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

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

Keywords. Hub-to-hub network, bid-price control, certainty equivalent control, combinatorial optimization, structures, primal-dual, revenue management, airline network, monotone thresholds, supermodularity/submodularity, L♮ concavity, Lagrange dual. / The subject of this study is the revenue management problem in hub-to-hub airline networks. The network consists of two hubs and a connecting flight between them with spoke cities expanding outwards. The airline produces various itineraries within the network, and its flights compete with each other for limited flight capacities during a fixed booking period. Although stochastic dynamic network revenue management has been theoretically established, in reality its implementation is still heavily dependent on linear programming-based heuristics. Simpson (1989) and Williamson (1992) proposed bid price control, which is now widely adopted by major airlines. Bertsimas and de Boer (2003) proposed certainty equivalent control, which has been little studied by RM researchers. In this thesis, bid price control is first explained, and then the structural properties of the hub-to-hub network are investigated. Using the Lagrange dual-function and the primal-dual relationship, it is shown that the threshold values used in bid price control have some monotone properties in the network's capacity states. The certainty equivalent control is then applied to the hub-to-hub network. By linking the network revenue management problem with a maximum-weight circulation problem in network flow, the optimal value function is shown to be supermodular in certain capacity dimensions, and submodular in other dimensions. This leads to the monotonicity of CEC thresholds on some short-haul itineraries. The notion of L ♮ concavity developed by Murota and Shioura (2005) is applied to this work, and it is shown that even the CEC thresholds on some two-leg or three-leg long-haul itineraries are monotonically increasing or decreasing in certain legs' capacities. It is hoped that the new structural properties found in this thesis can lead to a reduction of the computational work in the implementation of both the bid price control and the certainty equivalent control in the hub-to-hub airline network. / He, Hongzhi. / Adviser: Zhang Shuzhong. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 111-118). / 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 Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

The efficiency of revenue management relies on the effectiveness of the strategies it employs. In the competing market nowadays, time has become an important concern at both demand and supply sides. With time-sensitive customers and additional benefits from intertemporal demand shift, we find that the sellers could turn to a time-differentiation based strategy as an effective revenue management tool. Motivated by real-life business issues of Toyota China dealerships, in this thesis, we consider stylized inventory-control models with delivery upgrades, in which the seller allocates its on-hand inventory to price and delivery-time sensitive customers. The seller provides two delivery-time options with different prices. Customers choose the proper purchasing option according to their heterogeneous preferences. The seller has two decisions: inventory commitment and inventory replenishment. The former addresses, within an inventory cycle, how on-hand inventories are allocated between the two classes of customers. The latter addresses, between inventory cycles, how the inventory is replenished. Furthermore, the seller may employ early delivery, namely upgrade, to achieve a higher inventory flexibility. We develop the optimal inventory allocation and upgrade, and inventory replenishment policies, and demonstrate that the optimal control can be characterized by a switching curve. Based on the basic model setting, we extend our analysis to include cases of upgrade cost, stock-out substitution, and capacity constraint. We further discuss the joint pricing and inventory decision. We obtain the form of the optimal joint policy, and show that the two strategies may well complement each other in our setting. When each is applied separately, their performances are also compared. To shed more light on the practical side, finally, we use the Toyota dealership data to calibrate the required parameters, and demonstrate the potential of the optimal inventory allocation and upgrade control. / Liang, Xiaoying. / Advisers: Houmin Yan; Youhua Chen. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 118-124). / 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 Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

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

Lam, Ho Man. / "August 2010." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 42-43). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Levy Process --- p.5 / Chapter 2.1 --- Merton's Jump-Diffusion model (1976) --- p.8 / Chapter 2.2 --- Estimation of Levy processes --- p.9 / Chapter 3 --- Transform Martingale Estimation --- p.11 / Chapter 3.1 --- Maximum Likelihood Estimation --- p.11 / Chapter 3.2 --- Transform Martingale Estimating Functions --- p.13 / Chapter 3.2.1 --- Transform Quasi-Score Function --- p.15 / Chapter 3.2.2 --- Composite Quasi-Score Function --- p.17 / Chapter 3.2.3 --- Implementation Issue --- p.18 / Chapter 3.2.4 --- Transform Martingale Estimation on Levy process --- p.21 / Chapter 4 --- Structural Models of Credit Risk --- p.22 / Chapter 4.1 --- Overview --- p.22 / Chapter 4.2 --- Merton's structural credit risk model (1974) --- p.23 / Chapter 4.3 --- Estimation Methodologies --- p.24 / Chapter 4.4 --- Martingale Estimation with KMV's Method --- p.26 / Chapter 5 --- Simulation Study --- p.28 / Chapter 5.1 --- Equity Estimation --- p.28 / Chapter 5.2 --- Estimation of Structural Models --- p.37 / Chapter 6 --- Conclusion --- p.41 / Bibliography --- p.42