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Deepening relations between the Czech Republic and selected priority countries as part of bilateral development cooperation
Havel, Štěpán ; Tesař, Jakub (advisor) ; Kučerová, Irah (referee)
This thesis deals with the deepening of relations between the Czech Republic and its priority countries (for the period 2006-2010) within the framework of Czech bilateral development cooperation. The main research question of the thesis is the influence of development cooperation on the expansion of cooperation between the Czech Republic and its priority countries. In the theoretical part of the thesis, the main assumptions concerning the deepening of cooperation are presented. The main theoretical framework is the spillover theory. The main hypotheses of this theory are presented and applied to the selected case. The observed areas in which cooperation is expected to increase are the political sphere, the economic sphere, culture sphere and the military sphere. For every sphere was chosen measurable indicator. Paired countries with comparable conditions were selected as priority countries. The conditions on the basis of which the countries are paired are comparable history, GDP per capita, population size and trade with the Czech Republic before the period under review. The method used to answer the research question is regression analysis. In this case, the dependent variable is the growth of the selected indicators for the selected regions. The independent variable is development cooperation,...
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Conditional quantile models for asset returns
Havel, Štěpán ; Baruník, Jozef (advisor) ; Fanta, Nicolas (referee)
The literature related to Value at Risk estimation is rich in general. However, majority of papers written on this subject concentrates on the unconditional non-parametric or parametric approach to VaR modelling. This thesis focuses on direct conditional VaR estimation using quantile regression. Thereby im- posing no restrictions on the return distribution. We use daily volatility mea- surements for individual stocks in S&P 500 index and quantile regress them on one-day ahead returns of the entire index. Depending on the quantile selected this estimation produces different confidence levels of Value at Risk. In order to minimize complexity of the final model, regularization methods are applied. To the author's knowledge this specific methodology has not yet been applied in any paper. The main objective is to investigate whether this approach is able to produce sound VaR estimates comparable with different methods usu- ally applied. Our result suggests that quantile regression extended with lasso regularization can be used to produce sound one-day-ahead Value at Risk es- timates. JEL Classification C22, C58, G15 Keywords volatility, quantile regression, VaR, GARCH Title Conditional quantile models for asset re- turns Author's e-mail havel.stepan@gmail.com Supervisor's e-mail barunik@fsv.cuni.cz
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