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1
Mazibas, Murat. "Dynamic portfolio construction and portfolio risk measurement." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3297.
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The research presented in this thesis addresses different aspects of dynamic portfolio construction and portfolio risk measurement. It brings the research on dynamic portfolio optimization, replicating portfolio construction, dynamic portfolio risk measurement and volatility forecast together. The overall aim of this research is threefold. First, it is aimed to examine the portfolio construction and risk measurement performance of a broad set of volatility forecast and portfolio optimization model. Second, in an effort to improve their forecast accuracy and portfolio construction performance, it is aimed to propose new models or new formulations to the available models. Third, in order to enhance the replication performance of hedge fund returns, it is aimed to introduce a replication approach that has the potential to be used in numerous applications, in investment management. In order to achieve these aims, Chapter 2 addresses risk measurement in dynamic portfolio construction. In this chapter, further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation is provided by using monthly returns of hedge fund strategy indices for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH models provide some improvements in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing a better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have a substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, it is shown that these results are robust across the low - volatility and high-volatility sub-periods. Chapter 3 addresses optimization in dynamic portfolio construction. In this chapter, the advantages of introducing alternative optimization frameworks over the mean-variance framework in constructing hedge fund portfolios for a fund of funds. Using monthly return data of hedge fund strategy indices for the period 1990 to 2011, the standard mean-variance approach is compared with approaches based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investors. In order to estimate portfolio CVaR, CDaR and Omega, a semi-parametric approach is proposed, in which first the marginal density of each hedge fund index is modelled using extreme value theory and the joint density of hedge fund index returns is constructed using a copula-based approach. Then hedge fund returns from this joint density are simulated in order to compute CVaR, CDaR and Omega. The semi-parametric approach is compared with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. Two main findings are reported. The first is that CVaR-, CDaR- and Omega-based optimization offers a significant improvement in terms of risk-adjusted portfolio performance over mean-variance optimization. The second is that, for all three risk measures, semi-parametric estimation of the optimal portfolio offers a very significant improvement over non-parametric estimation. The results are robust to as the choice of target return and the estimation period. Chapter 4 searches for improvements in portfolio risk measurement by addressing volatility forecast. In this chapter, two new univariate Markov regime switching models based on intraday range are introduced. A regime switching conditional volatility model is combined with a robust measure of volatility based on intraday range, in a framework for volatility forecasting. This chapter proposes a one-factor and a two-factor model that combine useful properties of range, regime switching, nonlinear filtration, and GARCH frameworks. Any incremental improvement in the performance of volatility forecasting is searched for by employing regime switching in a conditional volatility setting with enhanced information content on true volatility. Weekly S&P500 index data for 1982-2010 is used. Models are evaluated by using a number of volatility proxies, which approximate true integrated volatility. Forecast performance of the proposed models is compared to renowned return-based and range-based models, namely EWMA of Riskmetrics, hybrid EWMA of Harris and Yilmaz (2009), GARCH of Bollerslev (1988), CARR of Chou (2005), FIGARCH of Baillie et al. (1996) and MRSGARCH of Klaassen (2002). It is found that the proposed models produce more accurate out of sample forecasts, contain more information about true volatility and exhibit similar or better performance when used for value at risk comparison. Chapter 5 searches for improvements in risk measurement for a better dynamic portfolio construction. This chapter proposes multivariate versions of one and two factor MRSACR models introduced in the fourth chapter. In these models, useful properties of regime switching models, nonlinear filtration and range-based estimator are combined with a multivariate setting, based on static and dynamic correlation estimates. In comparing the out-of-sample forecast performance of these models, eminent return and range-based volatility models are employed as benchmark models. A hedge fund portfolio construction is conducted in order to investigate the out-of-sample portfolio performance of the proposed models. Also, the out-of-sample performance of each model is tested by using a number of statistical tests. In particular, a broad range of statistical tests and loss functions are utilized in evaluating the forecast performance of the variance covariance matrix of each portfolio. It is found that, in terms statistical test results, proposed models offer significant improvements in forecasting true volatility process, and, in terms of risk and return criteria employed, proposed models perform better than benchmark models. Proposed models construct hedge fund portfolios with higher risk-adjusted returns, lower tail risks, offer superior risk-return tradeoffs and better active management ratios. However, in most cases these improvements come at the expense of higher portfolio turnover and rebalancing expenses. Chapter 6 addresses the dynamic portfolio construction for a better hedge fund return replication and proposes a new approach. In this chapter, a method for hedge fund replication is proposed that uses a factor-based model supplemented with a series of risk and return constraints that implicitly target all the moments of the hedge fund return distribution. The approach is used to replicate the monthly returns of ten broad hedge fund strategy indices, using long-only positions in ten equity, bond, foreign exchange, and commodity indices, all of which can be traded using liquid, investible instruments such as futures, options and exchange traded funds. In out-of-sample tests, proposed approach provides an improvement over the pure factor-based model, offering a closer match to both the return performance and risk characteristics of the hedge fund strategy indices.
2
Liao, Chien-Hui. "Essays on dynamic portfolio management." Thesis, University of Warwick, 2003. http://wrap.warwick.ac.uk/1254/.
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Over the last three decades, there has been an increasing interest in the problem of the investor's optimal consumption and portfolio rules. Despite the substantial amount of related literature, there remain many areas for further investigation. The thesis, therefore, addresses a number of important issues relating to the theory and practice of dynamic portfolio strategies. The thesis consists of five essays. The first two essays, Chapters 3 and 4, are concerned with efficient dynamic asset allocation programs under alternative market assumptions. Chapter 3 studies a situation where the simple time-invariant portfolio strategies are efficient and provides a complete characterisation of the strategies using the efficiency arguments. The popularised constant proportion portfolio insurance (CPPI) is embedded as a special case. Chapter 4 relaxes the assumption of a constant interest rate to allow the interest rate to follow a one factor stochastic process. The factor risk premium is then determined in a way that is consistent with the underlying equilibrium. These results are then applied to solve explicitly for an investor's optimal portfolio choice problem under the special case of a Vacisek short rate model and alternative utility functions. The third essay, Chapter 5, relaxes the assumption of a constant equity risk premium to allow the risk premium to vary through time. The evolution of the market risk premium in a representative agent equilibrium (consistent with the Black-Scholes option pricing) is investigated using a unified approach. The presence of dividends and intermediate consumption proves to be the key element that enables us to obtain a stationary economy with decreasing relative risk aversion, a theoretical result that has not be established in the existing literature. The last two essays. Chapters 6 and 7. are concerned with issues of portfolio efficiency and performance measurement. Chapter 6 uses the result from Chapter 5 that, without dividends and intermediate consumption, the market risk premium must satisfy the Burgers' equation, and applies Dybvig's payoff distribution pricing model to measure the inefficiency costs incurred when this condition is violated. The numerical results show that the degree of inefficiency is not very significant, at least for the cases which we postulate, but the findings also reassure negative result predicted from the model. Finally, Chapter 7 proposes a new utility based performance measure that can be applied in the ex-post evaluation of dynamic portfolio strategies. We construct a contingent claim estimation approach to estimate the nearest efficient strategy from a single realisation and then quantify the opportunity cost resulting from the departure of the observed strategy from the nearest efficient one. The numerical examples show that the technique is remarkably robust.
3
Wang, Jianshen. "Portfolio optimisation and dynamic trading." Thesis, University of Bristol, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702879.
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Gutkowska, Anna Barbara. "Essays on the dynamic portfolio choice." [Rotterdam] : Rotterdam : Erasmus Research Institute of Management (ERIM), Erasmus University Rotterdam ; Erasmus University [Host], 2006. http://hdl.handle.net/1765/7994.
