Zhou, Ying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 89-91). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Aims and objectives --- p.2 / Chapter 1.3 --- Outline of the thesis --- p.3 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Portfolio Optimization Problems --- p.6 / Chapter 2.2 --- Cooperative Games and Cooperative Investment Models --- p.8 / Chapter 2.2.1 --- Linear Production Games And Basic Concepts of Co- operative Game Theory --- p.9 / Chapter 2.2.2 --- Investment Models Using Linear Production Games --- p.12 / Chapter 3 --- Multi-period Cooperative Investment Games: Basic Model --- p.15 / Chapter 3.1 --- Cooperative Investment Game under Deterministic Case --- p.16 / Chapter 3.2 --- Cooperative Investment Game with Stochastic Return --- p.18 / Chapter 3.2.1 --- Basic Assumptions --- p.18 / Chapter 3.2.2 --- Choose the Proper Risk Measure --- p.20 / Chapter 3.2.3 --- One Period Case --- p.21 / Chapter 3.2.4 --- Multi-Period Case --- p.23 / Chapter 4 --- The Two-Period Investment Game under L∞ Risk Measure --- p.26 / Chapter 4.1 --- The Two Period Model --- p.26 / Chapter 4.2 --- The Algorithm --- p.35 / Chapter 4.3 --- Optimal Solution of the Dual --- p.41 / Chapter 5 --- Primal Solution and Stability of the Core under Two-Period Case --- p.43 / Chapter 5.1 --- Direct Results --- p.44 / Chapter 5.2 --- Find the Optimal Solutions of the Primal Problem --- p.46 / Chapter 5.3 --- Relationship between A and the Core --- p.53 / Chapter 5.3.1 --- Tracing out the efficient frontier --- p.54 / Chapter 6 --- Multi-Period Case --- p.63 / Chapter 6.1 --- Common Risk Price and the Negotiation Process with Concave Risk Utility --- p.64 / Chapter 6.1.1 --- Existence of Common Risk Price and Core --- p.65 / Chapter 6.1.2 --- Negotiation Process --- p.68 / Chapter 6.2 --- Modified Simplex Method --- p.71 / Chapter 7 --- Other Risk Measures --- p.76 / Chapter 7.1 --- The Downside Risk Measure --- p.76 / Chapter 7.1.1 --- Discrete (Finite Scenario) Distributions --- p.78 / Chapter 7.1.2 --- General Distributions --- p.81 / Chapter 7.2 --- Coherent Risk Measure and CVaR --- p.83 / Chapter 8 --- Conclusion and Future Work --- p.87

Siu, Tsz Hang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 59-61). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction and Literature Review --- p.1 / Chapter 2 --- Data and Methodology --- p.5 / Chapter 2.1 --- Portfolio Formation --- p.8 / Chapter 2.2 --- Delisting --- p.11 / Chapter 2.3 --- Rebalancing --- p.11 / Chapter 2.4 --- Performance Measurement --- p.12 / Chapter 3 --- Results --- p.16 / Chapter 3.1 --- Daily Portfolio Returns --- p.16 / Chapter 3.2 --- CAPM and Fama French Model --- p.18 / Chapter 3.3 --- Cumulative Returns --- p.22 / Chapter 3.4 --- Over Different Time Periods --- p.22 / Chapter 3.5 --- Analysis on Capital Market Theory --- p.24 / Chapter 3.6 --- Explanations --- p.27 / Chapter 3.6.1 --- Overconfidence --- p.27 / Chapter 3.6.2 --- Anchoring --- p.28 / Chapter 3.6.3 --- A Simple Model and Smoothing Effect --- p.29 / Chapter 3.6.4 --- Securities Selection --- p.32 / Chapter 3.6.5 --- Transaction Costs --- p.32 / Chapter 4 --- Conclusions --- p.33 / Chapter A --- Proof --- p.36 / Chapter B --- Tables and Figures --- p.40 / Bibliography --- p.59

The aim of this thesis is to study multi-asset barrier options, where the volatilities of the stocks are assumed to define a matrix-valued bounded stochastic process. The bounds on volatilities may represent, for instance, the extreme values of the volatilities of traded options. As the volatilities are not known exactly, the value of the option can not be determined. Nevertheless, it is possible to calculate extreme values. We show that these values correspond to the best and the worst case scenarios of the future volatilities for short positions and long positions in the portfolio of the options. Our main tool is the equivalence of the option pricing and a certain stochastic control problem and the resulting concept of superhedging. This concept has been well known for some time but never applied to barrier options. First, we prove the dynamic programming principle (DPP) for the control problem. Next, using rather standard arguments we derive the Hamilton-Jacobi-Bellman equation for the value function. We show that the value function is a unique viscosity solution of the Hamilton-Jacobi-Bellman equation. Then we define the super price and superhedging strategy for the barrier options and show equivalence with the control problem studied above. The superprice price can be found by solving the nonlinear Hamilton-Jacobi-Equation studied above. It is called sometimes the Black-Scholes-Barenblatt (BSB) equation. This is the Hamilton-Jacobi-Bellman equation of the exit control problem. The sup term in the BSB equation is determined dynamically: it is either the upper bound or the lower bound of the volatility matrix, according to the convexity or concavity of the value function with respect to the stock prices. By utilizing a probabilistic approach, we show that the value function of the exit control problem is continuous. Then, we also obtain bounds for the first derivative of the value function with respect to the space variable. This derivative has an important financial interpretation. Namely, it allows us to define the superhedging strategy. We include an example: pricing and hedging of a single-asset barrier option and its numerical solution using the finite difference method.

