Thesis (doctoral)--Universität Mainz.
Thesis (doctoral)--Universität Greifswald.
Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xii, 145 p.: ill. Includes abstract and vita. Advisor: Sherman D. Hanna, Dept. of Human Ecology. Includes bibliographical references (p. 139-145).
So, Yuk-ming, Theresa.
Thesis (M.B.A.)--University of Hong Kong, 1985.
Wong, Kwok-chuen, 黃國全
In this thesis, two problems of time consistent mean-variance portfolio selection have been studied: mean-variance asset-liability management with regime switchings and mean-variance optimization with state-dependent risk aversion under short-selling prohibition. Due to the non-linear expectation term in the mean-variance utility, the usual Tower Property fails to hold, and the corresponding optimal portfolio selection problem becomes time-inconsistent in the sense that it does not admit the Bellman Optimality Principle. Because of this, in this thesis, time-consistent equilibrium solution of two mean-variance optimization problems is established via a game theoretic approach. In the first part of this thesis, the time consistent solution of the mean-variance asset-liability management is sought for. By using the extended Hamilton-Jacobi- Bellman equation for equilibrium solution, equilibrium feedback control of this MVALM and the corresponding equilibrium value function can be obtained. The equilibrium control is found to be affine in liability. Hence, the time consistent equilibrium control of this problem is state dependent in the sense that it depends on the uncontrollable liability process, which is in substantial contrast with the time consistent solution of the simple classical mean-variance problem in Björk and Murgoci (2010), in which it was independent of the state. In the second part of this thesis, the time consistent equilibrium strategies for the mean-variance portfolio selection with state dependent risk aversion under short-selling prohibition is studied in both a discrete and a continuous time set- tings. The motivation that urges us to study this problem is the recent work in Björk et al. (2012) that considered the mean-variance problem with state dependent risk aversion in the sense that the risk aversion is inversely proportional to the current wealth. There is no short-selling restriction in their problem and the corresponding time consistent control was shown to be linear in wealth. However, we discovered that the counterpart of their continuous time equilibrium control in the discrete time framework behaves unsatisfactory, in the sense that the corresponding “optimal” wealth process can take negative values. This negativity in wealth will change the investor into a risk seeker which results in an unbounded value function that is economically unsound. Therefore, the discretized version of the problem in Bjork et al. (2012) might yield solutions with bankruptcy possibility. Furthermore, such “bankruptcy” solution can converge to the solution in continuous counterpart as Björk et al. (2012). This means that the negative risk aversion drawback could appear in implementing the solution in Björk et al. (2012) discretely in practice. This drawback urges us to prohibit short-selling in order to eliminate the chance of getting non-positive wealth. Using backward induction, the equilibrium control in discrete time setting is explicit solvable and is shown to be linear in wealth. An application of the extended Hamilton-Jacobi-Bellman equation leads us to conclude that the continuous time equilibrium control is also linear in wealth. Also, the investment to wealth ratio would satisfy an integral equation which is uniquely solvable. The discrete time equilibrium controls are shown to converge to that in continuous time setting. / published_or_final_version / Mathematics / Master / Master of Philosophy
Draper, Paul Richard
This study attempts to set out in detail some of the factors and influeuces affecting portfolio decisions. In particular it attempts to outline the factors affecting portfolio selection decisions in an investment management organisation. Influences on share selection such as the need for diversification in portfolios, the desire to buy marketable stocks and the use of sector selection - a technique for selecting shares by their industry characteristics - as well as a variety of institutional factors are discussed at some length. Specific factors involved in investment analysis, such as intrinsic value analysis, and methods of portfolio evaluations are also considered. With this basis it is then possible to investigate more fully the value and usefulness of one of the managers decision rules. The technique investigated - sector selection - was on the one hand, felt by the investment managers to be a central and important part of their portfolio construction techniques contributing significantly to the performance of their portfolios, whilst on the other hand it was believed by the author, on the basis of preliminary observations, to be of rather less consequence. To resolve this conflict a multi-stage analysis (discussed below) was devised to provide empirical evidence as to the theoretical validity and practical usefulness of the technique.
Morrison, Alan D.
No description available.