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Catanas, Fernando Jorge de Lyz Girou Rodrigues. "Heuristics for the dynamic portfolio problem." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322226.
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Sbuelz, Alessandro. "Essays in derivatives pricing and dynamic portfolio." Thesis, London Business School (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313275.
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He, Hua. "Essays in dynamic portfolio optimization and diffusion estimations." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14136.
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Polat, Onur. "Dynamic Complex Hedging And Portfolio Optimization In Additive Markets." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610441/index.pdf.
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In this study, the geometric Additive market models are considered. In general, these market models are incomplete, that means: the perfect replication of derivatives, in the usual sense, is not possible. In this study, it is shown that the market can be completed by new artificial assets which are called &ldquopower-jump assets&rdquo
based on the power-jump processes of the underlying Additive process. Then, the hedging portfolio for claims whose payoff function depends on the prices of the stock and the power-jump assets at maturity is derived. In addition to the previous completion strategy, it is also shown that, using a static hedging formula, the market can also be completed by considering portfolios with a continuum of call options with different strikes and the same maturity. What is more, the portfolio optimization problem is considered in the enlarged market. The optimization problem consists of choosing an optimal portfolio in such a way that the largest expected utility of the terminal wealth is obtained. For particular choices of the equivalent martingale measure, it is shown that the optimal portfolio consists only of bonds and stocks.
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Horneff, Wolfram Johannes. "Dynamic portfolio choice with pension annuities and life insurance /." Frankfurt, 2008. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000253337.
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Karamanis, Dimitrios. "Stochastic dynamic programming methods for the portfolio selection problem." Thesis, London School of Economics and Political Science (University of London), 2013. http://etheses.lse.ac.uk/724/.
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In this thesis, we study the portfolio selection problem with multiple risky assets, linear transaction costs and a risk measure in a multi-period setting. In particular, we formulate the multi-period portfolio selection problem as a dynamic program and to solve it we construct approximate dynamic programming (ADP) algorithms, where we include Conditional-Value-at-Risk (CVaR) as a measure of risk, for different separable functional approximations of the value functions. We begin with the simple linear approximation which does not capture the nature of the portfolio selection problem since it ignores risk and leads to portfolios of only one asset. To improve it, we impose upper bound constraints on the holdings of the assets and we notice that we have more diversified portfolios. Then, we implement a piecewise linear approximation, for which we construct an update rule for the slopes of the approximate value functions that preserves concavity as well as the number of slopes. Unlike the simple linear approximation, in the piecewise linear approximation we notice that risk affects the composition of the selected portfolios. Further, unlike the linear approximation with upper bounds, here wealth flows naturally from one asset to another leading to diversified portfolios without us needing to impose any additional constraints on how much we can hold in each asset. For comparison, we consider existing portfolio selection methods, both myopic ones such as the equally weighted and a single-period portfolio models, and multi-period ones such as multistage stochastic programming. We perform extensive simulations using real-world equity data to evaluate the performance of all methods and compare all methods to a market Index. Computational results show that the piecewise linear ADP algorithm significantly outperforms the other methods as well as the market and runs in reasonable computational times. Comparative results of all methods are provided and some interesting conclusions are drawn especially when it comes to comparing the piecewise linear ADP algorithms with multistage stochastic programming.
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Bade, Alexander. "Bayesian portfolio optimization from a static and dynamic perspective /." Münster : Verl.-Haus Monsenstein und Vannerdat, 2009. http://d-nb.info/996985085/04.
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Wang, Alexander C. (Alexander Che-Wei). "Approximate value iteration approaches to constrained dynamic portfolio problems." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/30089.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 173-176).
This thesis considers a discrete-time, finite-horizon dynamic portfolio problem where an investor makes sequential investment decisions with the goal of maximizing expected terminal wealth. We allow non-standard utility functions and constraints upon the portfolio selections at each time. These problem formulations may be computationally difficult to address through traditional optimal control techniques due to the high dimensionality of the state space and control space. We consider suboptimal solution methods based on approximate value iteration. The primary innovation is the use of mean-variance portfolio selection methods. We present two case studies that employ these approximate value iteration methods. The first case study explores the effect of an insolvency constraint that prohibits further investing when an investor reaches non-positive wealth. When the investor has an exponential utility function, the insolvency constraint leads to more conservative investment policies when there are many investment periods remaining, except when wealth is very low. The second case study explores the effects of dollar position constraints that represent limited liquidity in certain investment strategies. When the investor has a CRRA utility function, we find that these constraints lead to non-myopic policies that are more conservative than the constrained myopic policy.
by Alexander C. Wang.
Ph.D.
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Gabih, Abdelali, Matthias Richter, and Ralf Wunderlich. "Dynamic optimal portfolios benchmarking the stock market." Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200501244.
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The paper investigates dynamic optimal portfolio strategies of utility maximizing portfolio managers in the presence of risk constraints. Especially we consider
the risk, that the terminal wealth of the portfolio falls short of a certain benchmark level which is proportional to the stock price. This risk is measured by the
Expected Utility Loss. We generalize the findings our previous papers to this case.
Using the Black-Scholes model of a complete financial market and applying martingale methods, analytic expressions for the optimal terminal wealth and the optimal
portfolio strategies are given. Numerical examples illustrate the analytic results.
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Hassan, G. N. A. "Multiobjective genetic programming for financial portfolio management in dynamic environments." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/20456/.
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Multiobjective (MO) optimisation is a useful technique for evolving portfolio optimisation solutions that span a range from high-return/high-risk to low-return/low-risk. The resulting Pareto front would approximate the risk/reward Efficient Frontier [Mar52], and simplifies the choice of investment model for a given client’s attitude to risk. However, the financial market is continuously changing and it is essential to ensure that MO solutions are capturing true relationships between financial factors and not merely over fitting the training data. Research on evolutionary algorithms in dynamic environments has been directed towards adapting the algorithm to improve its suitability for retraining whenever a change is detected. Little research focused on how to assess and quantify the success of multiobjective solutions in unseen environments. The multiobjective nature of the problem adds a unique feature to be satisfied to judge robustness of solutions. That is, in addition to examining whether solutions remain optimal in the new environment, we need to ensure that the solutions’ relative positions previously identified on the Pareto front are not altered. This thesis investigates the performance of Multiobjective Genetic Programming (MOGP) in the dynamic real world problem of portfolio optimisation. The thesis provides new definitions and statistical metrics based on phenotypic cluster analysis to quantify robustness of both the solutions and the Pareto front. Focusing on the critical period between an environment change and when retraining occurs, four techniques to improve the robustness of solutions are examined. Namely, the use of a validation data set; diversity preservation; a novel variation on mating restriction; and a combination of both diversity enhancement and mating restriction. In addition, preliminary investigation of using the robustness metrics to quantify the severity of change for optimum tracking in a dynamic portfolio optimisation problem is carried out. Results show that the techniques used offer statistically significant improvement on the solutions’ robustness, although not on all the robustness criteria simultaneously. Combining the mating restriction with diversity enhancement provided the best robustness results while also greatly enhancing the quality of solutions.
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Frey, Rüdiger, Abdelali Gabih, and Ralf Wunderlich. "Portfolio Optimization under Partial Information with Expert Opinions." World Scientific Publishing, 2012. http://epub.wu.ac.at/3844/1/Frey.pdf.
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This paper investigates optimal portfolio strategies in a market with partial information
on the drift. The drift is modelled as a function of a continuous-time Markov chain
with finitely many states which is not directly observable. Information on the drift is
obtained from the observation of stock prices. Moreover, expert opinions in the form
of signals at random discrete time points are included in the analysis. We derive the
filtering equation for the return process and incorporate the filter into the state variables
of the optimization problem. This problem is studied with dynamic programming
methods. In particular, we propose a policy improvement method to obtain computable
approximations of the optimal strategy. Numerical results are presented at the end. (author's abstract)
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Lennon, Marie Claire. "Intensity based modelling with dynamic correlation applied to portfolio credit risk." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613660.
