
Investment problems with stochastic dominance constraints
Dorová, Bianka ; Kopa, Miloš (advisor) ; Kozmík, Václav (referee)
This thesis focuses on stochastic dominance in portfolio selection problems. The thesis recalls basic knowledge from the area of portfolio optimization with utility functions and first, second, $N$th and infinite order of stochastic dominance. It sumarizes Post's, Kuosmanen's and Kopa's criteria for portfolio efficiency and necessary and sufficient conditions of stochastic dominance for discrete and continuous probability distributions. The thesis also contains formulations of optimization problems with second order stochastic dominance constraints derived for discrete and continuous probability distributions. A practical application is also a part of the thesis, where the optimization problems for monthly returns of Czech stocks are solved using optimization software GAMS.


Mean absolute deviation risk measure
Janouchová, Petra ; Kozmík, Václav (advisor) ; Branda, Martin (referee)
This bachelor thesis considers the mean absolute deviation as a risk me asure. It deals with its properties and its application in the case of the asset allocation problem. The Markowitz model is described and we demonstrated the relation between our model with mean absolute deviation and the Mar kowitz model. We study the influence of changes in the input data for the linear model with mean absolute deviation. The primary data used in this thesis are historical relative rates of profit of shares in the Prague Stock Ex change. The testing is done on the selected subsets of scenarios from primary data and the stability is discussed in conclusion.


Sample approximation technique in stochastic programming
Vörös, Eszter ; Branda, Martin (advisor) ; Kozmík, Václav (referee)
Title: Sample approximation technique in stochastic programming Author: Eszter V¨or¨os Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Martin Branda, Ph.D., Department of Probability and Mathematical Statistics Abstract: This thesis deals with the problem of stochastic programming. Sto chastic problems are usually applied for optimalization problems involving uncer tain parameters. The problem, which we are aimed to solve, is approximated with the socalled sample average approximation method. The sample used to estimate the true problem is generated by the Monte Carlo method. This technique allows us to use standard algorithms for the further treatment of the problem. The aim of this thesis is to discuss the convergence properites of the optimal value and the optimal solution of the approximed problem to the optimal value and the optimal solution of the real problem. The thesis ends with a practical demonstration of the theoretical results on a portfolio optimization problem. Keywords: stochastic programming, sample average approximation, Monte Carlo method, portfolio optimization 1


Optimization and stress tests
Fašungová, Diana ; Dupačová, Jitka (advisor) ; Kozmík, Václav (referee)
Title: Optimization and stress tests Author: Diana Fašungová Department: Department of Probability and Mathematical Statistics Supervisor: Prof. RNDr. Jitka Dupačová, DrSc., Department of Probability and Mathematical Statistics Abstract: In the thesis we apply contamination technique on a portfolio optimiza tion problem using minimization of risk measure CVaR. The problem is considered from a risk manager point of view. We stress correlation structure of data and of revenues using appropriately chosen data for this kind of problem and for ge nerated stress scenarios. From behaviour of CVaR with regard to contamination bounds, we formulate recommendations for the risk manager optimizing his port folio. The recommendations are interpreted for both types of stress scenarios. In the end, limitations of the model and possible ways of improvement are discussed. Keywords: contamination bounds, stress tests, portfolio optimization, risk mana gement


Portfolio efficiency with continuous probability distribution of returns
Kozmík, Václav ; Kopa, Miloš (advisor) ; Dupačová, Jitka (referee)
Present work deals with the portfolio selection problem using meanrisk models. The main goal of this work is to investigate the convergence of approxi mate solutions using generated scenarios to the analytic solution and its sensitivity to chosen risk measure and probability distribution. The considered risk measures are: variance, VaR, cVaR, absolute deviation and semivariance. We present analytical solutions for all risk measures under the assumption of normal or Student distribution. For lognormal distribution, we use the approximate assumption that the sum of lognormal random variables has lognormal distribution. Optimization models for discrete scenarios are derived for all risk measures and compared with analytical solution. In case of approximate solution with scenarios, we repeat the procedure multiple times and present our own approach to nding the optimal solution using the cluster analysis. All optimization models are written in GAMS language. Testing and estimating are realized using an application developed in C++ language.


