Optimal Asset Allocation Problems Under The Discrete Time Regime Switching Model

Optimal Asset Allocation Problems Under the Discrete-time Regime-switching Model

Optimal Asset Allocation Problems Under the Discrete-Time Regime-Switching Model

This dissertation, "Optimal Asset Allocation Problems Under the Discrete-time Regime-switching Model" by Ka-chun, Cheung, 張家俊, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of the thesis entitled OPTIMAL ASSET ALLOCATION PROBLEMS UNDER THE DISCRETE-TIME REGIME-SWITCHING MODEL submitted by Cheung, Ka Chun for the degree of Doctor of Philosophy at The University of Hong Kong in January 2005 Recently, academics and practitioners have started paying attention to using the Markov Regime-Switching process to model asset price dynamics. The Markov Regime-Switchingmodelcancapturetherealitythattheinvestmentenvironment is changing over time and hence is non-stationary. Another merit of the model is that it can provide a reasonable degree of analytical tractability. In this thesis, the optimal behavior of an investor in a Markov regime-switching environment will be examined. The thesis studies the optimal dynamic asset allocation strategy, the optimal consumption strategy in the presence of default risk, and the optimal surrender strategy of an equity-linked investment product. By employing the concept of stochastic dominance and assuming that the transition matrix is stochasticallymonotone, where both the concept and assumption have natural and appealing financial interpretations, it was shown that the optimal behavior of the investor is consistent with our intuition. As default risk is an important subject in mod- ern finance and actuarial science, this thesis also studies the optimal portfolio problem in which financial instruments are subject to dependent default risks. Sufficient condition to order the optimal allocations was obtained. The analy- sis demonstrates that in the optimal portfolio problem context, the dependency structure between the default risks is essential and cannot be ignored. DOI: 10.5353/th_b3131123 Subjects: Asset allocation - Mathematical models Markov processes

Hidden Markov Models in Finance

Since the groundbreaking research of Harry Markowitz into the application of operations research to the optimization of investment portfolios, finance has been one of the most important areas of application of operations research. The use of hidden Markov models (HMMs) has become one of the hottest areas of research for such applications to finance. This handbook offers systemic applications of different methodologies that have been used for decision making solutions to the financial problems of global markets. As the follow-up to the authors’ Hidden Markov Models in Finance (2007), this offers the latest research developments and applications of HMMs to finance and other related fields. Amongst the fields of quantitative finance and actuarial science that will be covered are: interest rate theory, fixed-income instruments, currency market, annuity and insurance policies with option-embedded features, investment strategies, commodity markets, energy, high-frequency trading, credit risk, numerical algorithms, financial econometrics and operational risk. Hidden Markov Models in Finance: Further Developments and Applications, Volume II presents recent applications and case studies in finance and showcases the formulation of emerging potential applications of new research over the book’s 11 chapters. This will benefit not only researchers in financial modeling, but also others in fields such as engineering, the physical sciences and social sciences. Ultimately the handbook should prove to be a valuable resource to dynamic researchers interested in taking full advantage of the power and versatility of HMMs in accurately and efficiently capturing many of the processes in the financial market.