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Optimal investment problems solvable using linear programming
Jančařík, Joel ; Branda, Martin (advisor) ; Kopa, Miloš (referee)
Portfolio optimization problem is a classical optimization problem, where the expected return of the portfolio is maximized and the risk is minimized. In this bachelor thesis some LP solvable portfolio optimization models are studied. Application on real life financial data is also included. Model with Conditional Value at Risk, MAD-model and Minimax model are described. In numerical analysis data from Frankfurt Stock Exchange are used and optimization has been made by Wolfram Mathematica 9.0 function LinearProgramming. As a result we got optimal portfolios for eleven different models for each of six minimal expected return constraints. The portfolios have been then evaluated according to the data from next year period. Powered by TCPDF (www.tcpdf.org)
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Quantitative methods in finance
Zboňáková, Lenka ; Hurt, Jan (advisor) ; Zichová, Jitka (referee)
In the present thesis we deal with the quantitative risk measures estimating the influence of market risk on the investments to the financial instruments. The most commonly used measure is Value at Risk which we introduce with its characteristics and modifications. Applying the methods to real data we deal with the problem of approximation of its distribution, especially in the multidimensional cases when the risk factors are dependent on each other. This leads us to explore copula functions that are in the thesis used to include the dependence structures of the risk factors to calculation of the risk measures. Chosen methods of approximation and evaluation of the risk measures are applied to real data and stated with outputs and their comparison.
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Sparse robust portfolio optimization via NLP regularizations
Branda, Martin ; Červinka, Michal ; Schwartz, A.
We deal with investment problems where we minimize a risk measure\nunder a condition on the sparsity of the portfolio. Various risk measures\nare considered including Value-at-Risk and Conditional Value-at-Risk\nunder normal distribution of returns and their robust counterparts are\nderived under moment conditions, all leading to nonconvex objective\nfunctions. We propose four solution approaches: a mixed-integer formulation,\na relaxation of an alternative mixed-integer reformulation and\ntwo NLP regularizations. In a numerical study, we compare their computational\nperformance on a large number of simulated instances taken\nfrom the literature.
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Stochastic Programming Methods for Investment Decisions
Kubelka, Lukáš ; CFA, Tomáš Menčík, (referee) ; Popela, Pavel (advisor)
This thesis deals with methods of stochastic programming and their application in financial investment. Theoretical part is devoted to basic terms of mathematical optimization, stochastic programming and decision making under uncertainty. Furter, there are introduced basic principles of modern portfolio theory, substantial part is devoted to risk measurement techniques in the context of investment, mostly to the methods Value at Risk and Expected shortfall. Practical part aims to creation of optimization models with an emphasis to minimize investment risk. Created models deal with real data and they are solved in optimization software GAMS.
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Financial optimization
Štolc, Zdeněk ; Kuncová, Martina (advisor) ; Kalčevová, Jana (referee)
This thesis is focused on a theoretical explanation of some models for the optimization stock portfolios with different risk measure. The theory of the nonlinear programming is detailed developed and also basic Markowitz`s model with another optimization models as Konno -- Yamazaki`s model, Roy`s model, semivariance approach and Value at Risk approach, which are based on alternative risk measure. For all models the assumptions of their applications are highlighted and the comparation of these models is made too. Analytical part is concerned in the construction of the effecient portfolios according to the described models is made on the historical market prices of 13 companies traded on Prague Stock Exchange in SPAD.
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