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Methods of dynamical analysis of portfolio composition
Meňhartová, Ivana ; Hanzák, Tomáš (advisor) ; Cipra, Tomáš (referee)
Title: Methods of dynamical analysis of portfolio composition Author: Ivana Meňhartová Department: Department of Probability and Mathematical Statistics Supervisor: Mgr. Tomáš Hanzák, KPMS, MFF UK Abstract: In the presented thesis we study methods used for dynamic analysis of portfolio based on it's revenues. The thesis focuses on Kalman filter and local- ly weighted regression as two basic methods for dynamic analysis. It describes in detail theory for these methods as well as their utilization and it discusses their proper settings. Practical applications of both methods on artificial data and real data from Prague stock-exchange are presented. Using artificial data we demonstrate practical importance of Kalman filter's assumptions. Afterwards we introduce term multicolinearity as a possible complication to real data applicati- ons. At the end of the thesis we compare results and usage of both methods and we introduce possibility of enhancing Kalman filter by projection of estimations or by CUSUM tests (change detection tests). Keywords: Kalman filter, locally weighted regression, multicollinearity, CUSUM test
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Robustification of statistical and econometrical regression methods
Jurczyk, Tomáš ; Víšek, Jan Ámos (advisor) ; Hlávka, Zdeněk (referee) ; Malý, Marek (referee)
Title: Robustification of statistical and econometrical regression methods Author: Mgr. Tomáš Jurczyk Department: Department of probability and mathematical statistics Supervisor: prof. RNDr. Jan Ámos Víšek CSc., IES FSV UK Praha Abstract: Multicollinearity and outlier presence are two problems of data which can occur during the regression analysis. In this thesis we are interested mainly in situations where combined outlier-multicollinearity problem is present. We will show first the behavior of classical methods developed for overcoming one of these problems. We will investigate the functionality of methods proposed as robust multicollinearity detectors as well. We will prove that proposed two-step procedures (in one step typically based on robust regression methods) are failing in outlier detection and therefore also multicollinearity detection, if the strong multicollinearity is present in the majority of the data. We will propose a new one-step method as a candidate for the robust detector of multicollinearity as well as the robust ridge regression estimate. We will derive its properties, behavior and propose the diagnostic tools derived from that method. Keywords: multicollinearity, outliers, robust detector of multicollinearity, ro- bust ridge regression 1
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Methods of dynamical analysis of portfolio composition
Meňhartová, Ivana ; Hanzák, Tomáš (advisor) ; Cipra, Tomáš (referee)
Title: Methods of dynamical analysis of portfolio composition Author: Ivana Meňhartová Department: Department of Probability and Mathematical Statistics Supervisor: Mgr. Tomáš Hanzák, KPMS, MFF UK Abstract: In the presented thesis we study methods used for dynamic analysis of portfolio based on it's revenues. The thesis focuses on Kalman filter and local- ly weighted regression as two basic methods for dynamic analysis. It describes in detail theory for these methods as well as their utilization and it discusses their proper settings. Practical applications of both methods on artificial data and real data from Prague stock-exchange are presented. Using artificial data we demonstrate practical importance of Kalman filter's assumptions. Afterwards we introduce term multicolinearity as a possible complication to real data applicati- ons. At the end of the thesis we compare results and usage of both methods and we introduce possibility of enhancing Kalman filter by projection of estimations or by CUSUM tests (change detection tests). Keywords: Kalman filter, locally weighted regression, multicollinearity, CUSUM test
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Does campaign spending have any impact on election outcome ?
Dušek, Ondřej ; Hronza, Martin (advisor) ; Kovanda, Lukáš (referee)
This Thesis analyzes the impact of campaign spending of political parties on election outcome. The Thesis uses data from the Parliamentary library of the Chamber of Deputies of the Czech republic, annual reports of political parties and from the Czech Statistical Office. For the first estimation, a method of Ordinary Least Square is used, consequently the equation of the model is edited using instrumental variables, in order to eliminate endogeneity. A new regression is estimated using Two-Stage Least Squares method. After the editing, all the explanatory variables are corelated and insignificant, although, the model itself works. In the end, this work did not succeed in measuring a predicted positive impact of campaign spending on election outcome. This "non-result result" shows the importance of an extensive dataset, which would allow an alternative approach to modelling and eliminating strong multicollinearity in the model.
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Dynamické modely inflace
Sodoma, Jan ; Hušek, Roman (advisor) ; Lejnarová, Šárka (referee)
In the first part, inflation is desribed theoretically. This part is about cost-push inflation, demand-pull inflation, galloping inflation, hyperinflation, monetarist and keneynesian view on inflation, issues in measuring inflation, effects of inflation and controlling inflation. Second part is empiric research. Inflation is endogenous variable. Price of petrol natural95 and monetary aggregate M2 are delayed exogenous variables. Object of analyse are relations between these variables: Correlation coefficient, F-test, t-tests, multicollinearity, autocorrelation.
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