The theme of this thesis relates to solving the optimal portfolio selection problems using linear programming. There are two key contributions in this thesis. The first contribution is to generalize the well-known linear optimization framework of Conditional Value-at-Risk (CVaR)-based portfolio selection problems (see Rockafellar and Uryasev (2000, 2002)) to more general risk measure portfolio selection problems. In particular, the class of risk measure under consideration is called the Coherent Distortion Risk Measure (CDRM) and is the intersection of two well-known classes of risk measures in the literature: the Coherent Risk Measure (CRM) and the Distortion Risk Measure (DRM). In addition to CVaR, other risk measures which belong to CDRM include the Wang Transform (WT) measure, Proportional Hazard (PH) transform measure, and lookback (LB) distortion measure. Our generalization implies that the portfolio selection problems can be solved very efficiently using the linear programming approach and over a much wider class of risk measures. The second contribution of the thesis is to establish the equivalences among four formulations of CDRM optimization problems: the return maximization subject to CDRM constraint, the CDRM minimization subject to return constraint, the return-CDRM utility maximization, the CDRM-based Sharpe Ratio maximization. Equivalences among these four formulations are established in a sense that they produce the same efficient frontier when varying the parameters in their corresponding problems. We point out that the first three formulations have already been investigated in Krokhmal et al. (2002) with milder assumptions on risk measures (convex functional of portfolio weights). Here we apply their results to CDRM and establish the fourth equivalence. For every one of these formulations, the relationship between its given parameter and the implied parameters for the other three formulations is explored. Such equivalences and relationships can help verifying consistencies (or inconsistencies) for risk management with different objectives and constraints. They are also helpful for uncovering the implied information of a decision making process or of a given investment market. We conclude the thesis by conducting two case studies to illustrate the methodologies and implementations of our linear optimization approach, to verify the equivalences among four different problem formulations, and to investigate the properties of different members of CDRM. In addition, the efficiency (or inefficiency) of the so-called 1/n portfolio strategy in terms of the trade off between portfolio return and portfolio CDRM. The properties of optimal portfolios and their returns with respect to different CDRM minimization problems are compared through their numerical results.

[Truncated abstract] This thesis consists of six chapters, and the main research contributions are contained in chapters two through five inclusive. The topics addressed in each chapter are distinct, but related, and the specific contributions to knowledge made by the different chapters are related to: (i) understanding more fully the nature of the demand for alcohol; (ii) explaining the relationship between reputation characteristics and consumers’ willingness to pay for wine; (iii) estimating the rate of return to Australian wine; and (iv) using financial analysis to reveal the risk diversification benefits available by including wine in an investment portfolio. The details of each contribution are briefly outlined below. Chapter 2 discusses the nature of the demand for alcohol. The demand for alcoholic beverages is an area much studied, and there are numerous studies estimating the own-price elasticity of alcoholic beverages. A review of relevant published studies indicates reported: beer own-price elasticity estimates range from -.02 to -3.00, with a mean estimate value of -.46, and standard deviation of -.41 (n = 139); wine own-price elasticity estimates range from -.05 to -3.00, with a mean estimate value of -.72, and standard deviation of .53 (n = 140); and spirits own-price elasticity estimates range from -.01 to -2.18, with a mean estimate value of -.74, and standard deviation of .47 (n = 136). Chapter 2 contributes to understanding the demand for alcohol, not by adding yet another set of elasticity estimates to an already substantial literature, but by providing a framework through which all known own-price elasticity estimates can be understood. Specifically, a meta-regression framework is employed to study previously published own-price elasticity estimates. This framework allows the effect of model design attributes to be isolated, and the underlying trend in consumer responses to price changes to be identified.

Zugl.: Regensburg, Universiẗat, Diss.

Univ., Diss.--Mainz, 2003. / Literaturverz. S. [165] - 170.

Zugl.: Bremen, Universiẗat, Diss., 2009.

