The thesis deals with high-order stochastic dominance of random variables and portfolios. The summary of findings about high-order stochastic dominance and portfolio efficiency is presented. As a main part of the thesis it is proven that under assumption of both normal and gamma distribution the infinite-order stochastic dominance is equivalent to the second-order stochastic dominance. The necessary and sufficient condition for the infinite-order stochastic dominance portfolio efficiency is derived under the assumption of normality. The condition is used in the empirical part of the thesis where parametrical approach to the portfolio efficiency is compared to the nonparametric scenario approach. The derived necessary and sufficient condition is based on the assumption of normality; therefore we use two sets of data, one with fulfilled assumption of normality and the other for which the assumption of normality was unambigously rejected. Consequently, the influence of fulfillment of the normality assumption on the results of the necessary and sufficient condition for portfolio efficiency is estimated.

<div>Life cycle cost analysis (LCCA) has been defined as a method to assess the total cost of a project. It is a simple tool to use when a single project has different alternatives that fulfill the original requirements. Different alternatives could differ in initial investment, operational and maintenance costs among other long term costs. The cost involved in building a bridge depends upon many different factors. Moreover, long-term cost needs to be considered to estimate the true overall cost of the project and determine its life-cycle cost. Without watchful consideration of the long-term costs and full life cycle costing, current investment decisions that look attractive could result in a waste of economic resources in the future. This research is focused on short and medium span bridges (between 30-ft and 130-ft) which represents 65\% of the NBI INDIANA bridge inventory. </div><div><br></div><div>Bridges are categorized in three different groups of span ranges. Different superstructure types are considered for both concrete and steel options. Types considered include: bulb tees, AASHTO prestressed beams, slab bridges, prestressed concrete box beams, steel beams, steel girders, folded plate girders and simply supported steel beams for dead load and continuous for live load (SDCL). A design plan composed of simply supported bridges and continuous spans arrangements was carried out. Analysis for short and medium span bridges in Indiana based on LCCA is presented for different span ranges and span configurations. </div><div><br></div><div>Deterministic and stochastic analysis were done for all the span ranges considered. Monte Carlo Simulations (MCS) were used and the categorization of the different superstructure alternatives was done based on stochastic dominance. First, second, almost first and almost second stochastic dominance rules were used to determined the efficient set for each span length and all span configurations. Cost-effective life cycle cost profiles for each superstructure type were proposed. Additionally, the top three cost-effective alternatives for superstructure types depending on the span length are presented as well as the optimum superstructure types set for both simply supported and continuous beams. Results will help designers to consider the most cost-effective bridge solution for new projects, resulting in cost savings for agencies involved.</div>

Asset and Liability Management (ALM) models have become well established decision tools for pension funds. ALMs are commonly modelled as multi-stage, in which a large terminal wealth is required, while at intermediate time periods, constraints on the funding ratio, that is, the ratio of assets to liabilities, are imposed. Underfunding occurs when the funding ratio is too low; a target value for funding ratios is pre-specified by the decision maker. The risk of underfunding has been usually modelled by employing established risk measures; this controls one single aspect of the funding ratio distributions. For example, controlling the expected shortfall below the target has limited power in controlling shortfall under worst-case scenarios. We propose ALM models in which the risk of underfunding is modelled based on the concept of Second Order Stochastic Dominance (SSD). This is a criterion of ranking random variables - in our case funding ratios - that takes the entire distributions of interest into account and works under the widely accepted assumptions of decision makers being rational and risk averse. In the proposed SSD models, investment decisions are taken such that the resulting short-term distribution of the funding ratio is non-dominated with respect to SSD, while a constraint is imposed on the expected terminal wealth. This is done by considering progressively larger tails of the funding ratio distribution and considering target levels for them; a target distribution is thus implied. Different target distributions lead to different SSD efficient solutions. Improved distributions of funding ratios may be thus achieved, compared to the existing risk models for ALM. This is the first contribution of this thesis. Interesting results are obtained in the special case when the target distribution is deterministic, specified by one single outcome. In this case, we can obtain equivalent risk minimisation models, with risk defined as expected shortfall or as worst case loss. This represents the second contribution. The third contribution is a framework for scenario generation based on the "Birth, Immigration, Death, Emigration" (BIDE) population model and the Empirical copula; the scenarios are used to evaluate the proposed models and their special cases both in-sample and out-of-sample. As an application, we consider the planning problem of a large DB pension fund in Saudi Arabia.

