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Selected financial optimization models
Bujnovský, Daniel ; Bednář, Josef (referee) ; Popela, Pavel (advisor)
This work is focused on models of optimal asset and liability management. The practical section illustrates various ways of modelling strategies depending on the problem formulation, chosen set of assets and the type of the used optimization technique. The main examples are portfolio immunization and the Yasuda-Kasai model together with the extended version of Markowitz model. The author provides across the work an overview of different financial risks and various tools for their measurement together with possible formulations of expected returns relevant to the studied models. The individual models are compared and often extended by other constraints in order to improve their practical applicability. From the point of view of the mathematical optimization several ways of input data generation are described for example by using the extended Brownian motion. All practical parts go hand in hand with illustrative pictures and codes. The necessary financial and mathematical theory is included as well.
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Robust portfolio selection problem
Zákutná, Tatiana ; Kopa, Miloš (advisor) ; Lachout, Petr (referee)
In this thesis, a portfolio optimization with integer variables which influ- ence optimal assets allocation, is studied. Measures of risk are defined and the cor- responding mean-risk models are derived. Two methods are used to develop robust models involving uncertainty in probability distribution: the worst-case analyses and contamination. The uncertainty in values of scenarios and in their probabili- ties of the discrete probability distribution is assumed separately followed by their combination. These models are applied to stock market data with using optimization software GAMS.
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Importance Sampling methods in solving optimization problems
Zavřel, Lukáš ; Kozmík, Václav (advisor) ; Kopa, Miloš (referee)
Present work deals with the portfolio selection problem using mean-risk 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)
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Efficiency of representative portfolios using data envelopment analysis
Junová, Jana ; Kopa, Miloš (advisor) ; Branda, Martin (referee)
In this work, several data envelopment analysis (DEA) models are used to assess efficiency of US representative portfolios. We consider a portfolio to be efficient if no other surpasses it in minimizing risk or maximizing return. This property is precisely defined in the work and it can be well detected by DEA models. DEA models assuming constant return-to-scale (CRS) as well as variable return-to- scale (VRS) are described here. A model with directional measure is also presented. Four of the VRS models are transformed into diversification consistent (DC) models. In the empirical part, CVaRs on multiple levels are used as risk measures and expected return as a return measure typically. Results acquired using different DEA models to assess efficiency of portfolios are compared. DC models are stronger than their classical VRS counterparts. The DC models identified as efficient only the portfolio with the highest expected return. On the contrary, VRS models classified as efficient more portfolios which differ in riskiness. Their results could be interesting if an investor wanted to choose only one portfolio based on its riskiness.
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Choice of the risk-aversion coefficient in optimization
Janásková, Eliška ; Kopa, Miloš (advisor) ; Lachout, Petr (referee)
Cílem této práce je studovat chování portfolia slo¾eného z daných akcií pro rùzné parametry averze k riziku. Nejprve popí¹eme, jaké vlastnosti by mìla splòovat vhodná míra rizika a poté uká¾eme, které z nich tyto vlastnosti opravdu splòují. Pøedstavíme Markowitzùv model a Mean-CVaR model, které slou¾í k optimalizaci portfolia. Z historických dat poté pomocí Mean-CVaR modelu urèíme pro dané akcie jejich zastoupení v optimálním portfoliu v závislosti na parametru averze k riziku a podíváme se, jak by si toto portfolio vedlo v následujících obdob ích. Na základì tìchto výpoètù budeme diskutovat výbìr vhodného parametru. Powered by TCPDF (www.tcpdf.org)
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Importance Sampling methods in solving optimization problems
Zavřel, Lukáš ; Kozmík, Václav (advisor) ; Kopa, Miloš (referee)
Present work deals with the portfolio selection problem using mean-risk 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)
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Robust portfolio selection
Horváthová, Inés ; Červinka, Michal (advisor) ; Kraicová, Lucie (referee)
In this thesis, we take the mean-risk approach to portfolio optimi- zation. We will first define risk measures in general and then intro- duce three commonly used ones: variance, Value-at-risk (V aR) and Conditional-value-at-risk (CV aR). For each of these risk measures we formulate the corresponding mean-risk models. We then present their robust counterparts. We focus mainly on the robust mean-variance models, which we also apply to historical data using free statistical software R. Finally, we compare the results with the classical non- robust mean-variance model.
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Multi-Stage Stochastic Programming with CVaR: Modeling, Algorithms and Robustness
Kozmík, Václav ; Dupačová, Jitka (advisor) ; Morton, David (referee) ; Kaňková, Vlasta (referee)
Multi-Stage Stochastic Programming with CVaR: Modeling, Algorithms and Robustness RNDr. Václav Kozmík Abstract: We formulate a multi-stage stochastic linear program with three different risk measures based on CVaR and discuss their properties, such as time consistency. The stochastic dual dynamic programming algorithm is described and its draw- backs in the risk-averse setting are demonstrated. We present a new approach to evaluating policies in multi-stage risk-averse programs, which aims to elimi- nate the biggest drawback - lack of a reasonable upper bound estimator. Our approach is based on an importance sampling scheme, which is thoroughly ana- lyzed. A general variance reduction scheme for mean-risk sampling with CVaR is provided. In order to evaluate robustness of the presented models we extend con- tamination technique to the case of large-scale programs, where a precise solution cannot be obtained. Our computational results are based on a simple multi-stage asset allocation model and confirm usefulness of the presented procedures, as well as give additional insights into the behavior of more complex models. Keywords: Multi-stage stochastic programming, stochastic dual dynamic programming, im- portance sampling, contamination, CVaR
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Newsboy problem
Šedina, Jaroslav ; Dupačová, Jitka (advisor) ; Lachout, Petr (referee)
This thesis deals with the newsboy problem and its various modifications. The first part of the thesis mentions definitions and theorems that are essential for investigation of the optimal solution of the problem. In the second part, various formulations of newsboy problem are discussed and their solutions are presented. For instance, we use Sample Average Approximation method. In the final part, the results are applied to calculate Conditional Value-at-Risk (CVaR) and the thesis concludes with a numerical study programmed in R which compares parametric and nonparametric approach to the problem. The text is consecutively supplemented with graphs. Powered by TCPDF (www.tcpdf.org)
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