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Illeditsch, Philipp Karl. "Essays in asset pricing and portfolio choice." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1508.
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Hamada, Mahmoud Actuarial Studies Australian School of Business UNSW. "Dynamic portfolio optimization & asset pricing : Martingale methods and probability distortion functions." Awarded by:University of New South Wales. School of Actuarial Studies, 2001. http://handle.unsw.edu.au/1959.4/18232.
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This dissertation consist of three contributions to financial and insurance mathematics. The first part considers numerical methods for dynamic portfolio optimisation in the expected utility model. The aim is to compare the risk-neutral computational approach (RNCA) also known as the martingale approach to stochastic dynamic programming (SDP) in a discrete-time setting. The main idea of the RNCA is to use the completeness and the arbitrage free properties of the market to compute the optimal consumption rules and then determine the trading strategy that finance this optimal consumption. In contrast, SDP solves for the optimal consumption and investment rules simultaneously using backward recursion and the principle of optimality. The setting that we consider is a discrete time and state space lattice. We provide some new theoretical results relating to the Hyperbolic Absolute Risk Aversion class of utility functions as well as propose a straightforward implementation of RNCA in binomial and trinomial lattices. Moreover, instead of discretizing the Hamilton-Jacobi-Bellman equation with possibly more than one state variable, we use symbolic algorithms to implement stochastic dynamic programming. This new approach provides a simpler numerical procedure for computing optimal consumption-investment policies. A comparison of the RNCA with SDP demonstrates the superiority of the RNCA in terms of computation. The second part considers the pricing of contingent claims using an approach developed and applied in applied in insurance. This approach utilize probability distortion functions as the dual of the utility functions used in financial theory. The main idea of the dual theory is to distort the subjective probabilities rather than outcomes to express the investor????????s risk aversion. In the first part, the RNCA for asset allocation uses the same principle as risk-neutral valuation for derivative pricing. The idea of the second part of this research is to show that the risk-neutral valuation can be recovered from the probability distortion function approach, thereby establishing consistency between the insurance and the financial approaches. We prove that pricing contingent claims under the real world probability measure using an appropriate distortion operator produces arbitrage-free prices when the underlying asset prices are log-normal. We investigate cases when the insurance-based approach fails to produce arbitrage-free prices and determine the appropriate distortion operator under more general assumptions than those used in Black-Scholes option pricing. In the third part we introduce dynamic portfolio optimisation with risk measures based on probability distortion function and provide a formal treatment of this class of risk measures. We employ the RNCA to study the consumption-investment problem in discrete time with preferences consistent with Yaari????????s dual (non-expected utility) theory of choice. As an application, we first consider risk measures based on the Proportional Hazard Transform that treats the upside and downside of the risk differently and secondly a risk measure based on the standard Normal cumulative distribution function. When the objective is to maximise a dual utility of wealth, and the underlying security returns are normal, the efficient frontier is found to be the same as in the mean-variance portfolio problem for an equivalent risk tolerance. When the objective is to maximise a dual utility of consumption, then ????????plunging???????????? behaviour occurs ( investing everything is the risky asset). Other properties of the optimal consumption-investment policies in the dual theory are also investigated and discussed.
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Mupambirei, Rodwel. "Dynamic and robust estimation of risk and return in modern portfolio theory." Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/4913.
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Includes abstract.Includes bibliographical references (leaves 134-138).
The portfolio selection method developed by Markowitz gives a rational investor a way of evaluating different investment options in a portfolio using the expected return and variance of the returns. Sharpe uses the same optimization approach but estimates the mean and covariance in a regression framework using the index models. Sharpe makes a crucial assumption that the residuals from different assets are uncorrelated and that the beta estimates are constant. When the Sharpe model parameters are estimated using ordinary least squares, the regression assumptions are violated when there is significant autocorrelation and heteroskedasticity in the residuals. Furthermore, the presence of outlying observations in the data leads to unreliable estimates when the ordinary least squares method is used. We find significant correlation in the residuals from different shares and thus we use the Troskie-Hossain model which relaxes this assumption and ultimately produces an efficient frontier that is almost identical to the Markowitz model. The combination of the GARCH and AR models to remove both autocorrelation and heteroskedasticity is used on the single index model and it causes the efficient frontier to shift significantly to the left. Using dynamic estimation through the Kalman filter, it is noticed that the beta coefficients are not constant and that the resulting efficient frontiers significantly outperform the Sharpe model. In order to deal with the problem of outlying observations in the data, we propose using the Minimum Covariance Determinant, (MCD) estimator as a robust version of the Markowitz formulation. Robust alternatives to the ordinary lea.st squares estimator are also investigated and they all cause the efficient frontier to shift to the left. Finally, to solve the problem of collinearity in the multiple index framework, we construct orthogonal indices using principal components regression to estimate the efficient frontier.
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Poomimars, Ponladesh. "The performance of dynamic covariance models in portfolio allocation, hedging and risk management." Thesis, University of Birmingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395728.
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Li, Yusong. "Stochastic maximum principle and dynamic convex duality in continuous-time constrained portfolio optimization." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/45536.
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This thesis seeks to gain further insight into the connection between stochastic optimal control and forward and backward stochastic differential equations and its applications in solving continuous-time constrained portfolio optimization problems. Three topics are studied in this thesis. In the first part of the thesis, we focus on stochastic maximum principle, which seeks to establish the connection between stochastic optimal control and backward stochastic differential differential equations coupled with static optimality condition on the Hamiltonian. We prove a weak neccessary and sufficient maximum principle for Markovian regime switching stochastic optimal control problems. Instead of insisting on the maxi- mum condition of the Hamiltonian, we show that 0 belongs to the sum of Clarkes generalized gradient of the Hamiltonian and Clarkes normal cone of the control constraint set at the optimal control. Under a joint concavity condition on the Hamiltonian and a convexity condition on the terminal objective function, the necessary condition becomes sufficient. We give four examples to demonstrate the weak stochastic maximum principle. In the second part of the thesis, we study a continuous-time stochastic linear quadratic control problem arising from mathematical finance. We model the asset dynamics with random market coefficients and portfolio strategies with convex constraints. Following the convex duality approach,we show that the necessary and sufficient optimality conditions for both the primal and dual problems can be written in terms of processes satisfying a system of FBSDEs together with other conditions. We characterise explicitly the optimal wealth and portfolio processes as functions of adjoint processes from the dual FBSDEs in a dynamic fashion and vice versa. We apply the results to solve quadratic risk minimization problems with cone-constraints and derive the explicit representations of solutions to the extended stochastic Riccati equations for such problems. In the final section of the thesis, we extend the previous result to utility maximization problems. After formulating the primal and dual problems, we construct the necessary and sufficient conditions for both the primal and dual problems in terms of FBSDEs plus additional conditions. Such formulation then allows us to explicitly characterize the primal optimal control as a function of the adjoint processes coming from the dual FBSDEs in a dynamic fashion and vice versa. Moreover, we also find that the optimal primal wealth process coincides with the optimal adjoint process of the dual problem and vice versa. Finally we solve three constrained utility maximization problems and contrasts the simplicity of the duality approach we propose with the technical complexity in solving the primal problem directly.
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Trägårdh, Andreas. "Additional Value in Project Portfolio Selection : Doing the right things by right valuation – Gains of real options portfolio theory." Thesis, Blekinge Tekniska Högskola, Sektionen för management, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12795.