[pt] Esta dissertação investiga um sistema de apoio à decisão baseado em Algoritmos Genéticos e Simulação Monte Carlo para a formação de carteiras de projetos de petróleo e gás. O objetivo do trabalho é avaliar o desempenho de Algoritmos Genéticos -AG- para selecionar projetos que formarão a carteira. A construção de carteiras de projetos é um problema de múltiplos objetivos, onde se deseja escolher um conjunto de projetos com perspectivas de lucro para formar uma carteira. O sistema emprega o Algoritmo Genético para formação de carteiras de projetos. Em seguida, a Simulação de Monte Carlo é utilizada para obter a função de distribuição do Valor Presente Líquido -VPL- da carteira baseado nas distribuições dos projetos escolhidos. Por último, avalia-se a carteira usando-se o método de minimização de energia que busca o equilibro dos três objetivos considerados. O problema consiste, basicamente, em maximizar a média do VPL, que representa o retorno esperado, minimizando-se o Desvio Padrão, que é a medida de risco, e maximizando-se o Percentil 90 -P90-, que significa a possibilidade de obter um maior lucro. Nos estudos de casos são apresentados os resultados da aplicação do sistema para diferentes grupos de projetos, constituídos por 16, 18, 20 e 26 projetos, onde cada um deles tem distribuições teóricas do VPL definidas por funções: F, Normal e Logarítmica, formadas por 500 dados. Os resultados obtidos mostram a eficiência do AG com a técnica de múltiplos objetivos, na utilização para a otimização de carteiras de projetos de investimento em petróleo e gás. / [en] This thesis investigates a system of support to the decision based on Genetic Algorithms and Monte Carlo Simulation for the creation of portfolio projects of oil and gas. The objective of this work is to evaluate the performance of Genetic Algorithms -GA- to select projects that will form the portfolio. The portfolio construction of projects is a problem of objective multiples, where it is wishes to choose a set of projects with profit perspectives to form a portfolio. The system uses the Genetic Algorithm to create the portfolio formation of projects. After that, the Monte Carlo Simulation is used to get the function of distribution of the Net Present Value -NPV- of the portfolio based on the distributions of the chosen projects. Finally, the portfolio is evaluated portfolio by using itself the method of minimizes energy for the three considered objectives. The problem consists, basically, in maximizing the average of the NPV which represents the return expected, minimizing the Standard of Deviation, which is the measure of the risk, and maximizing the Percentile 90 -P90-, which means the possibility to get a bigger profit. In the study of cases, it is presented the results of the application of the system for different groups of projects, consisting in 16, 18, 20 and 26 projects, where each project has theoretical distributions of the NPV defined by functions: F, Normal and Logarithmic, formed for 500 data. The gotten results show the efficiency of the GA with the technique of objective multiples, in the use of the optimization of the portfolio projects oil and gas investment.

Submitted by Cristina Tessari (tinatessari@gmail.com) on 2016-06-09T13:51:42Z No. of bitstreams: 1 DissertationEPGE_CristinaTessari2016.pdf: 1264081 bytes, checksum: 14e65157457bfe8deea5353bb192a0af (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2016-06-29T14:03:25Z (GMT) No. of bitstreams: 1 DissertationEPGE_CristinaTessari2016.pdf: 1264081 bytes, checksum: 14e65157457bfe8deea5353bb192a0af (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2016-06-29T14:06:59Z (GMT) No. of bitstreams: 1 DissertationEPGE_CristinaTessari2016.pdf: 1264081 bytes, checksum: 14e65157457bfe8deea5353bb192a0af (MD5) / Made available in DSpace on 2016-06-29T14:07:23Z (GMT). No. of bitstreams: 1 DissertationEPGE_CristinaTessari2016.pdf: 1264081 bytes, checksum: 14e65157457bfe8deea5353bb192a0af (MD5) Previous issue date: 2016-03-22 / We use Brazilian data to compute monthly idiosyncratic moments (expected skewness, realized skewness, and realized volatility) for equity returns and assess whether they are informative for the cross-section of future stock returns. Since there is evidence that lagged skewness alone does not adequately forecast skewness, we estimate a cross-sectional model of expected skewness that uses additional predictive variables. Then, we sort stocks each month according to their idiosyncratic moments, forming quintile portfolios. We find a negative relationship between higher idiosyncratic moments and next-month stock returns. The trading strategy that sells stocks in the top quintile of expected skewness and buys stocks in the bottom quintile generates a significant monthly return of about 120 basis points. Our results are robust across sample periods, portfolio weightings, and to Fama and French (1993)’s risk adjustment factors. Finally, we identify a return reversal of stocks with high idiosyncratic skewness. Specifically, stocks with high idiosyncratic skewness have high contemporaneous returns. That tends to reverse, resulting in negative abnormal returns in the following month. / In the first chapter, we test some stochastic volatility models using options on the S&P 500 index. First, we demonstrate the presence of a short time-scale, on the order of days, and a long time-scale, on the order of months, in the S&P 500 volatility process using the empirical structure function, or variogram. This result is consistent with findings of previous studies. The main contribution of our paper is to estimate the two time-scales in the volatility process simultaneously by using nonlinear weighted least-squares technique. To test the statistical significance of the rates of mean-reversion, we bootstrap pairs of residuals using the circular block bootstrap of Politis and Romano (1992). We choose the block-length according to the automatic procedure of Politis and White (2004). After that, we calculate a first-order correction to the Black-Scholes prices using three different first-order corrections: (i) a fast time scale correction; (ii) a slow time scale correction; and (iii) a multiscale (fast and slow) correction. To test the ability of our model to price options, we simulate options prices using five different specifications for the rates or mean-reversion. We did not find any evidence that these asymptotic models perform better, in terms of RMSE, than the Black-Scholes model. In the second chapter, we use Brazilian data to compute monthly idiosyncratic moments (expected skewness, realized skewness, and realized volatility) for equity returns and assess whether they are informative for the cross-section of future stock returns. Since there is evidence that lagged skewness alone does not adequately forecast skewness, we estimate a cross-sectional model of expected skewness that uses additional predictive variables. Then, we sort stocks each month according to their idiosyncratic moments, forming quintile portfolios. We find a negative relationship between higher idiosyncratic moments and next-month stock returns. The trading strategy that sells stocks in the top quintile of expected skewness and buys stocks in the bottom quintile generates a significant monthly return of about 120 basis points. Our results are robust across sample periods, portfolio weightings, and to Fama and French (1993)’s risk adjustment factors. Finally, we identify a return reversal of stocks with high idiosyncratic skewness. Specifically, stocks with high idiosyncratic skewness have high contemporaneous returns. That tends to reverse, resulting in negative abnormal returns in the following month.

