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Multivariate Dependence Modeling using Copulas
Klaus, Marek ; Šopov, Boril (advisor) ; Gapko, Petr (referee)
Multivariate volatility models, such as DCC MGARCH, are estimated under assumption of multivariate normal distribution of random variables, while this assumption has been rejected by empirical evidence. Therefore, the esti- mated conditional correlation may not explain the whole dependence struc- ture, since under non-normality the linear correlation is only one of the de- pendency measures. The aim of this thesis is to employ a copula function to the DCC MGARCH model, as copulas are able to link non-normal marginal distributions to create corresponding multivariate joint distribution. The copula-based MGARCH model with uncorrelated dependent errors permits to model conditional cor- relation by DCC-MGARCH and dependence by the copula function, sepa- rately and simultaneously. In other words the model aims to explain addi- tional dependence not captured by traditional DCC MGARCH model due to assumption of normality. In the empirical analysis we apply the model on datasets consisting primarily of stocks of the PX Index and on the pair of S&P500 and NASDAQ100 in order to compare the copula-based MGARCH model to traditional DCC MGARCH in terms of capturing the dependency structure. 1
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PPP usage in the transport sector : theory vs. experience
Klaus, Marek ; Hájek, Filip (advisor) ; Mertlík, Pavel (referee)
The main goal of the thesis is to create Critical Success Factors (in other words factors, that are essential to achieve successful project) for the Public-Private Partnerships (PPP) projects in the sector of transport. For this purpose, the first part of the thesis is considering general theory, main characteristics, advantages and disadvantages of this alternative model of public infrastructure implementation. Following practical part is studying opportunities and risks of PPP project in the transport sector in term of experience and conclusions based on case study of a successful PPP project. The final part of thesis is constructing the Critical Success Factors and verifying them on certain PPP transport projects.
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Multivariate Dependence Modeling using Copulas
Klaus, Marek ; Šopov, Boril (advisor) ; Gapko, Petr (referee)
Multivariate volatility models, such as DCC MGARCH, are estimated under assumption of multivariate normal distribution of random variables, while this assumption has been rejected by empirical evidence. Therefore, the esti- mated conditional correlation may not explain the whole dependence struc- ture, since under non-normality the linear correlation is only one of the de- pendency measures. The aim of this thesis is to employ a copula function to the DCC MGARCH model, as copulas are able to link non-normal marginal distributions to create corresponding multivariate joint distribution. The copula-based MGARCH model with uncorrelated dependent errors permits to model conditional cor- relation by DCC-MGARCH and dependence by the copula function, sepa- rately and simultaneously. In other words the model aims to explain addi- tional dependence not captured by traditional DCC MGARCH model due to assumption of normality. In the empirical analysis we apply the model on datasets consisting primarily of stocks of the PX Index and on the pair of S&P500 and NASDAQ100 in order to compare the copula-based MGARCH model to traditional DCC MGARCH in terms of capturing the dependency structure. 1
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Multivariate Dependence Modeling Using Copulas
Klaus, Marek ; Šopov, Boril (advisor) ; Gapko, Petr (referee)
Multivariate volatility models, such as DCC MGARCH, are estimated under assumption of multivariate normal distribution of random variables, while this assumption have been rejected by empirical evidence. Therefore, the estimated conditional correlation may not explain the whole dependence structure, since under non-normality the linear correlation is only one of the dependency measures. The aim of this thesis is to employ a copula function to the DCC MGARCH model, as copulas are able to link non-normal marginal distributions to create corresponding multivariate joint distribution. The copula-based MGARCH model with uncorrelated dependent errors permits to model conditional cor- relation by DCC-MGARCH and dependence by the copula function, sepa- rately and simultaneously. In other words the model aims to explain addi- tional dependence not captured by traditional DCC MGARCH model due to assumption of normality. In the empirical analysis we apply the model on datasets consisting primarily of stocks of the PX Index and on the pair of S&P500 and NASDAQ100 in order to compare the copula-based MGARCH model to traditional DCC MGARCH in terms of capturing the dependency structure. 1
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PPP usage in the transport sector : theory vs. experience
Klaus, Marek ; Mertlík, Pavel (referee) ; Hájek, Filip (advisor)
The main goal of the thesis is to create Critical Success Factors (in other words factors, that are essential to achieve successful project) for the Public-Private Partnerships (PPP) projects in the sector of transport. For this purpose, the first part of the thesis is considering general theory, main characteristics, advantages and disadvantages of this alternative model of public infrastructure implementation. Following practical part is studying opportunities and risks of PPP project in the transport sector in term of experience and conclusions based on case study of a successful PPP project. The final part of thesis is constructing the Critical Success Factors and verifying them on certain PPP transport projects.
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