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Purpose: The purpose of this thesis is to address the, by scholars and managers alike, expressed need of development in the project portfolio selection. The research will aim to investigate how the selection of innovation projects portfolios could change if flexibility, and with it uncertainty, were added to the project portfolio selection. The aim is further to investigate how options value can be incorporated as additional value to a portfolio selection decision, with the goal to choose projects that maximize the goal function of the firm. Method: This thesis takes a qualitative approach as such approach is favourable when studying social science. The empirical research is carried out at a large international company conducting in an extensive amount of R&D as well working with innovation projects. The data is collected by unstructured and semi structured interviews with management at the company subjected to the study. Results: The results show, that by adapting the real options framework to a static way of selecting projects, the incorporation of flexibility to the selection process can add economic value by accounting for options value and handle uncertainty. The real options framework will substantiate a dynamic approach to the selection process of innovation projects, as flexibility is changing the selection process from individual project selection to the selection of portfolios.Syfte: Syftet med följande uppsats är belysa och utveckla det, av forskare och chefer, uttryckta behov av utveckling av projektportföljval. Uppsatsen syftar till att undersöka hur valet av innovationsprojekt genom portföljvalsmodeller kan förändras om flexibilitet och osäkerhet adderas till beslutsprocessen. Syftet är vidare att undersöka hur ytterligare värde kan inkorporeras i ett beslut, med målet att välja den portfölj som maximerar företagets målfunktion. Metod: Denna uppsats tar en kvalitativ metodansats då ett sådant tillvägagångssätt är fördelaktigt i studier av samhällsvetenskap. Den empiriska undersökningen har bedrivits på ett stort internationellt företag vilket deltar i ett omfattande FoU arbete, samt i stor skala arbetar med innovationsprojekt. Data har samlats in genom ostrukturerade samt semistrukturerade intervjuer med ledningen på företaget. Slutsatser: Resultaten visar att genom att inkorporera reella optioner, i en statisk beslutsprocess, så kan ett bättre beslutsunderlag genereras genom inkluderandet av osäkerhet och värdet av optioner. Ett sådant beslutsunderlag genereras genom att real options adderar flexibilitet till urvalsprocessen. Genom att inkorporera flexibilitet kommer en statisk metod att välja individuella projekt på, skifta till fördel för en dynamisk metod att välja portföljer.
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Vieira, Joana Colarinha. "International portfolio diversification: evidence from emerging markets." reponame:Repositório Institucional do FGV, 2015. http://hdl.handle.net/10438/14114.
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Taking into account previous research we could assume to be beneficial to diversify investments in emerging economies. We investigate in the paper International Portfolio Diversification: evidence from Emerging Markets if it still holds true, given the assumption of larger world markets integration. Our results suggest a wide spread positive time-varying correlations of emerging and developed markets. However, pair-wise cross-country correlations gave evidence that emerging markets have low integration with developed markets. Consequently, we evaluate out-of-sample performance of a portfolio with emerging equity countries, confirming the initial statement that it has a better a risk-adjusted performance over a purely developed markets portfolio.
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MBITI, JOHN N. "Deep learning for portfolio optimization." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104567.
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In this thesis, an optimal investment problem is studied for an investor who can only invest in a financial market modelled by an Itô-Lévy process; with one risk free (bond) and one risky (stock) investment possibility. We present the dynamic programming method and the associated Hamilton-Jacobi-Bellman (HJB) equation to explicitly solve this problem. It is shown that with purification and simplification to the standard jump diffusion process, closed form solutions for the optimal investment strategy and for the value function are attainable. It is also shown that, an explicit solution can be obtained via a finite training of a neural network using Stochastic gradient descent (SGD) for a specific case.
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Karlsson, Viktor, and Emil Nygren. "Beating the Swedish Market : A dynamic approach to Value Investing using Modern Portfolio Theory." Thesis, Södertörns högskola, Institutionen för ekonomi och företagande, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-16465.
Full textAbstract:
Previous research has confirmed the existence of a value premium in a wide array of markets and using this value stock anomaly has yielded superior performance. This thesis investigates if one could take advantage of the existence of a value premium to deploy a dynamic investment strategy on the Swedish stock market (OMXS30) with focus on minimizing risk to achieve higher risk adjusted performance than the stock market index. The investment strategy implemented use Market-to-Book-Value to screen for both entry and exit signals and Modern Portfolio Theory, using the minimum-variance portfolio with short-selling constraints, to allocate assets within the portfolio. The investment strategy is evaluated using the Modigliani-Modigliani Risk Adjusted Performance measure. Conclusions from the thesis are that the strategy does outperform the Swedish stock market index, both in terms of nominal return and risk-adjusted performance. The suboptimal behaviour of investors where they overreact to signals and unconsciously rely on heuristics is used to explain why this is possible. Market-to-Book-Value, using the first quartile as entry signal and third quartile as exit signal, is considered to be a successful key ratio to screen for value stocks.
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Khoo, Wai Gea. "Dynamic-programming approaches to single-and multi-stage stochastic knapsack problems for portfolio optimization." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA362005.
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Box, John. "A dynamic structure for high dimensional covariance matrices and its application in portfolio allocation." Thesis, University of York, 2015. http://etheses.whiterose.ac.uk/10770/.
Full textAbstract:
Estimation of high dimensional covariance matrices is an interesting and important research topic. In this thesis, we propose a dynamic structure and develop an estimation procedure for high dimensional covariance matrices. Simulation studies are conducted to demonstrate its performance when the sample size is finite. By exploring a financial application, an empirical study shows that portfolio allocation based on dynamic high dimensional covariance matrices can significantly outperform the market from 1995 to 2014. Our proposed method also outperforms portfolio allocation based on the sample covariance matrix and the portfolio allocation proposed in Fan, Fan, Lv (2008).
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Meireles, Rodrigues Andrea Sofia. "Non-concave and behavioural optimal portfolio choice problems." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9694.
Full textAbstract:
Our aim is to examine the problem of optimal asset allocation for investors exhibiting a behaviour in the face of uncertainty which is not consistent with the usual axioms of Expected Utility Theory. This thesis is divided into two main parts. In the first one, comprising Chapter II, we consider an arbitrage-free discrete-time financial model and an investor whose risk preferences are represented by a possibly nonconcave utility function (defined on the non-negative half-line only). Under straightforward conditions, we establish the existence of an optimal portfolio. As for Chapter III, it consists of the study of the optimal investment problem within a continuous-time and (essentially) complete market framework, where asset prices are modelled by semi-martingales. We deal with an investor who behaves in accordance with Kahneman and Tversky's Cumulative Prospect Theory, and we begin by analysing the well-posedness of the optimisation problem. In the case where the investor's utility function is not bounded above, we derive necessary conditions for well-posedness, which are related only to the behaviour of the distortion functions near the origin and to that of the utility function as wealth becomes arbitrarily large (both positive and negative). Next, we focus on an investor whose utility is bounded above. The problem's wellposedness is trivial, and a necessary condition for the existence of an optimal trading strategy is obtained. This condition requires that the investor's probability distortion function on losses does not tend to zero faster than a given rate, which is determined by the utility function. Provided that certain additional assumptions are satisfied, we show that this condition is indeed the borderline for attainability, in the sense that, for slower convergence of the distortion function, there does exist an optimal portfolio. Finally, we turn to the case of an investor with a piecewise power-like utility function and with power-like distortion functions. Easily verifiable necessary conditions for wellposedness are found to be sufficient as well, and the existence of an optimal strategy is demonstrated.
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Ashant, Aidin, and Elisabeth Hakim. "Quantitative Portfolio Construction Using Stochastic Programming." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230243.