abstract: Project portfolio selection (PPS) is a significant problem faced by most organizations. How to best select the many innovative ideas that a company has developed to deploy in a proper and sustained manner with a balanced allocation of its resources over multiple time periods is one of vital importance to a company's goals. This dissertation details the steps involved in deploying a more intuitive portfolio selection framework that facilitates bringing analysts and management to a consensus on ongoing company efforts and buy into final decisions. A binary integer programming selection model that constructs an efficient frontier allows the evaluation of portfolios on many different criteria and allows decision makers (DM) to bring their experience and insight to the table when making a decision is discussed. A binary fractional integer program provides additional choices by optimizing portfolios on cost-benefit ratios over multiple time periods is also presented. By combining this framework with an `elimination by aspects' model of decision making, DMs evaluate portfolios on various objectives and ensure the selection of a portfolio most in line with their goals. By presenting a modeling framework to easily model a large number of project inter-dependencies and an evolutionary algorithm that is intelligently guided in the search for attractive portfolios by a beam search heuristic, practitioners are given a ready recipe to solve big problem instances to generate attractive project portfolios for their organizations. Finally, this dissertation attempts to address the problem of risk and uncertainty in project portfolio selection. After exploring the selection of portfolios based on trade-offs between a primary benefit and a primary cost, the third important dimension of uncertainty of outcome and the risk a decision maker is willing to take on in their quest to select the best portfolio for their organization is examined. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2018