The goal of this study is to test the accuracy of various mutual fund timing and selectivity models under a range of portfolio managerial skills and varying market conditions. Portfolio returns in a variety of skill environments are generated using a simulation procedure. The generated portfolio returns are based on the historical patterns and time series behavior of a market portfolio proxy and on a sample of mutual funds. The proposed timing and selectivity portfolio returns mimic the activities of actual mutual fund managers who possess varying degrees of skill. Using the constructed portfolio returns, various performance models are compared in terms of their power to detect timing and selectivity abilities, by means of an iterative simulation procedure. The frequency of errors in rejecting the null hypotheses of no market timing and no selectivity abilities shape the analyses between the models for power comparison. The results indicate that time varying beta models of Lockwood- Kadiyala and Bhattacharya-Pfleiderer rank highest in tests of both market timing and selectivity. The Jensen performance model achieves the best results in selectivity environments in which managers do not possess timing skill. The Henriksson-Merton model performs most highly in tests of market timing in which managers lack timing skill. The study also investigates the effects of heteroskedasticity on the performance models. The results of analysis before and after model correction for nonconstant error term variance (heteroskedasticity) for specific performance methodologies do not follow a consistent pattern. / Graduation date: 1992

No description available.

Each of the three essays in this dissertation deals with asset timing or allocation using technical techniques and pattern recognition. The first essay uses a technical indicator, the stochastic oscillator, for market timing in the bond market. The trading strategy using this technical indicator is optimized using a genetic algorithm The second essay finds that a measure of market chaos improves the performance of a simple trend-following technique in the stock market. The last essay uses technical analysis for asset allocation. A neural network with technical indicator inputs outperforms both a passive asset mix strategy and a neural network with economic data as inputs.

No abstract supplied / Dissertation (MSc (Mathematics of Finance))--University of Pretoria, 2005. / Mathematics and Applied Mathematics / unrestricted

This dissertation focuses on testing and exploring the usage of the jump-diffusion two-beta asset pricing model. Daily and monthly security returns from both NYSE and AMEX are employed to form various samples for the empirical study. The maximum likelihood estimation is employed to estimate parameters of the jump-diffusion processes. A thorough study on the existence of jump-diffusion processes is carried out with the likelihood ratio test. The probability of existence of the jump process is introduced as an indicator of "switching" between the diffusion process and the jump process. This new empirical method marks a contribution to future studies on the jump-diffusion process. It also makes the jump-diffusion two-beta asset pricing model operational for financial analyses. Hypothesis tests focus on the specifications of the new model as well as the distinction between it and the conventional capital asset pricing model. Both parametric and non-parametric tests are carried out in this study. Comparing with previous models on the risk-return relationship, such as the capital asset pricing model, the arbitrage pricing theory and various multi-factor models, the jump-diffusion two-beta asset pricing model is simple and intuitive. It possesses more explanatory power when the jump process is dominant. This characteristic makes it a better model in explaining the January effect. Extra effort is put in the study of the January Effect due to the importance of the phenomenon. Empirical findings from this study agree with the model in that the systematic risk of an asset is the weighted average of both jump and diffusion betas. It is also found that the systematic risk of the conventional CAPM does not equal the weighted average of jump and diffusion betas.

No description available.

Nie Xiaofeng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 52-56). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- A Minimax Model and Its CAPM --- p.9 / Chapter 2.1 --- Model Formulation --- p.9 / Chapter 2.2 --- Efficient Frontier --- p.11 / Chapter 2.3 --- Market Portfolio --- p.15 / Chapter 2.4 --- CAPM of Minimax Model --- p.22 / Chapter 3 --- "A Revised Minimax Model with Downside Risk, and Its CAPM" --- p.28 / Chapter 3.1 --- Model Formulation --- p.28 / Chapter 3.2 --- Efficient Frontier --- p.30 / Chapter 3.3 --- Market Portfolio and CAPM --- p.38 / Chapter 4 --- Numerical Analysis --- p.43 / Chapter 4.1 --- Efficient Frontiers --- p.43 / Chapter 4.1.1 --- Input Data --- p.45 / Chapter 4.1.2 --- Efficient Frontiers --- p.45 / Chapter 4.2 --- Monthly Rate of Return Comparison --- p.47 / Chapter 5 --- Summary --- p.50 / Bibliography --- p.52