Full textAbstract:
In this study within quantitative portfolio optimization, stochastic programming is investigated as an investment decision tool. This research takes the direction of scenario based Mean-Absolute Deviation and is compared with the traditional Mean-Variance model and widely used Risk Parity portfolio. Furthermore, this thesis is done in collaboration with the First Swedish National Pension Fund, AP1, and the implemented multi-asset portfolios are thus tailored to match their investment style. The models are evaluated on two different fund management levels, in order to study if the portfolio performance benefits from a more restricted feasible domain. This research concludes that stochastic programming over the investigated time period is inferior to Risk Parity, but outperforms the Mean-Variance Model. The biggest aw of the model is its poor performance during periods of market stress. However, the model showed superior results during normal market conditions.I denna studie inom kvantitativ portföljoptimering undersöks stokastisk programmering som ett investeringsbeslutsverktyg. Denna studie tar riktningen för scenariobaserad Mean-Absolute Deviation och jämförs med den traditionella Mean-Variance-modellen samt den utbrett använda Risk Parity-portföljen. Avhandlingen görs i samarbete med Första AP-fonden, och de implementerade portföljerna, med era tillgångsslag, är därför skräddarsydda för att matcha deras investeringsstil. Modellerna utvärderas på två olika fondhanteringsnivåer för att studera om portföljens prestanda drar nytta av en mer restrektiv optimeringsmodell. Den här undersökningen visar att stokastisk programmering under undersökta tidsperioder presterar något sämre än Risk Parity, men överträffar Mean-Variance. Modellens största brist är dess prestanda under perioder av marknadsstress. Modellen visade dock något bättre resultat under normala marknadsförhållanden.
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Pecino, Rodriguez Jose Ignacio. "Portfolio of original compositions : dynamic audio composition via space and motion in virtual and augmented environments." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/portfolio-of-original-compositions-dynamic-audio-composition-via-space-and-motion-in-virtual-and-augmented-environments(637e9f5b-7d42-4214-92c4-70bac912cec2).html.
Full textAbstract:
Electroacoustic music is often regarded as not being sufficiently accessible to the general public because of its sound-based abstract quality and the complexity of its language. Live electronic music introduces the figure of the performer as a gestural bodily agent that re-enables our multimodal perception of sound and seems to alleviate the accessibility dilemma. However, live electronic music generally lacks the level of detail found in studio-based fixed media works, and it can hardly be transferred outside the concert hall situation (e.g. as a video recording) without losing most of its fresh, dynamic and unpredictable nature. Recent developments in 3D simulation environments and game audio technologies suggest that alternative approaches to music composition and distribution are possible, presenting an opportunity to address some of these issues. In particular, this Portfolio of Compositions proposes the use of real and virtual space as a new medium for the creation and organisation of sound events via computer-simulated audio-sources. In such a context, the role of the performer is sometimes assumed by the listener itself, through the operation of an interactive-adaptive system, or it is otherwise replaced by a set of automated but flexible procedures. Although all of these works are sonic centric in nature, they often present a visual component that reinforces the multimodal perception of meaningful musical structures, either as real space locations for sonic navigation (locative audio), or live visualisations of physically-informed gestural agents in 3D virtual environments. Consequently, this thesis draws on general game-audio concepts and terminology, such as procedural sound, non-linearity, and generative music; but it also embraces game development tools (game engines) as a new methodological and technological approach to electroacoustic music composition. In such context, space and the real-time generation, control, and manipulation of assets combine to play an important role in broadening the routes of musical expression and the accessibility of the musical language. The portfolio consists of six original compositions. Three of these works–Swirls, Alice - Elegy to the Memory of an Unfortunate Lady, and Alcazabilla–are interactive in nature and they required the creation of custom software solutions (e.g. SonicMaps) in order to deal with open-form musical structures. The last three pieces–Singularity, Apollonian Gasket, and Boids–are based on fractal or emergent behaviour models and algorithms, and they propose a non-interactive linear organisation of sound materials via real-time manipulation of non-conventional 3D virtual instruments. These original instrumental models exhibit strong spatial and kinematic qualities with an abstract and minimal visual representation, resulting in an extremely efficient way to build spatialisation patterns, texture, and musical gesture, while preserving the sonic-centric essence of the pieces.
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Avramidis, Stylianos. "Can we use cap rates to better allocate investments in commercial real estate in a dynamic portfolio?" Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62134.
Full textAbstract:
Thesis (S.M. in Real Estate Development)--Massachusetts Institute of Technology, Program in Real Estate Development in Conjunction with the Center for Real Estate , 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 67).
This thesis has a two-fold objective, namely to explore the role of cap rates in predicting the returns to commercial real estate, and to identify how cap rates can be used to improve the allocation of real estate in a dynamic investment portfolio. Seeking an answer to the first question, we run predictive regressions using data for real estate "All Properties" and for all four major property types, examining the predictability power of cap rates for a forecasting horizon from one to four quarters in the future. Moreover, we examine whether or not stock dividend-price ratio can predict real estate returns, and examine the predictability of stock returns by cap rates and dividend-price ratio. The analysis confirms that both cap rates and the dividend-price ratio can predict real estate "All Properties" returns for up to one year in the future. Concerning the analysis per property type, the results vary from property type to property type, and for different forecast horizons. Moreover, the analysis shows that stock returns can be predicted by the dividend-price ratio at all forecast horizons, whereas the cap rates seem to have somewhat limited predictive power regarding the stock returns. We approach the second question by following the dynamic portfolio allocation methodology proposed by Brandt and Santa-Clara (2006). We expand the existing set of "basis" assets comprised of stocks and real estate to include "conditional" portfolios, and then compute the portfolio weights of this expanded set of assets by applying the Markowitz solution to the optimization problem. We apply this methodology to three different portfolio rebalancing horizons. Moreover, we work with three cases for each portfolio, i.e. with the unconditional case, with the case where the dividend-price ratio is the only conditioning variable, and with the case where the cap rate is the second conditioning variable. In almost all instances the results confirm that, by adding the cap rate as an additional state variable, the performance of the portfolios increases significantly. The same conclusion stands when we impose a "no shorting" restriction to real estate, although now the role of cap rates seems somewhat less significant.
by Stylianos Avramidis.
S.M.in Real Estate Development
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Wang, Jo-Yu. "Portfolio based VaR model : a combination of extreme value theory (EVT) and dynamic conditional correlation (DCC) model." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/348328/.
Full textAbstract:
This thesis fills a gap in the risk management literature and expands the understanding of the portfolio value at risk (VaR) by providing a theoretical market risk measurement of a portfolio (called “GEV-DCC model”), which combines the tail dynamic conditional correlation (tail-DCC) and extreme value theory. According to the spirit of VaR, the tail distribution is more important than the entire distribution, as well as the correlation in the tail area between various assets. The main advantage of this approach is the increase of accuracy in the parameter estimation of the tail distribution and more consistent correlation measurement for VaR. The results from this method are compared with four other conventional VaR approaches; GARCH model, RiskMetrics, stochastic volatility, and historical simulation. Furthermore, three quality measures are applied to evaluate the suitability, conservativeness, and magnitude of loss of the forecasted VaR, which offer more information from the forecasted VaR pattern. Applying 16 major equity index returns from developed and emerging markets, this study finds that the GEV-DCC model offers a more accurate coverage across the blocks in the three hypothetical portfolios (the developed equity markets, Asian and Latin American equity markets) compared with the four competing models. The uncovered rates of the GEV-DCC model with the 5-day block approach are generally close to the given probability (α) set in the VaR calculation. These consistent results can also be found in the robustness test with the shorter forecasting period. In the quality checks, the GEV-DCC presents a relatively stable pattern in the daily and 10-day VaR results. In addition, the GEV-DCC model also provides satisfactory results in the conservativeness and potential loss tests although no direct evidence indicates that it delivers the best result in these two checks. We also find significant differences between the original DCC and the tail-DCC. This evidence shows that the correlations between equity markets in the left tail are significantly higher than the ones in the right tail, and there are significant changes (generally rising) in the tail-DCC patterns around the period of financial crisis in the third quarter of 2008. The results from this study could potentially provide a critical reference for investors in measuring or managing the market risk.
