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Statistical tests for VaR and CVaR
Mirtes, Lukáš ; Pešta, Michal (advisor) ; Večeř, Jan (referee)
The thesis presents test statistics of Value-at-Risk and Conditional Value-at-Risk. The reader is familiar with basic nonparametric estimators and their asymptotic distributions. Tests of accuracy of Value-at- Risk are explained and asymptotic test of Conditional Value-at-Risk is derived. The thesis is concluded by process of backtesting of Value-at-Risk model using real data and computing statistical power and probability of Type I error for selected tests. Powered by TCPDF (www.tcpdf.org)
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Measuring systemic risk in time-frequency domain
Muzikářová, Ivana ; Baruník, Jozef (advisor) ; Bauer, Michal (referee)
This thesis provides an analysis of systemic risk in the US banking sector. We use conditional value at risk (∆CoVaR), marginal expected shortfall (MES) and cross-quantilogram (CQ) to statistically measure tail-dependence in return series of individual institutions and the system as a whole. Wavelet multireso- lution analysis is used to study systemic risk in the time-frequency domain. De- composition of returns on different scales allows us to isolate cycles of 2-8 days, 8-32 days and 32-64 days and analyze co-movement patterns which would oth- erwise stay hidden. Empirical results demonstrate that filtering out short-term noise from the return series improves the forecast power of ∆CoVaR. Eventu- ally, we investigate the connection between statistical measures of systemic risk and fundamental characteristics of institutions (size, leverage, market to book ratio) and conclude that size is the most robust determinant of systemic risk.
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Diversification in Data Envelopment Analysis in finance
Macková, Simona ; Branda, Martin (advisor) ; Hurt, Jan (referee)
Title: Diversification in Data Envelopment Analysis in Finance Author: Simona Macková Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Martin Branda, Ph.D., Department of Probability and Ma- thematical Statistics Abstract: This thesis deals with an extension of data envelopment analysis and its application in finance. This method enables to evaluate the efficiency of cho- sen production units based on several inputs and outputs. Administrative fees or risk measures can be used as inputs and expected incomes of observed assets as outputs in financial application. We show basic traditional models in a form of a primary problem of linear programming and a dual problem as well and later compare with diversification models. It is suitable to deal with diversification which enables to consider dependencies between assets in case of finance and in- vestments. Than we get to nonlinear programming problem hence we introduce appropriate risk and return measures to make the problem solvable. Especially, we focus on the conditional value at risk. Next we introduce the model which deals with diversification. We use this on real data of chosen mutual funds. Keywords: Data envelopment analysis, Efficiency, Diversification, Conditional value at risk
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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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Contemporary measures of financial risk
Leder, Ondřej ; Hurt, Jan (advisor) ; Zichová, Jitka (referee)
The main goal of this thesis is to talk about some financial risks and to introduce some methods of measuring them. We place great emphasis on the value at risk, its extension in form of conditional value at risk and introduction of some of its possible alternatives, which are expectile and spectral risk measures. For this it is necessary to introduce some findings of the theory of probability. Our goal is to show the similarity of expectile and quantile, because value at risk is practicaly a quantile. Another goal of this thesis is to show weakness of VaR and to practically illustrate the possibility of using expectile as an alternative to VaR. Powered by TCPDF (www.tcpdf.org)
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Contemporary measures of financial risk
Leder, Ondřej ; Hurt, Jan (advisor) ; Zichová, Jitka (referee)
The main goal of this work is to talk about some financial risks and to introduce some methods of measuring them. The most important part of this work is the value at risk, its extension in form of conditional value at risk and introduction of some of its possible alternatives, which are expectile and spectral risk measures. For this it is needed to give a theoretical framework from the theory of probability. Its goal is to show the similarity of expectile and quantile, because value at risk is practicaly a quantile. Another goal of this fork is to show some weak properties of VaR and to practically illustrate the possibility of using expectile as an alternative to VaR. 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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Application of quantile autoregressive models in minimum Value at Risk and Conditional Value at Risk hedging
Svatoň, Michal ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
Futures contracts represent a suitable instrument for hedging. One conse- quence of their standardized nature is the presence of basis risk. In order to mitigate it an agent might aim to minimize Value at Risk or Expected Shortfall. Among numerous approaches to their modelling, CAViaR models which build upon quantile regression are appealing due to the limited set of assumptions and decent empirical performance. We propose alternative specifications for CAViaR model - power and exponential CAViaR, and an alternative, flexible way of computing Expected Shortfall within CAViaR framework - Implied Expectile Level. Empirical analysis suggests that ex- ponential CAViaR yields competitive results both in Value at Risk and Ex- pected Shortfall modelling and in subsequent Value at Risk and Expected Shortfall hedging. 1
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