In this dissertation we study several non-convex and stochastic optimization problems. The common theme is the use of mixed-integer programming (MIP) techniques including valid inequalities and reformulation to solve these problems. We first study a strategic capacity planning model which captures the trade-off between the incentive to delay capacity installation to wait for improved technology and the need for some capacity to be installed to meet current demands. This problem is naturally formulated as a MIP with a bilinear objective. We develop several linear MIP formulations, along with classes of strong valid inequalities. We also present a specialized branch-and-cut algorithm to solve a compact concave formulation. Computational results indicate that these formulations can be used to solve large-scale instances. We next study methods for optimization with joint probabilistic constraints. These problems are challenging because evaluating solution feasibility requires multidimensional integration and the feasible region is not convex. We propose and analyze a Monte Carlo sampling scheme to simplify the probabilistic structure of such problems. Computational tests of the approach indicate that it can yield good feasible solutions and reasonable bounds on their quality. Next, we study a MIP formulation of the non-convex sample approximation problem. We obtain two strengthened formulations. As a byproduct of this analysis, we obtain new results for the previously studied mixing set, subject to an additional knapsack inequality. Computational results indicate that large-scale instances can be solved using the strengthened formulations. Finally, we study optimization problems with stochastic dominance constraints. A stochastic dominance constraint states that a random outcome which depends on the decision variables should stochastically dominate a given random variable. We present new formulations for both first and second order stochastic dominance which are significantly more compact than existing formulations. Computational tests illustrate the benefits of the new formulations.

Stochastic methods are present in our daily lives, especially when we need to make a decision based on uncertain events. In this thesis, we present basic approaches used in stochastic tasks. In the first chapter, we define the stochastic problem and introduce basic methods and tasks which are present in the literature. In the second chapter, we present various problems which are non-linearly dependent on the probability measure. Moreover, we introduce deterministic and non-deterministic multicriteria tasks. In the third chapter, we give an insight on the concept of stochastic dominance and we describe the methods that are used in tasks with multidimensional stochastic dominance. In the fourth chapter, we capitalize on the knowledge from chapters two and three and we try to solve the role of portfolio optimization on real data using different approaches. 1

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Title: Semi-infinite programming: theory and portfolio efficiency application Author: Bc. Lukáš Klouda Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Ing. Miloš Kopa, PhD. Supervisor's e-mail address: kopa@karlin.mff.cuni.cz Abstract: The thesis deals with application of semi-infinite programming to a portfolio efficiency testing. The summary of semi-infinite programming, first and second order optimality conditions and duality in linear semi-infinite programming is presented. The optimization problem for a portfolio efficiency testing with respect to the second order stochastic dominance under assumption of discrete, normal, Students and general elliptical distribution is formulated. Conditional value at risk(CVaR) is used as the risk measure, because of its consistency with the second order stochastic dominance relation. Efficiency of index PX with respect to the second order stochastic dominance is tested. The tests are performed using the program GAMS.

This thesis deals with selecting the optimal portfolio for a risk averse investor. Firstly, we present the risk measures, specifically spectral risk me- asures which consider an individual risk aversion of the investor. Then we propose a diversification-consistent data envelopment analysis model. The model is searching for an efficient portfolio with respect to second-order sto- chastic dominance. The crux of the thesis is a model based on the theory of multi-criteria optimization and spectral risk measures. The presented mo- del is searching for an optimal portfolio suitable for the investor with a given risk aversion. In addition, the optimal portfolio is also consistent with second- order stochastic dominance efficiency. The topic of the practical part is a nu- merical study in which both models are implemented in MATLAB. Models are applied to a dataset from real financial markets. Personal contribution lies in comparing the diversification-consistent data envelopment analysis model and model based on multi-criteria optimization, both with respect to second order stochastic dominance efficiency.