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Du, Plessis Richard Michael. "Comparative performances of capital protection strategies in the South African market." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15497.
Full textAbstract:
The performance of cash protection strategies implemented in the South African market are investigated in order to establish if investors are able to add value through the use of dynamic portfolio insurance methods. The analysis is performed, using monthly data, from January 1961 to August 2014 using six alternative methodologies including both a Fixed Rate and Rolling Average Stop-Loss approach, a Lock-In approach, a Constant Mix strategy, a Constant Proportion Portfolio Insurance ("CPPI") approach and an alternative CPPI approach using a Ratchet mechanism. The results indicate that the use of such cash protection strategies can markedly improve portfolio performance from a risk return perspective compared to a pure diversified investment strategy. Notably, the use of older, simpler trading strategies such as the Stop-Loss and Lock-In approaches at optimum threshold levels can still offer investors higher risk to reward benefits with less commitment required. These strategies, though, lack the flexibility observed with the more recently developed dynamic trading strategies in terms of providing for varying risk appetites.
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Dondi, Gabriel Arnon. "Models and dynamic optimisation for the asset and liability management of pension funds." Zürich : Measurement and Control Laboratory, ETH Zentrum ML, 2005. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=16257&part=abstracts.
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Maximchuk, Oleg, and Yury Volkov. "Provisions estimation for portfolio of CDO in Gaussian financial environment." Thesis, Högskolan i Halmstad, Tillämpad matematik och fysik (MPE-lab), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16508.
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The problem of managing the portfolio provisions is of very high importance for any financial institution. In this paper we provide both static and dynamic models of provisions estimation for the case when the decision about provisions is made at the first moment of time subject to the absence of information and for the case of complete and incomplete information. Also the hedging strategy for the case of the defaultable market is presented in this work as another tool of reducing the risk of default. The default time is modelled as a first-passage time of a standard Brownian motion through a deterministic barrier. Some methods of numerical provision estimation are also presented.
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Valian, Haleh. "Optimizing dynamic portfolio selection." 2009. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051917.
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"Dynamic options portfolio selection." 2003. http://library.cuhk.edu.hk/record=b5891531.
Full textAbstract:
Zhou Xiaozhou.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (leaves 58-59).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Overview --- p.1
Chapter 1.2 --- Organization Outline --- p.4
Chapter 2 --- Literature Review --- p.5
Chapter 2.1 --- Option --- p.5
Chapter 2.1.1 --- The definition of option --- p.5
Chapter 2.1.2 --- Payoff of Options --- p.6
Chapter 2.1.3 --- Black-Scholes Option Pricing Model --- p.7
Chapter 2.1.4 --- Binomial Model --- p.12
Chapter 2.2 --- Portfolio Theory --- p.15
Chapter 2.2.1 --- The Markowitz Mean-Variance Model --- p.15
Chapter 2.2.2 --- Multi-period Mean-Variance Formulation --- p.17
Chapter 3 --- Multi-Period Options Portfolio Selection Model with Guaran- teed Return --- p.20
Chapter 3.1 --- Problem Formulation --- p.20
Chapter 3.2 --- Solution Algorithm Using Dynamic Programming --- p.25
Chapter 3.3 --- Numerical Example --- p.27
Chapter 4 --- Mean-Variance Formulation of Options Portfolio --- p.36
Chapter 4.1 --- The Problem Formulation --- p.36
Chapter 4.2 --- Solution Algorithm Using Dynamic Programming --- p.39
Chapter 4.3 --- Numerical Example --- p.41
Chapter 5 --- Summary --- p.56
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Godin, Vincent. "Dynamic portfolio optimization across asset classes." Thesis, 2007. http://spectrum.library.concordia.ca/975758/1/MR40979.pdf.
Full textAbstract:
This study provides evidence that a dynamic portfolio strategy, grounded on an asymmetric GARCH model and applied to investments in equities, real estate, and commodities, outperforms static strategies in terms of wealth, Sharpe ratios, and expected utility even when short selling restrictions are imposed on real estate and equities. For small investors, the benefits are subsumed by transactions costs; for large investors, the dynamic strategy remains marginally feasible.
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Hsu, Ei-Lin, and 徐艾苓. "A Framework for Dynamic Project Portfolio Management." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/33209383057431242154.
Full textAbstract:
碩士國立交通大學
工業工程與管理系所
95
NPD Project portfolio management is important for a firm’s resource allocation. Its’ role is to lead the firm in spending capital and human resource on the right projects. In today’s rapidly changing environments, effective NPD project portfolio should be able to adapt to critical changes. Therefore, in this study we propose a dynamic NPD project portfolio framework that somehow complements the periodical portfolio review meeting in practice. In the periodical portfolio meeting that takes place two to hour times annually, top management is gathered for review, evaluate, and redirect the project portfolio on a strategic viewpoint. Periodical review meetings are not suitable as a tool for real-time adaptation of portfolio due to its original goal and high costs, which rise from the need of huge amount of information and time devoted by top management. Thus, the proposed dynamic portfolio is expected to complement the periodical meeting in facing the changing environments, with lower cost and real-time change detection. This is done through the identified critical change factors that can, if occurs, initiate the evaluation and adjustment actions, and a systematic evaluation procedure that helps to evaluate and identify where adjustments are needed, with a minimum amount of information and management devotion. A subsidiary part of this study is a project evaluation approach that supports the main objective. It takes into concern especially the uncertainty nature of R&D activates, the complex interactions among projects, and the possibility to make control decisions during project development. The two parts together may contribute in an active real-time portfolio management style that leads the firm to do the right projects at the right time.
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Huang, Yu-hsiang, and 黃俞翔. "Dynamic Portfolio Management with Trading Signal Prediction." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/01955732599001869345.
Full textAbstract:
碩士國立中央大學
資訊工程學系
101
The goal of portfolio management is to allocate the limited money into multiple securities effectively to earn more money. The two key factors of obtaining high profit are “the trading time” and “asset allocation”. Most of the past researches focus on one specific problem, for example, trading signal prediction, asset allocation, adaptive investment goal setting etc. But in real investment, investors need solutions for all these problems to achieve investment goal. This research combines trading signal prediction [9], Time Invariant Portfolio Protection [10], Optimal Dynamic Asset Allocation [20] into a portfolio management system. In the first part, we use turning points to partition the stock price, and use Back-propagation neural network (BPNN) to learn and predict the trading signal for each stock. We propose a new way to calculate the trading signal to avoid parameter tuning required in [9]. The second part is asset allocation. With asset protection mechanism (TIPP), we set an explicit ROI as the investment goal. We then allocate money for investment target for their probability to reach the investment goal in each rebalance. The experiment shows that the new way to calculate trading signal has similar performance with the original method but avoids the parameter tuning problem. Furthermore, with asset protection mechanism, our portfolio management system would receive less damage in encountering bear market. However, the fixed investment return goal would limit the profit in bull market. Therefore, we start a new round when the portfolio reaches the investment goal, and successfully makes the portfolio management system conquer the problem in bull market. For long-term investment, this mechanism could get better performance by setting appropriate return rate.
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"Dynamic portfolio selection for asset-liability management." Thesis, 2007. http://library.cuhk.edu.hk/record=b6074430.
