapter 5.1 --- Initial Setting of Parameters --- p.69 / Chapter 5.2 --- Numerical Results and Discussion --- p.74 / Chapter Chapter 6. --- Conclusion --- p.89 / Appendix A. The Derivation of the Three-Factor Model --- p.91 / Bibliography --- p.99