Optimal trading strategies and risk in the government bond market : two essays in financial economicsKoster, Hendrik Aaldrik Jan January 1987 (has links)
The two main questions arising from the problem of optimal bond portfolio management concern the formulation of an optimal trading rule and the specification of an appropriate dynamic risk measure in which to express portfolio objectives. We study these questions in two related essays: (l) a theoretical study of optimal trading policies in view of, as yet unspecified, portfolio objectives when trading is costly; and (2) an empirical, comparative study of several bond risk measures, proposed in the literature or in use by practitioners, for the government or default-free bond market. The theoretical study considers a delegated portfolio management setting, in which the manager optimizes a cumulative reward over a finite time period and where the reward rate increases with portfolio value and decreases with deviations from the given risk objectives. Trading is then often not worthwhile, as the possible gains from smaller objective deviations are offset by losses on account of transactions costs. This setting obviates the need for separate ex post performance evaluation. The trading problem is formulated as one of optimal impulse control in the framework of stochastic dynamic programming; this formulation improves upon prior results in the literature using continuous control theory. A myopic optimal trading rule is characterized, which is also applicable to time-homogeneous problems and more general preferences. An algorithm for its use in applications is derived. The empirical study applies the usual methods of stock market tests to the returns of constant risk bond portfolios. These portfolios are artificial constructs composed, at varying risk levels, of traded bonds on the basis of six different one or two dimensional risk measures. These risk measures are selected in order to obtain a cross-section of term structure variabilities; they include duration, short interest rate risk, long (13-year) interest rate risk, combined short and consol rate risks, duration combined with convexity, and average time-to-maturity. The sample period is the 1970s decade, for which parameter estimates for the risk measures— where necessary—are available from source papers. This period is known to be one with wide-ranging term structure movements and is therefore ideally suited for the tests of this paper. Portfolios are formed at two levels of diversification: bullet and ladder selection. We confirm that all of these risk measures are reasonably effective in capturing relevant bond market risk: the state space of bond returns has in all cases a low dimension (two or three), with only a single factor significantly priced. Best fit is found for portfolios selected by duration, the 13-year spot yield risk, and the two-dimensional short/consol rate risk, all of which consist predominantly of "long" rate risk. The short rate-based risk measure does not explain portfolio returns as well: it has difficulty discriminating between portfolios with long remaining times-to-maturity. Convexity, furthermore, adds nothing to the explanatory power of duration. Average time-to-maturity compares reasonably well with the above risk measures, provided the portfolios are well-diversified across the maturity spectrum; this lends some support to the use of yield curves. A strong diversification effect has also been found, to the extent that the returns on ladder portfolios are practically linear combinations of two or three of the portfolios, typically the lowest and highest risk portfolios in the one dimensional risk cases, with an intermediate portfolio added in the two-dimensional cases. Provided that diversified portfolios are used, the comparatively easy to implement duration measure is as good as any of the risk measures tested. / Business, Sauder School of / Finance, Division of / Graduate