Full textAbstract:
Mean-variance criterion in optimization AL problem aims at maximizing the final surplus; asset value minus liability value, subject to a given variance of the final surplus or, equivalently, minimizing the variance of the final surplus subject to a given expected final surplus. The stochastic optimal control theory is employed to analytically solve the AL management problem in continuous-time setting. Then the comparison of derived optimal AL management policy and the literatures are examined and the discrepancy in objectives between equity holders and investors of a mutual fund is discussed finally.Portfolio selection in asset-liability (AL) management is to seek the best allocation of wealth among a basket of securities with taking into account the liabilities. There are a lot of portfolio selection criteria among in the literature. The two of them are mean-variance criterion and Roy's safety-first principle. This thesis investigates the optimal asset allocation for an investor who is facing an uncontrollable liability under either one of these two portfolio constructions. The relation between these two different principles are discussed in the context of AL management.
Roy's safety-first principle (Roy, 1956) asserts that the investor would specify a threshold level of the final surplus below which the outcome is regarded as disaster. The objective is then to minimize the ruin probability or the chance of disaster subject to a constraint that the expected final surplus is higher than the threshold. Roy however solves this problem by minimizing an upper bound of the ruin probability based on the Bienayme-Chebycheff inequality. With the same consideration of Roy, the analytical trading strategy of the safety-first. AL management, problem, in the sense of surplus, under both continuous- and multi-period-time settings are derived. We link this surrogated safety-first principle to the mean-variance ones.
The final objective of this thesis attacks the genuine safety-first AL problem. Without replacing the ruin probability in the objective function by its upper bound, we use a martingale approach and consider the funding ratio which is the total wealth divided by the total liability. Two important situations in the literature are investigated. In the first situation, the mean constraint of the original problem is removed, We show that removing the mean constraint makes the problem become a target reaching problem that can be solved analytically. However, the essence of safety-first is lost. In the second case in which the mean constraint is there, the problem becomes ill-posed and is then solved using an approximation using a martingale approach. The approximation relies on the assumption that the investor gives up unreasonably high profits and sets an upper bounded for the final funding ratio.
Chiu, Mei Choi.
"July 2007."
Adviser: Duan Li.
Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1304.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2007.
Includes bibliographical references (p. 121-126).
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.
Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract in English and Chinese.
School code: 1307.
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Lee, Wan-Rou, and 李宛柔. "A Dynamic Rebalancing Strategy for Portfolio Allocation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/794uhm.
Full textAbstract:
碩士國立中央大學
統計研究所
105
Reallocation, or adjust weights of portfolio is an indispensable part in portfolio management. In the practice, calendar rebalancing is a basic rebalancing strategy that either retail or institutional investors can utilize to create an optimal investment process. In calendar rebalancing, portfolio managers reallocate their portfolio at predefined intervals and use the historical data over the pass fixed time to calculate the suitable weights. It's known that each time you rebalance the portfolio, paying for the tax and transaction fee is inevitable.However, reallocating the portfolio does not always get the relevant return. In this study, we focus on examining the necessity of rebalancing before the regular reallocation by using changepoint detection under a product partition model. We propose a dynamic rebalancing with optimal training period (DRO) to improve the calendar rebalancing. We examine the efficiency of our rebalancing strategy by using backtesting procedure and compare with the calendar rebalancing. As a result, we discover that the DRO strategy has greater reward in terms of compound annual growth rate when the rolling window is longer. Besides, the representation of the DRO strategy is better than the calendar rebalancing in general when the economic situation is steady.
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Chao, Yi-Fan, and 趙宜凡. "Dynamic Portfolio Selection Based on Value Function." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/56985584104432129286.
Full textAbstract:
碩士國立彰化師範大學
財務金融技術學系
104
There are many economic phenomenon and investors’ behaviors can’t be fully explained by Expected Utility Theory. Until the Prospect Theory was published, those abnormal behaviors were explained successfully. Empirical results show that the Prospect Theory describes investors’ behavior more appropriately. This study assumes that investors’ behavior conform the Prospect Theory, and modeling investors’ dynamic asset allocation is based on value function. This study is discussing about how investors making the asset allocation during investing period appropriately, obtaining the amount of change in the expected utility (value) of their wealth maximized. The result shows that optimal portfolio based on value function compared with market price weighted portfolio (ETF50), has significant higher return.
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"Dynamic portfolio analysis: mean-variance formulation and iterative parametric dynamic programming." 1998. http://library.cuhk.edu.hk/record=b5889737.
Full textAbstract:
by Wan-Lung Ng.Thesis submitted in: November 1997.
On added t.p.: January 19, 1998.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaves 114-119).
Abstract also in Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Overview --- p.1
Chapter 1.2 --- Organization Outline --- p.5
Chapter 2 --- Literature Review --- p.7
Chapter 2.1 --- Modern Portfolio Theory --- p.7
Chapter 2.1.1 --- Mean-Variance Model --- p.9
Chapter 2.1.2 --- Setting-up the relationship between the portfolio and its component securities --- p.11
Chapter 2.1.3 --- Identifying the efficient frontier --- p.12
Chapter 2.1.4 --- Selecting the best compromised portfolio --- p.13
Chapter 2.2 --- Stochastic Optimal Control --- p.17
Chapter 2.2.1 --- Dynamic Programming --- p.18
Chapter 2.2.2 --- Dynamic Programming Decomposition --- p.21
Chapter 3 --- Multiple Period Portfolio Analysis --- p.23
Chapter 3.1 --- Maximization of Multi-period Consumptions --- p.24
Chapter 3.2 --- Maximization of Utility of Terminal Wealth --- p.29
Chapter 3.3 --- Maximization of Expected Average Compounded Return --- p.33
Chapter 3.4 --- Minimization of Time to Reach Target --- p.35
Chapter 3.5 --- Goal-Seeking Investment Model --- p.37
Chapter 4 --- Multi-period Mean-Variance Analysis with a Riskless Asset --- p.40
Chapter 4.1 --- Motivation --- p.40
Chapter 4.2 --- Dynamic Mean-Variance Analysis Formulation --- p.43
Chapter 4.3 --- Auxiliary Problem Formulation --- p.45
Chapter 4.4 --- Efficient Frontier in Multi-period Portfolio Selection --- p.53
Chapter 4.5 --- Obseravtions --- p.58
Chapter 4.6 --- Solution Algorithm for Problem E (w) --- p.62
Chapter 4.7 --- Illstrative Examples --- p.63
Chapter 4.8 --- Verification with Single-period Efficient Frontier --- p.72
Chapter 4.9 --- Generalization to Cases with Nonlinear Utility Function of E (xT) and Var (xT) --- p.75
Chapter 5 --- Dynamic Portfolio Selection without Risk-less Assets --- p.84
Chapter 5.1 --- Construction of Auxiliuary Problem --- p.88
Chapter 5.2 --- Analytical Solution for Efficient Frontier --- p.89
Chapter 5.3 --- Reduction to Investment Situations with One Risk-free Asset --- p.101
Chapter 5.4 --- "Multi-period Portfolio Selection via Maximizing Utility function U(E {xT),Var (xT))" --- p.103
Chapter 6 --- Conclusions and Recommendations --- p.108
Chapter 6.1 --- Summaries and Achievements --- p.108
Chapter 6.2 --- Future Studies --- p.110
Chapter 6.2.1 --- Constrained Investment Situations --- p.110
Chapter 6.2.2 --- Including Higher Moments --- p.111
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Tzeng, Yu-Ying, and 曾毓英. "Dynamic Portfolio Selection incorporating Inflation Risk Learning Adjustments." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/71225577638371747301.
Full textAbstract:
碩士國立政治大學
風險管理與保險研究所
97
This study examines the optimal portfolio selection incorporating inflation risk learning adjustments for a long-term investor. For long-term investors, it is inevitable to face the uncertainty of inflation. On the other hand, quantifying inflation risk needs more effort since the government announced the information on Consumer Price Index (CPI) rather than the real inflation rates.
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Lee, Chin-Lung, and 李金龍. "Dynamic Portfolio Selection with Predicted Return and Risk." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/24047487918069406370.
