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A time-varying copula approach to equity market contagion
Horáčková, Petra ; Baruník, Jozef (advisor) ; Buzková, Petra (referee)
The dependence structures in financial markets count among the most frequently discussed topics in the recent literature. However, no general consensus on modeling of the cross-market linkages has been reached. This thesis analyses the dependence structure and contagion in the financial markets in Central and Eastern Europe. Tail dependence, symmetry and dynamics of the dependence structure are examined. A conditional copula framework extended by recently developed dynamic generalized autoregressive score (GAS) model is used to capture the conditional time-varying joint distribution of stock market returns. Considering the Czech, Croatian, Hungarian, Austrian and Polish stock market indices over the 2005-2012 period, we find that time-varying Student's t GAS copula provides the best fit. The results show, that the degree of dependence increases substantially during the global financial crisis, having a direct impact on portfolio optimization.
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A time-varying copula approach to equity market contagion
Horáčková, Petra ; Baruník, Jozef (advisor) ; Buzková, Petra (referee)
The dependence structures in financial markets count among the most frequently discussed topics in the recent literature. However, no general consensus on modeling of the cross-market linkages has been reached. This thesis analyses the dependence structure and contagion in the financial markets in Central and Eastern Europe. Tail dependence, symmetry and dynamics of the dependence structure are examined. A conditional copula framework extended by recently developed dynamic generalized autoregressive score (GAS) model is used to capture the conditional time-varying joint distribution of stock market returns. Considering the Czech, Croatian, Hungarian, Austrian and Polish stock market indices over the 2005-2012 period, we find that time-varying Student's t GAS copula provides the best fit. The results show, that the degree of dependence increases substantially during the global financial crisis, having a direct impact on portfolio optimization.
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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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Modelling natural catastrophes in insurance
Varvařovský, Václav ; Zimmermann, Pavel (advisor) ; Justová, Iva (referee)
Quantification of risks is one of the pillars of the contemporary insurance industry. Natural catastrophes and their modelling represents one of the most important areas of non-life insurance in the Czech Republic. One of the key inputs of catastrophe models is a spatial dependence structure in the portfolio of an insurance company. Copulas represents a more general view on dependence structures and broaden the classical approach, which is implicitly using the dependence structure of a multivariate normal distribution. The goal of this work, with respect to absence of comprehensive monographs in the Czech Republic, is to provide a theoretical basis for use of copulas. It focuses on general properties of copulas and specifics of two most commonly used families of copulas -- Archimedean and elliptical. The other goal is to quantify difference between the given copula and the classical approach, which uses dependency structure of a multivariate normal distribution, in modelled flood losses in the Czech Republic. Results are largely dependent on scale of losses in individual areas. If the areas have approximately a "tower" structure (i.e., one area significantly outweighs others), the effect of a change in the dependency structure compared to the classical approach is between 5-10% (up and down depending on a copula) at 99.5 percentile of original losses (a return period of once in 200 years). In case that all areas are approximately similarly distributed the difference, owing to the dependency structure, can be up to 30%, which means rather an important difference when buying the most common form of reinsurance -- an excess of loss treaty. The classical approach has an indisputable advantage in its simplicity with which data can be generated. In spite of having a simple form, it is not so simple to generate Archimedean copulas for a growing number of dimensions. For a higher number of dimensions the complexity of data generation greatly increases. For above mentioned reasons it is worth considering whether conditions of 2 similarly distributed variables and not too high dimensionality are fulfilled, before general forms of dependence are applied.
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