Hou, Wenting. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 113-117). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature Review --- p.1 / Chapter 1.2 --- Problem Description --- p.8 / Chapter 1.3 --- The Main Contributions of This Thesis --- p.11 / Chapter 2 --- Model I --- p.13 / Chapter 2.1 --- Notation --- p.13 / Chapter 2.2 --- Model Formulation --- p.16 / Chapter 2.3 --- Analytical Solution --- p.19 / Chapter 3 --- Model II --- p.25 / Chapter 3.1 --- Model Formulation --- p.25 / Chapter 3.2 --- Analytical Solution --- p.30 / Chapter 3.3 --- How to Find y --- p.38 / Chapter 3.4 --- Numerical Example --- p.42 / Chapter 4 --- Model III --- p.47 / Chapter 4.1 --- Model Formulation --- p.48 / Chapter 4.2 --- Dynamic Programming --- p.50 / Chapter 4.2.1 --- DP I --- p.50 / Chapter 4.2.2 --- DP II --- p.53 / Chapter 4.3 --- Approximate Analytical Solution --- p.56 / Chapter 4.4 --- Computational Result Comparison --- p.65 / Chapter 5 --- Conclusions --- p.73 / Chapter A --- Source Data --- p.76 / Chapter A.l --- rti --- p.76 / Chapter A.2 --- qti --- p.79 / Chapter B --- Model II Numerical Example and Result --- p.82 / Chapter B. --- l Value of xti when A = 0.3 --- p.82 / Chapter B.2 --- Value of xti when A = 0.6 --- p.84 / Chapter B.3 --- Value of xti when A = 0.9 --- p.88 / Chapter B.4 --- True Value of xti --- p.91 / Chapter C --- Model III Numerical Example and Result --- p.98 / Chapter C.l --- The Value of Mt of DP II --- p.98 / Chapter C.2 --- Track of Optimal Value of DP II --- p.101 / Chapter C.3 --- The Optimal Total Wealth of DP II --- p.105 / Chapter C.4 --- The Optimal Asset Allocation of P4 --- p.109 / Bibliography --- p.113
Theoretical and numerical study on continuous-time mean-variance optimal strategies. / Theoretical & numerical study on continuous-time mean-variance optimal strategiesJanuary 2006 (has links)
Li Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 87-88). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.8 / Chapter 2.1 --- Markowitz´ةs Single-Period Mean-Variance Model --- p.9 / Chapter 2.2 --- Discrete-Time Mean-Variance Problem --- p.10 / Chapter 2.2.1 --- Optimal Buy-and-Hold Policy --- p.11 / Chapter 2.2.2 --- Optimal Rolling Markowitz Policy --- p.12 / Chapter 2.2.3 --- Multi-Period Mean-Variance Optimal Policy --- p.12 / Chapter 2.3 --- Continuous-Time Market --- p.13 / Chapter 2.3.1 --- Optimal Unconstrained Policy --- p.15 / Chapter 2.3.2 --- Bankruptcy Prohibited Optimal Policy --- p.16 / Chapter 2.3.3 --- No-Shorting Optimal Policy --- p.17 / Chapter 2.4 --- Continuously Rebalancing Optimal Policy --- p.18 / Chapter 3 --- Discretized Continuous-Time Optimal Policies --- p.20 / Chapter 3.1 --- Problem Setup --- p.21 / Chapter 3.2 --- Unconstrained Problem --- p.25 / Chapter 3.3 --- Problem with No-shorting Constraint --- p.31 / Chapter 3.4 --- Problem with No-Bankruptcy Constraint --- p.34 / Chapter 3.4.1 --- Quasi No-Bankruptcy Problem --- p.36 / Chapter 3.5 --- Stability of the Simulation --- p.38 / Chapter 3.6 --- Concluding Remarks --- p.41 / Chapter 4 --- Performance of Continuous-Time M-V Optimal Policies --- p.43 / Chapter 4.1 --- Measures of the Performance by Probabilities --- p.45 / Chapter 4.2 --- Performance of the Optimal Mean-Variance Portfolio --- p.51 / Chapter 4.2.1 --- Target-Hitting Probability --- p.51 / Chapter 4.2.2 --- Cut-Off Probability --- p.53 / Chapter 4.2.3 --- Target-Hitting-before-Cut-Off Probability --- p.58 / Chapter 4.3 --- Numerical Evaluations of Probabilities for Discrete-Time Market --- p.63 / Chapter 4.3.1 --- Simulation on Target-Hitting Probability --- p.64 / Chapter 4.3.2 --- Simulation on Zero-Hitting Probability --- p.66 / Chapter 4.3.3 --- Simulation on Target-Hitting-before-Bankruptcy Probability --- p.67 / Chapter 4.4 --- Policy Comparison --- p.68 / Chapter 4.4.1 --- Profile of the Probabilities --- p.70 / Chapter 4.4.2 --- Impact of z on the Probabilities --- p.72 / Chapter 4.5 --- Concluding Remarks --- p.74 / Chapter 5 --- Empirical Analysis --- p.75 / Chapter 5.1 --- Experiment Description and Parameter Estimation --- p.76 / Chapter 5.1.1 --- Introduction of the Data --- p.76 / Chapter 5.1.2 --- Experiment Description --- p.77 / Chapter 5.1.3 --- Parameter Estimation --- p.79 / Chapter 5.2 --- Empirical Results and Analysis --- p.80 / Chapter 5.2.1 --- Performance Indicator --- p.80 / Chapter 5.2.2 --- Experimental Results and Analysis --- p.81 / Chapter 5.3 --- Concluding Remarks --- p.83 / Chapter 6 --- Summary --- p.84 / Bibliography --- p.87
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