Full textAbstract:
碩士國立暨南國際大學
資訊管理學系
96
This study proposed two forecasting models, which are Fuzzy GP/SC and Fuzzy Piecewise MOGP/SC. Fuzzy GP/SC is used to deal with crisp observations, and can be applied in small observations and provide decision makers the best-possible and worst-possible situations. The experiments found that Fuzzy GP/SC is better than Fuzzy ARIMA (Tseng et al., 2001) with minimizing Mean Absolute Deviations (MAD). Fuzzy Piecewise MOGP/SC is used to deal with fuzzy observations, and can be applied with small observations and get smaller MAD than Fuzzy ARIMA does because it can solves problem of outliers instead of deleting all the outliers. This study used predicted return instead of arithmetic mean for Multiple Criteria Decision Making (MCDM) to conduct portfolio selection. GARCH model was used to calculate the risk for standard deviations. Moreover, there are two forecasting models used to forecast predicted return, which are GP/SC model and ARIMA model. MCDM is consisted of four criteria, which are predicted return, predicted risk, β value, and skewness. The experiments found that the GPSC-GARCH model outperformed the MVBS (Cho, 2007), GPSC-STD, and ARMA-GARCH.
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Liu, Chia-Chen, and 劉佳誠. "Dynamic Portfolio Hedging under Asymmetric and Basis Effects." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/33786594501666918494.
Full textAbstract:
碩士國立暨南國際大學
財務金融學系
96
This paper investigates the portfolio effect and the dynamic effect of portfolio hedging effectiveness. BEKK-GARCH (Baba-Engle-Kraft-Kroner) is used to model the dynamic covariance structure to calculate the minimum variance hedge ratios. The effects of asymmetries and basis are also investigated. Six metal commodities traded in the London Metal Exchange are used. Results show that portfolio hedging is superior to separate hedging for all cases. The asymmetry effect can’t increase hedging effectiveness. After adding the basis effect, hedging effectiveness is improved obviously.
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LU, KENG-FU, and 盧畊甫. "Dynamic Customized Portfolio Selection－Multi-objective Stochastic Programming." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/68hse9.
Full textAbstract:
碩士東吳大學
資訊管理學系
105
In an era of inflation and salary freeze, many people invest in stocks to increase their incomes. But stocks are very risky commodities. How can we reduce the risk of investment? How to make long-term steady profit? It has always been a question for investors to think about. The purpose of this study is to establish a dynamic customized portfolio strategy that can effectively keep profit and reduce risk. This strategy will be based on investor preferences, recommend suitable portfolio for the investor, and according to market changes, dynamically adjust the portfolio of investment targets and weight structure, bring the portfolio to maintain low risk and high yield. This study using January 2013 to December 2016 of the Taiwan 100 stocks as investment targets, users fill in customized portfolio questionnaire, calculated weights by the analytic network process (long-term returns, short-term returns, yield rate, long-term risk, short-term risk, beta value), and then calculate the total score of Taiwan 100 with the highest scores top 10 as the user of the most suitable portfolio, then use multi-objective stochastic programming to calculate the proportion of each stock investment, then use constant proportions portfolio insurance strategy(CPPI) to adjust the portfolio during the Back-Testing, every six months to dynamically adjust the portfolio, during the period, 8 times. Finally, the performance results are compared with the Taiwan 100.The empirical results show that each questionnaire has at least 4 times, the Treynor index exceeds the Taiwan 100, and the Jensen index is the same; in addition, the average of Treynor index and the average of Jensen index of each questionnaire both exceeded the Taiwan 100, this study uses the CPPI strategy to dynamically adjust the portfolio, and has better investment performance in the long-term investment than the Taiwan 100.
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"Optimal dynamic portfolio selection under downside risk measure." 2014. http://library.cuhk.edu.hk/record=b6116127.
Full textAbstract:
传统的风险控制以终端财富的各阶中心矩作为风险度量，而现在越来越多的投资模型转向以不对称的在某个特定临界值的下行风险作为风险度量。在现有的下行风险中，安全第一准则，风险价值，条件风险价值，下偏矩可能是最有活力的代表。在这篇博士论文中，在已有的静态文献之上，我们讨论了以安全第一准则，风险价值，条件风险价值，下偏矩为风险度量的一系列动态投资组合问题。我们的贡献在于两个方面，一个是建立了可以被解析求解的模型，另一个是得到了最优的投资策略。在终端财富上加上一个上界，使得我们克服了一类下行风险投资组合问题的不适定性。引入的上界不仅仅使得我们的下行风险下的投资组合问题能得到显式解，而且也让我们可以控制下行风险投资组合问题的最优投资的冒险性。用分位数法和鞅方法，我们能够得到上述的各种模型的解析解。在一定的市场条件下，我们得到了对应的拉格朗日问题的乘子的存在性和唯一性, 这也是对应的鞅方法中的核心步骤。更进一步，当市场投资组合集是确定性的时候，我们推出解析的最优财富过程和最优投资策略。Instead of controlling "symmetric" risks measured by central moments of terminal wealth, more and more portfolio models have shifted their focus to manage "asymmetric" downside risks that the investment return is below certain threshold. Among the existing downside risk measures, the safety-first principle, the value-at-risk (VaR), the conditional value-at-risk (CVaR) and the lower-partial moments (LPM) are probably the most promising representatives.
In this dissertation, we investigate a general class of dynamic mean-downside risk portfolio selection formulations, including the mean-exceeding probability portfolio selection formulation, the dynamic mean-VaR portfolio selection formulation, the dynamic mean-LPM portfolio selection formulation and the dynamic mean-CVaR portfolio selection formulation in continuous-time, while the current literature has only witnessed their static versions. Our contributions are two-fold, in both building up tractable formulations and deriving corresponding optimal policies. By imposing a limit funding level on the terminal wealth, we conquer the ill-posedness exhibited in the class of mean-downside risk portfolio models. The limit funding level not only enables us to solve dynamic mean-downside risk portfolio optimization problems, but also offers a flexibility to tame the aggressiveness of the portfolio policies generated from the mean-downside risk optimization models. Using quantile method and martingale approach, we derive optimal solutions for all the above mentioned mean-downside risk models. More specifically, for a general market setting, we prove the existence and uniqueness of the Lagrangian multiplies, which is a key step in applying the martingale approach, and establish a theoretical foundation for developing efficient numerical solution approaches. Furthermore, for situations where the opportunity set of the market setting is deterministic, we derive analytical portfolio policies.
Detailed summary in vernacular field only.
Zhou, Ke.
Thesis (Ph.D.) Chinese University of Hong Kong, 2014.
Includes bibliographical references (leaves i-vi).
Abstracts also in Chinese.
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Huang, Hao-Ting, and 黃浩庭. "Portfolio Optimization under Dynamic Conditional Value-at-Risk." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/45177012458411049643.
Full textAbstract:
碩士國立交通大學
經營管理研究所
102
In modern portfolio theory (MPT), investors use minimum portfolio variance strategy to allocate their assets and optimize their portfolios, but MPT assumes portfolio variance never changes and uses the historical parameter “volatility” as a proxy for risk. We use range-based dynamic conditional correlation (DCC) and choose the coherent risk measure, Conditional Value-at-Risk (CVaR), as a portfolio risk management tool. We collected Standard &; Poor’s 500 Composite Index (S&;P 500) futures, 10-year U.S. Treasury bond (10-year T-bond) futures as our sample data. In our empirical study, we found that range-based DCC performance is superior to another two models, which are used as model comparison, in in-sample and out-of-sample comparison, and it can help investors construct optimal portfolio with profitable expected return and manageable portfolio risk. The empirical results support our main idea that we can develop promising dynamic investment strategies by using a range-based DCC model in portfolio optimization of conditional value-at-risk framework.