The broad aim of this research is to contribute knowledge that enables improvements in how designers model decision alternatives at the systems level—i.e., how they model different system configurations and concepts. There are three principal complications: (1) design concepts and systems configurations are partially-defined solutions to a problem that correspond to a large set of possible design implementations, (2) each concept or configuration may operate on different physical principles, and (3) decisions typically involve tradeoffs between multiple competing objectives that can include "non-engineering" considerations such as production costs and profits. This research is an investigation of a data-driven approach to modeling partially-defined system alternatives that addresses these issues. The approach is based on compositional strategy in which designers model a system alternative using abstract models of its components. The component models are representations of the rational tradeoffs available to designers when implementing the components. Using these models, designers can predict key properties of the final implementation of each system alternative. A new construct, called a parameterized efficient set, is introduced as the decision-theoretic basis for generating the component-level tradeoff models. Appropriate efficiency criteria are defined for the cases of deterministic and uncertain data. It is shown that the model composition procedure is mathematically sound under reasonable assumptions for the case of deterministic data. This research also introduces an approach for describing the valid domain of a data-driven model based on the use of support-vector machines. Engineering examples include performing requirements allocation for a hydraulic log splitter and architecture selection for a hybrid vehicle.

Submitted by Guilherme Risério (guilherme.riserio@gmail.com) on 2014-03-17T18:48:40Z No. of bitstreams: 1 O DESEMPENHO DOS HEDGE FUNDS BRASILEIROS A PARTIR DA NÃO NORMALIDADE DE SEUS RETORNOS - GUILHERME RISERIO.pdf: 4785756 bytes, checksum: 67e0577d6e7563bff201f16f19a2fd59 (MD5) / Approved for entry into archive by PAMELA BELTRAN TONSA (pamela.tonsa@fgv.br) on 2014-03-17T19:15:53Z (GMT) No. of bitstreams: 1 O DESEMPENHO DOS HEDGE FUNDS BRASILEIROS A PARTIR DA NÃO NORMALIDADE DE SEUS RETORNOS - GUILHERME RISERIO.pdf: 4785756 bytes, checksum: 67e0577d6e7563bff201f16f19a2fd59 (MD5) / Made available in DSpace on 2014-03-17T19:26:19Z (GMT). No. of bitstreams: 1 O DESEMPENHO DOS HEDGE FUNDS BRASILEIROS A PARTIR DA NÃO NORMALIDADE DE SEUS RETORNOS - GUILHERME RISERIO.pdf: 4785756 bytes, checksum: 67e0577d6e7563bff201f16f19a2fd59 (MD5) Previous issue date: 2014-02-26 / Devido à utilização de estratégias distintas de investimento dos hedge funds brasileiros caracterizadas pelo uso de derivativos, operações alavancadas e vendas a descoberto, esses fundos apresentam significante não normalidade dos retornos gerados. Portanto, as medidas usuais de avaliação de performance são incapazes de fornecer resultados consistentes com o verdadeiro desempenho dos portfólios de hedge fund. Este trabalho irá utilizar duas metodologias não tradicionais para analisar a performance dos hedge funds brasileiros e determinar qual estratégia supera o mercado acionário. Serão utilizadas duas medidas não paramétricas, Almost Stochastic Dominância (ASD) e Manipulation-Proof Performance Measure (MPPM). Os resultados demonstram que os hedge funds brasileiros não superam os benckmaks utilizados na dominância de primeira ordem, mas quando analisada a dominância de segunda ordem sete estratégias apresentaram desempenho superior ao Índice Ibovespa.