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Cost of Equity as a Measuring Instrument of Risks during the Corporate Life Cycle
Konečný, Zdeněk ; Bartoš, Vojtěch (referee) ; Duspiva, Pavel (referee) ; Živělová, Iva (referee) ; Zinecker, Marek (advisor)
In this doctoral thesis is suggested the methodics for determination the risk structure depending on the corporate life cycle with considering the sector sensitivity to the economic cycle. The share of the operational and financial risk is calculated using the beta coefficient, in which the selected measuring quantities are included. The phases of the corporate life cycle are identified according to the quadrants of the Boston matrix and the sector sensitivity to the economic cycle is determined using the Spearman´s rank correlation coefficient describing the relation between the gross domestic product and sales of the sector. The methodics is applicable for both managers and investors.
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The use of coherent risk measures in operational risk modeling
Lebovič, Michal ; Teplý, Petr (advisor) ; Doležel, Pavel (referee)
The debate on quantitative operational risk modeling has only started at the beginning of the last decade and the best-practices are still far from being established. Estimation of capital requirements for operational risk under Advanced Measurement Approaches of Basel II is critically dependent on the choice of risk measure, which quantifies the risk exposure based on the underlying simulated distribution of losses. Despite its well-known caveats Value-at-Risk remains a predominant risk measure used in the context of operational risk management. We describe several serious drawbacks of Value-at-Risk and explain why it can possibly lead to misleading conclusions. As a remedy we suggest the use of coherent risk measures - and namely the statistic known as Expected Shortfall - as a suitable alternative or complement for quantification of operational risk exposure. We demonstrate that application of Expected Shortfall in operational loss modeling is feasible and produces reasonable and consistent results. We also consider a variety of statistical techniques for modeling of underlying loss distribution and evaluate extreme value theory framework as the most suitable for this purpose. Using stress tests we further compare the robustness and consistency of selected models and their implied risk capital estimates...
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Cyber risk modelling using copulas
Spišiak, Michal ; Teplý, Petr (advisor) ; Baruník, Jozef (referee)
Cyber risk or data breach risk can be estimated similarly as other types of operational risk. First we identify problems of cyber risk models in existing literature. A large dataset consisting of 5,713 loss events enables us to apply extreme value theory. We adopt goodness of fit tests adjusted for distribution functions with estimated parameters. These tests are often overlooked in the literature even though they are essential for correct results. We model aggregate losses in three different industries separately and then we combine them using a copula. A t-test reveals that potential one-year global losses due to data breach risk are larger than the GDP of the Czech Republic. Moreover, one-year global cyber risk measured with a 99% CVaR amounts to 2.5% of the global GDP. Unlike others we compare risk measures with other quantities which allows wider audience to understand the magnitude of the cyber risk. An estimate of global data breach risk is a useful indicator not only for insurers, but also for any organization processing sensitive data.
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Analysis of truncated data with application to the operational risk estimation
Volf, Petr
Analysis of operational risk often faces problems arising from the structure of available data, namely of left truncation and occurrence of heavy-tailed loss values. We deal with model given by lognormal dostribution contaminated by the Pareto one and to use of the Cramér-von Mises, Anderson-Darling, and Kolmogorov-Smirnov minimum distance estimators. Analysis is based on MC studies. The main objective is to propose a method of statistical analysis and modeling for the distribution of sum of\nlosses over a given period, particularly of its right quantiles.
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Assessment of cyber risk in the banking industry
Spišiak, Michal ; Teplý, Petr (advisor) ; Maršál, Aleš (referee)
There has never been more need to discuss cybersecurity related issues. We live in a world where criminals do not have to physically visit a bank to steal money from it, where elections results can be influenced by data breached from personal email accounts, where to win a war a country needs skilled cybersecurity specialists rather than powerful weapons and where patients do not get recommended treatment because a hospital is under a cyberattack. The financial industry as a backbone of any modern economy requires adequate protection against cybercrime. We discuss major cyber threats for financial institutions as well as possible protection methods. After that we introduce Basel II Framework for operational risk assessment and we evaluate data breach risk in an empirical analysis. 1
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LDA approach to operational risk modelling
Kaplanová, Martina ; Mazurová, Lucie (advisor) ; Zichová, Jitka (referee)
In this thesis we will deal with the term of operational risk, as it is presented in the directives Basel 2 that are mandatory for financial institutions in the European Union. The main problem is operational risk modeling, therefore, how to measure and manage it. In the first part we will look at the possibility of calculating the capital requirements for operational risk under Basel 2, mainly the calculation with the internal model. We will describe the specific procedures for the development of the internal model and we will focus on Loss Distribution Approach. The internal model will be based on modeling of loss in each risk cell separately. In the second part we will show, how to include modeling of dependence structure between risk cells to the internal model with using copulas. Finally, we will show the illustrative example, where we will see, whether the modeling of dependence leads to a reduction of the total capital requirement. Powered by TCPDF (www.tcpdf.org)
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Operational risk modelling
Mináriková, Eva ; Mazurová, Lucie (advisor) ; Hlubinka, Daniel (referee)
In the present thesis we will firstly familiarize ourselves with the term of operational risk, it's definition presented in the directives Basel II and Solvency II, and afterwards with the methods of calculation Capital Requirements for Operational Risk, set by these directives. In the second part of the thesis we will concentrate on the methods of modelling operational loss data. We will introduce the Extreme Value Theory which describes possible approaches to modelling data with significant values that occur infrequently; the typical characteristic of operational risk data. We will mainly focus on the model for threshold exceedances which utilizes Generalized Pareto Distribution to model the distribution of those excesses. The teoretical knowledge of this theory and the appropriate modelling will be applied on simulated loss data. Finally we will test the ability of presented methods to model loss data distributions.
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Methods of Risk Aggregation on Financial Markets
Pavlovičová, Jana ; Gapko, Petr (advisor) ; Báťa, Karel (referee)
This diploma thesis "Methods of risk aggregation on financial markets" introduces all kinds of risk that are present on the financial markets. In the first part there are explained the ways and methods of measurement of these risks. Next there are shown the methods of aggregation of credit, market and operational risks. One of these methods are copula functions which are constructed in practical part of this thesis.
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The use of coherent risk measures in operational risk modeling
Lebovič, Michal ; Teplý, Petr (advisor) ; Doležel, Pavel (referee)
The debate on quantitative operational risk modeling has only started at the beginning of the last decade and the best-practices are still far from being established. Estimation of capital requirements for operational risk under Advanced Measurement Approaches of Basel II is critically dependent on the choice of risk measure, which quantifies the risk exposure based on the underlying simulated distribution of losses. Despite its well-known caveats Value-at-Risk remains a predominant risk measure used in the context of operational risk management. We describe several serious drawbacks of Value-at-Risk and explain why it can possibly lead to misleading conclusions. As a remedy we suggest the use of coherent risk measures - and namely the statistic known as Expected Shortfall - as a suitable alternative or complement for quantification of operational risk exposure. We demonstrate that application of Expected Shortfall in operational loss modeling is feasible and produces reasonable and consistent results. We also consider a variety of statistical techniques for modeling of underlying loss distribution and evaluate extreme value theory framework as the most suitable for this purpose. Using stress tests we further compare the robustness and consistency of selected models and their implied risk capital estimates...
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