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Sovereign credit risk drivers in a spatial perspective.
Záhlava, Josef ; Gapko, Petr (advisor) ; Janský, Petr (referee)
This thesis analyses what drives sovereign credit risk when contagion is con- trolled for. CDS spreads are used as a measure of credit risk and bond yields are used to estimate interconnectedness of the examined countries. The main contribution lies in the use of high-frequency data and a robust wavelet based estimator in addition to spatial econometric model. The aim of this thesis is to test for presence of contagion and to evaluate which fundamentals are decisive for market perception of sovereign credit risk. Another goal is to evaluate the possibility of a structural break caused by the Greek debt restructuring. The results show that the restructuring did bring change. Contagion is present during the post-crisis period and it diminishes as the economies recover. Sim- ilarly, fundamentals are of higher importance in the post-crisis period when compared with the following period. JEL Classification C22, C31, C33, G01, G32, G33 Keywords spatial econometrics, CDS spreads, sovereign credit risk, financial contagion, realised covari- ance Author's e-mail josef.zahlava@gmail.com Supervisor's e-mail petr.gapko@seznam.cz
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Sovereign credit risk drivers in a spatial perspective.
Záhlava, Josef ; Gapko, Petr (advisor) ; Janský, Petr (referee)
This thesis analyses what drives sovereign credit risk when contagion is con- trolled for. CDS spreads are used as a measure of credit risk and bond yields are used to estimate interconnectedness of the examined countries. The main contribution lies in the use of high-frequency data and a robust wavelet based estimator in addition to spatial econometric model. The aim of this thesis is to test for presence of contagion and to evaluate which fundamentals are decisive for market perception of sovereign credit risk. Another goal is to evaluate the possibility of a structural break caused by the Greek debt restructuring. The results show that the restructuring did bring change. Contagion is present during the post-crisis period and it diminishes as the economies recover. Sim- ilarly, fundamentals are of higher importance in the post-crisis period when compared with the following period. JEL Classification C22, C31, C33, G01, G32, G33 Keywords spatial econometrics, CDS spreads, sovereign credit risk, financial contagion, realised covari- ance Author's e-mail josef.zahlava@gmail.com Supervisor's e-mail petr.gapko@seznam.cz
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Portfólio Value at Risk a Expected Shortfall s použitím vysoko frekvenčních dat
Zváč, Marek ; Fičura, Milan (advisor) ; Janda, Karel (referee)
The main objective of this thesis is to investigate whether multivariate models using Highfrequency data provide significantly more accurate forecasts of Value at Risk and Expected Shortfall than multivariate models using only daily data. Our objective is very topical since the Basel Committee announced in 2013 that is going to change the risk measure used for calculation of capital requirement from Value at Risk to Expected Shortfall. The further improvement of accuracy of both risk measures can be also achieved by incorporation of high-frequency data that are rapidly more available due to significant technological progress. Therefore, we employed parsimonious Heterogeneous Autoregression and its asymmetric version that uses high-frequency data for the modeling of realized covariance matrix. The benchmark models are chosen well established DCC-GARCH and EWMA. The computation of Value at Risk (VaR) and Expected Shortfall (ES) is done through parametric, semi-parametric and Monte Carlo simulations. The loss distributions are represented by multivariate Gaussian, Student t, multivariate distributions simulated by Copula functions and multivariate filtered historical simulations. There are used univariate loss distributions: Generalized Pareto Distribution from EVT, empirical and standard parametric distributions. The main finding is that Heterogeneous Autoregression model using high-frequency data delivered superior or at least the same accuracy of forecasts of VaR to benchmark models based on daily data. Finally, the backtesting of ES remains still very challenging and applied Test I. and II. did not provide credible validation of the forecasts.
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