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Granger's causality in financial time series
Marčiny, Jakub ; Voříšek, Jan (advisor) ; Lachout, Petr (referee)
The bachelor thesis discusses causality in multiple time series. Granger causality, along with its more general counterparts instantaneous causality and multistep causality, are utilized to study the mutual influence of the individual components of a multiple time series. These concepts are investigated within the framework of vector autoregressive models VAR. After the introduction of basic definitions and facts, the construction of VAR model is described including methods for order selection and verification. Subsequently, causal relations within the model are examined. Finally, empirical analysis of real financial market data is performed using tests procedures programmed with computational software Mathematica.
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Phillips curve verification by time series analysis of Czech republic and Germany
Král, Ondřej ; Arltová, Markéta (advisor) ; Blatná, Dagmar (referee)
Government fiscal and monetary policy has long been based on the theory that was neither proven nor refuted since its origination. The original form of the Phillips curve has undergone significant modifications but its relevance remains questionable. This thesis examines the correlation between inflation and unemployment observed in the Czech Republic and Germany over the last twenty years. The validity of the theory is tested by advanced methods of time series analysis in the R environment. All the variables are gradually tested which results in the assessment of the correlation between the time series. The outcome of the testing is presented for both countries and a comparison at international level is drawn. Is is discovered that both of the countries have dependencies in their data. Czech republic has significant dependency in both ways, for Germany is the dependency significantly weaker and only in one way.
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Granger's causality in financial time series
Marčiny, Jakub ; Voříšek, Jan (advisor) ; Lachout, Petr (referee)
The bachelor thesis discusses causality in multiple time series. Granger causality, along with its more general counterparts instantaneous causality and multistep causality, are utilized to study the mutual influence of the individual components of a multiple time series. These concepts are investigated within the framework of vector autoregressive models VAR. After the introduction of basic definitions and facts, the construction of VAR model is described including methods for order selection and verification. Subsequently, causal relations within the model are examined. Finally, empirical analysis of real financial market data is performed using tests procedures programmed with computational software Mathematica.
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