Importance Sampling methods in solving optimization problems
Zavřel, Lukáš ; Kozmík, Václav (advisor) ; Kopa, Miloš (referee)
Present work deals with the portfolio selection problem using meanrisk models where analysed risk measures include variance, VaR and CVaR. The main goal is to approximate solution of optimization problems using simulation techniques like Monte Carlo and Importance Sampling. For both simulation techniques we present a numerical study of their variance and efficiency with respect to optimal solution. For normal distribution with particular expected value and variance the values of parameters for sampling using Importance Sampling method are empirically deduced and they are consequently used for solving a practical problem of choice of optimal portfolio from ten stocks, when their weekly historical prices are available. All optimization problems are solved in Wolfram Mathematica program. Powered by TCPDF (www.tcpdf.org)


Portfolio efficiency with continuous probability distribution of returns
Kozmík, Václav
Present work deals with the portfolio selection problem using meanrisk models. The main goal of this work is to investigate the convergence of approximate solutions using generated scenarios to the analytic solution and its sensitivity to chosen risk measure and probability distribution. The considered risk measures are: variance, VaR, cVaR, absolute deviation and semivariance. We present analytical solutions for all risk measures under the assumption of normal or Student distribution. For lognormal distribution, we use the approximate assumption that the sum of lognormal random variables has lognormal distribution. Optimization models for discrete scenarios are derived for all risk measures and compared with analytical solution. In case of approximate solution with scenarios, we repeat the procedure multiple times and present our own approach to finding the optimal solution using the cluster analysis. All optimization models are written in GAMS language. Testing and estimating are realized using an application developed in C++ language.


Solution of Emission Management Problem
Šmíd, Martin ; Kozmík, Václav
Optimal covering of emissions stemming from random production is a multistage stochastic programming problem. Solving it in a usual way  by means of deterministic equivalent  is possible only given an unrealistic approximation of random parameters. There exists an efficient way of solving multistage problems  stochastic dual dynamic programming (SDDP), however, it requires the interstage independence of random parameters, which is not the case which our problem. In the paper, we discuss a modified version of SDDP, allowing for some form of interstage dependence.


Two Algorithms for Riskaverse Reformulation of Multistage Stochastic Programming Problems
Šmíd, Martin ; Kozmík, Václav
Many reallife applications lead to riskaverse multistage stochastic problems, therefore effective solution of these problems is of great importance. Many tools can be used to their solution (GAMS, CoinOR, APML or, for smaller problems, Excel), it is, however, mostly up to researcher to reformulate the problem into its deterministic equivalent. Moreover, such solutions are usually onetime, not easy to modify for different applications. We overcome these problems by providing a frontend software package, written in C++, which enables to enter problem definitions in a way close to their mathematical definition. Creating of a deterministic equivalent (and its solution) is up to the computer. In particular, our code is able to solve linear multistage with Multiperiod MeanCVaR or Nested MeanCVaR criteria. In the present paper, we describe the algorithms, transforming these problems into their deterministic equivalents.


Importance Sampling methods in solving optimization problems
Zavřel, Lukáš ; Kozmík, Václav (advisor) ; Kopa, Miloš (referee)
Present work deals with the portfolio selection problem using meanrisk models where analysed risk measures include variance, VaR and CVaR. The main goal is to approximate solution of optimization problems using simulation techniques like Monte Carlo and Importance Sampling. For both simulation techniques we present a numerical study of their variance and efficiency with respect to optimal solution. For normal distribution with particular expected value and variance the values of parameters for sampling using Importance Sampling method are empirically deduced and they are consequently used for solving a practical problem of choice of optimal portfolio from ten stocks, when their weekly historical prices are available. All optimization problems are solved in Wolfram Mathematica program. Powered by TCPDF (www.tcpdf.org)
