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Model Train Controlled by Microcomputer
Látal, Tomáš ; Musil, Petr (referee) ; Zemčík, Pavel (advisor)
The goal of this bachelor's thesis is to verify whether and how it is possible to control a model railroad using Raspberry Pi. Another goal is to create a minimalistic model railroad control system using non-specialized equipment available to ordinary user, taking into account the possibility of controling this system using smartphones or tablets. The final result is the system implemented in C++ and Python~3 languages using Arduino Motor Shield R3 with H-bridge L298P.
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Intelligent control of train track model
Prokš, Jiří ; Bohrn, Marek (referee) ; Pavlík, Michal (advisor)
The aim of this thesis is design of elements intelligent control of train track model. The first part of the thesis is focused on principle of data communication. It explains principle data communication, modulation and interference. Practical part of the thesis is focused on designe of communication. Data communication is realized by infrared radiation. Practical part describes the funcional parts of system and their design, such as: control part, receiver and transmitter, switching elements and h-bridge. At the and of this thesis is described of control software. Properties of elements specialize for model of railway are considered in design. Summary of the design’s parameters, use, advantages and disadvantages are described at the end of this thesis.
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Šíření volatility na kryptoměnových trzích
Krampla, Dominik
This thesis investigated the identification of conditional volatility in cryptocurrency markets and explored how uncertainty spreads among various cryptocurrencies. Using GARCH family models, conditional volatility was modeled, and the DCC-GJR- GARCH(1,1) model was applied to identify the spread of conditional volatility, accounting for the impact of asymmetric shocks. The empirical analysis was based on high-frequency 15-minute data for five cryptocurrencies – Bitcoin, Ethereum, Ripple, Cardano, and Litecoin – from 23. April 2021 to 31. March 2022, with total number observations of 32 904 per cryptocurrency. The results suggest that uncertainty spreads most significantly between Bitcoin and Ethereum, while Ripple and Cardano are less affected by the spread of uncertainty from Bitcoin. The study also examines suitable combinations of cryptocurrency weights in various portfolio formation strategies, with the DCC-GJR-GARCH (1,1) strategy achieving the lowest risk.
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Model Train Controlled by Computer
Kolba, Zdeněk ; Kapinus, Michal (referee) ; Zemčík, Pavel (advisor)
Bachelor thesis „Computer controlled train model“ is focused on basic evaluation of terms and existing standards as basis for train control system design. Idea was to design basis solution ready for next extension. Open components, such as microprocessor unit, protocols or standards were preferred. The result of the thesis is an extensible application for controlling the railway model with support for simple automation.
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Model Train Controlled by Computer
Kolba, Zdeněk ; Kapinus, Michal (referee) ; Zemčík, Pavel (advisor)
Bachelor thesis "Computer controlled train model" is focused on basic evaluation of terms and existing standards as basis for train control system design. Idea was to design basis solution ready for next extension. Open components, such as microprocessor unit, protocols or standards were preferred. The result of the thesis is an extensible application for controlling the railway model with support for simple automation.
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Influence of stock market variables on correlations among S&P sectors
Coufal, Matěj ; Čech, František (advisor) ; Baruník, Jozef (referee)
This thesis investigates the influence of the exogenous variables (S&P 500 Index, 10-year US Treasury Note, crude oil, and CBOE Volatility Index (VIX)) on the dynamics of correlations among S&P sectors. We concentrate on daily and weekly investment horizons, and employ the bivariate Dynamic Conditional Correlation (DCC) model. Changes in correlations implied by the DCC model are further modelled using the exogenous variables. The results indicate that VIX has the best ability to predict future changes in correlations. An increase in VIX on day (week) t is expected to cause a rise in correlations on day (week) t + 1. Next, correlations of the Energy sector tend to increase in weeks when crude oil prices are falling. Further, correlations of the Information Technology sector are likely to increase on days of rising yield on the 10-year US Treasury Note. Although we detect a certain power to predict future changes in correlations, very little of these changes is actually explained. 1
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Multivariate GARCH
Maďar, Milan ; Hurt, Jan (advisor) ; Branda, Martin (referee) ; Mazurová, Lucie (referee)
4 Title: Multivariate GARCH Author: Mgr. Milan Mad'ar Department: Katedra pravděpodobnosti a matematické statistiky Abstract: This thesis will examine the regional and global linkages as evi- dence of integration of stock markets in Frankfurt, Amsterdam, Prague and the U.S. Therefore we will utilize the multivariate GARCH approach that investigates the dynamics of volatility transmission of related foreign exchange rates. Also, we will define three basic model classes. For each of the model classes a theoret- ical review, basic properties and estimation procedure with proofs are provided. We illustrate each approach by applying the models to daily market data. The two main aims of the thesis are to discuss and report the existence of regional and global stock markets linkages and provide a comparison of such multivariate GARCH models on the data sample. The main contribution of the thesis is that it treats the data in the context of real development in financial markets and takes into account the real situation during and after the financial crisis of 2008. We find out that the estimated time-varying conditional correlations indicate limited integration among the markets, which implies that investors can benefit from the risk reduction by investing in the different stock markets, especially during the crisis....
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Multivariate Financial Time Series
Veselý, Daniel ; Cipra, Tomáš (advisor) ; Kopa, Miloš (referee)
In this work we will describe methods for modeling multivariate financial time series. We will concentrate on both modeling expected value by multi- variate Box-Jenkins processes and primarily on modeling conditional corre- lations and volatility. Our main object will be DCC (Dynamic Conditional Correlation) model, estimation of its parameters and some other general- izations. Then we will programme DCC model in statistical software R and apply on real data. In applications we will concentrate on problem of high dimension of financial time series and on modeling conditional correlations data with outliers.
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Multivariate GARCH
Maďar, Milan ; Branda, Martin (referee) ; Mazurová, Lucie (referee)
4 Title: Multivariate GARCH Author: Mgr. Milan Mad'ar Department: Katedra pravděpodobnosti a matematické statistiky Abstract: This thesis will examine the regional and global linkages as evi- dence the integrated markets consist of stock markets in Frankfurt, Amsterdam, Prague the U.S. Therefore we will utilize the multivariate GARCH approach that investigates into the dynamics of volatility transmission of related foreign exchange rates. Also, we will define three basic model classes. For each of the model classes a theoretical review, basic properties and estimation procedure with proofs are provided. We illustrate approach by applying the models to daily market data. Our two main aims are discussing and reporting the existence of regional and global stock markets linkages and provide a comparison of such mul- tivariate GARCH models on the data sample. We find out that the estimated time-varying conditional correlations indicate limited integration among the mar- kets which implies that investors can benefit from the risk reduction by investing in the different stock markets especially during the crisis. Keywords: multivariate GARCH, VECH, BEKK, O-GARCH, GO-GARCH, CCC, DCC
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Multivariate generalized autoregressive conditional heteroscedasticity models
Nováková, Martina ; Pešta, Michal (advisor) ; Maciak, Matúš (referee)
This master thesis deals with extension of the univariate GARCH model to multivari- ate models. We present individual models and deal with methods of their estimation. Then we describe some statistical tests for diagnosting the models. We have programmed in the statistical software R one of them - the Ling-Li test. Afterwards we apply selected models to real data of stock market index S&P 500, stock market index Russell 2000 and stocks of crude oil. For the GO-GARCH model, we compare all available estimation methods and show their differences. Then we compare the results of all models with each other and also with univariate models in terms of estimates of conditional variances, estimates of conditional correlations and also in terms of computational complexity. 1
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