Made available in DSpace on 2014-12-17T14:53:04Z (GMT). No. of bitstreams: 1 MelquiadesPLJ_DISSERT.pdf: 2472393 bytes, checksum: 59503f299bf1a82dcfc59bffce406c09 (MD5) Previous issue date: 2011-12-21 / The portfolio theory is a field of study devoted to investigate the decision-making by investors of resources. The purpose of this process is to reduce risk through diversification and thus guarantee a return. Nevertheless, the classical Mean-Variance has been criticized regarding its parameters and it is observed that the use of variance and covariance has sensitivity to the market and parameter estimation. In order to reduce the estimation errors, the Bayesian models have more flexibility in modeling, capable of insert quantitative and qualitative parameters about the behavior of the market as a way of reducing errors. Observing this, the present study aimed to formulate a new matrix model using Bayesian inference as a way to replace the covariance in the MV model, called MCB - Covariance Bayesian model. To evaluate the model, some hypotheses were analyzed using the method ex post facto and sensitivity analysis. The benchmarks used as reference were: (1) the classical Mean Variance, (2) the Bovespa index's market, and (3) in addition 94 investment funds. The returns earned during the period May 2002 to December 2009 demonstrated the superiority of MCB in relation to the classical model MV and the Bovespa Index, but taking a little more diversifiable risk that the MV. The robust analysis of the model, considering the time horizon, found returns near the Bovespa index, taking less risk than the market. Finally, in relation to the index of Mao, the model showed satisfactory, return and risk, especially in longer maturities. Some considerations were made, as well as suggestions for further work / A teoria de portf?lio ? um campo de estudos que se dedica a investigar a tomada de decis?o por investidores de recursos. O prop?sito desse processo ? a redu??o do risco por meio da diversifica??o e, portanto, a garantia de determinado retorno. Apesar disso, o modelo cl?ssico de M?dia-Vari?ncia cont?m cr?ticas quanto a sua parametriza??o, observa-se que o uso da vari?ncia e covari?ncias possui sensibilidade ao mercado e ? estima??o de par?metros. Como forma de redu??o dos erros de estima??o, os modelos bayesianos possuem mais flexibilidade na modelagem, com a possibilidade de inserir par?metros quantitativos e qualitativos sobre o comportamento do mercado como forma de redu??o de erros. Observando isso, o presente trabalho teve como objetivo formular um novo modelo de matriz por meio do teorema de Bayes, como forma de substitui??o da covari?ncia no modelo M-V, denominado de MCB - Modelo de Covari?ncia Bayesiana. Para avalia??o do modelo, algumas hip?teses s?o formuladas por meio do m?todo ex post facto e por an?lise de sensibilidade. Os benchmarks utilizados como refer?ncia foram: (1) o modelo cl?ssico de M?dia Vari?ncia; (2) o ?ndice de mercado da Bovespa; e, (3) 94 Fundos de Investimento. Os retornos acumulados durante o per?odo de maio de 2002 a dezembro de 2009 demonstraram superioridade do MCB em rela??o ao modelo cl?ssico M-V e o ?ndice Bovespa, por?m assumindo um pouco mais de risco diversific?vel que o M-V. A an?lise robusta do modelo, considerando o horizonte de tempo, constatou retornos pr?ximos ao Ibovespa, considerando menor risco que o mercado. Por ?ltimo, em rela??o ao ?ndice de Mao, o modelo se demonstrou satisfat?rio, em retorno e risco, principalmente em prazos mais longos. Por fim, algumas considera??es s?o realizadas, bem como sugest?es de futuros trabalhos

Esta tese trata do problema de controle ótimo estocástico de sistemas com saltos Markovianos e ruído multiplicativo a tempo discreto, com horizontes de tempo finito e infinito. A função custo é composta de termos quadráticos e lineares nas variáveis de estado e de controle, com matrizes peso indefinidas. Como resultado principal do problema com horizonte finito, é apresentada uma condição necessária e suficiente para que o problema de controle seja bem posto, a partir da qual uma solução ótima é derivada. A condição e a lei de controle são escritas em termos de um conjunto acoplado de equações de Riccati interconectadas a um conjunto acoplado de equações lineares recursivas. Para o caso de horizonte infinito, são apresentadas as soluções ótimas para os problemas de custo médio a longo prazo e com desconto, derivadas a partir de uma solução estabilizante de um conjunto de equações algébricas de Riccati acopladas generalizadas (GCARE). A existência da solução estabilizante é uma condição suficiente para que tais problemas sejam do tipo bem posto. Além disso, são apresentadas condições para a existência das soluções maximal e estabilizante do sistema GCARE. Como aplicações dos resultados obtidos, são apresentadas as soluções de um problema de otimização de carteiras de investimento com benchmark e de um problema de gestão de ativos e passivos de fundos de pensão do tipo benefício definido, ambos os casos com mudanças de regime nas variáreis de mercado. / This thesis considers the finite-horizon and infinite-horizon stochastic optimal control problem for discrete-time Markov jump with multiplicative noise linear systems. The performance criterion is assumed to be formed by a linear combination of a quadratic part and a linear part in the state and control variables. The weighting matrices of the state and control for the quadratic part are allowed to be indefinite. For the finite-horizon problem the main results consist of deriving a necessary and sufficient condition under which the problem is well posed and a optimal control law is derived. This condition and the optimal control law are written in terms of a set of coupled generalized Riccati difference equations interconnected with a set of coupled linear recursive equations. For the infinite-horizon problem a set of generalized coupled algebraic Riccati equations (GCARE) is studied. In this case, a sufficient condition under which there exists the maximal solution and a necessary and sufficient condition under which there exists the mean square stabilizing solution for the GCARE are presented. Moreover, a solution for the discounted and long run average cost problems is presented. The results obtained are applied to solver a portfolio optimization problem with benchmark and a pension fund problem with regime switching.