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Essays on Data-driven, Non-parametric Modelling of Time-series
Hanus, Luboš ; Vácha, Lukáš (advisor) ; Witzany, Jiří (referee) ; Ellington, Michael (referee) ; Trimborn, Simon (referee)
This thesis consists of four contributions to the literature on data-driven and non-parametric modelling of time series. In the first paper, we study the synchronisation of business cycles and propose a multivariate co-movement measure based on time-frequency cohesion. We suggest that economic inte- gration may lead to increased co-movement of business cycles, which may reflect the benefits of convergence and coordination of economic policies. The second paper presents a new methodology for identifying persistence in macroeconomic variables. Using time-varying frequency response func- tions, we identify heterogeneous persistence effects in US macroeconomic variables. The third and fourth papers propose data-driven techniques for probabilistic forecasting of time series using deep learning. We introduce a multi-output neural network that selects the most appropriate distribution for the data. The distributional neural network is valuable for modelling data with non-linear, non-Gaussian and asymmetric structures. The third paper demonstrates the usefulness of the method by estimating information-rich macroeconomic fan charts and distributional forecasts of asset returns. In the last paper, we present the distributional neural network to obtain the proba- bility distribution of electricity price...
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Detection of the Cars Approaching the Crossroad
Vácha, Lukáš ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
Traffic monitoring using computer vision is becoming the desired system in practice. It allows nondestructive installation and also is very useful in many applications. This thesis focuses on automatic detection of vehicles approaching to a crossroads. This work also includes description of selected methods for detecting moving vehicles and the way of tracking them. On the basis of these methods is designed application that is implemented and tested in different lighting and weather conditions and various direction of approaching vehicles.
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Two-axis Stabilized Aerial Photography Platform
Vácha, Lukáš ; Hasmanda, Martin (referee) ; Strašil, Ivo (advisor)
This diploma thesis deals with design and realization of control board with controlling program for stabilization platform application. Thesis is splitted in to six parts. In first part of thesis are summarized required parameters and properties of proposed system together with explanation of necessary theoretical basics. In second part of thesis is made analysis of sensors which are designated for sensing necessary magnitudes. Namely then magnetometer, accelerometer, gyroscope. For every sensor is there made analysis of influence caused by parasitic effects. In conclusion of second part is made choice of concrete sensors by choosing sensory module. Third part deals with conception of mechanical solution. Fourth part of thesis deals with design and construction of control board and also with description of circuit functional blocks. This is followed with fifth part which describing program equipment of board with setting up sensory module. In last part of thesis are described conclusions of testing.
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Synchronization Signal Generator for Musical Applications
Vácha, Lukáš ; Přinosil, Jiří (referee) ; Schimmel, Jiří (advisor)
This bachelor thesis deals with analysis of MIDI´s protocol and especially by the implementation of synchronization methods. The aim of this thesis is suggestion and creating of a prototype of equipment, which transmits one sort of synchronization code and enables its direct extension on produced equipment. First part of solution of this thesis deals with MIDI in general, its history, hardware definition and protocol analysis, especially by analysis of methods and by types of synchronization. The main attention is devoted to MIDI Click/SPP code, which is also implemented in created prototype. The second part of this thesis deals with proposal of hardware solution of prototype, where is parsed a principle of involvement and used components in detail. The third part describes software proposal of solving from necessary initializations of microcontrollers up to operations with individual circumferences. There is also analyzed a customer service. Last, fourth part of thesis deals with realization of prototype, its revival and testing. There is stated value of properties of prototype, some suggestions for possible extension and summary of achieved aims of this thesis in the end.
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Transition Periods and Long Memory Property
März, Jan ; Vácha, Lukáš (advisor) ; Polák, Petr (referee)
This thesis examines the relationship between the distribution of structural breaks within a data sample and the estimated parameter of long memory. We use Monte Carlo simulations to generate data from processes with specific values of parameters. Subsequently we join the data with various shifts to mean and examine how the estimates of the parameters vary from their true values. We have discovered that the overestimate of the long memory parameter is higher when the breaks are clustered together. It further increases when the signs of the shifts are positively correlated within the clusters while negative correlation reduces the bias. Our findings enable the improvement of robustness of estimators against the presence structural breaks. Powered by TCPDF (www.tcpdf.org)
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Geopolitical risk and financial markets: trends, co-movements and effects
Jarina, Vesna ; Horváth, Roman (advisor) ; Vácha, Lukáš (referee)
This thesis explores the impact of geopolitical risk on cross-market co-movements in both global stock markets and regional foreign exchange markets over the period of 1995-2023. Employing two novel approaches, namely the return co- exceedances within the quantile regression framework and the GDCCX-GARCH model, our findings reveal that geopolitical risk has a tendency to weaken ex- treme return co-exceedances and dynamic conditional correlations within these markets, although there are few exceptions from this behaviour. Additionally, we emphasize the significance of considering geopolitical risk when building portfolio strategies by providing evidence for gold's hedging and safe haven properties, the resilience of clean energy investments, and the rise in crude oil prices in response to heightened geopolitical risk.
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Effect of covered calls on portfolio performance
Ježo, Tomáš ; Polák, Petr (advisor) ; Vácha, Lukáš (referee)
This thesis aims to evaluate the performance of a covered call strategy writ- ten on Exchange-traded funds compared to a buy-and-hold strategy of the Exchange-traded fund on the US stock market. The strategy is constructed us- ing at-the-money, two-percent and five-percent out-of-the-money call options. The premium for the former is taken from historical market data and for the latter two calculated using the Black-Scholes-Merton formula adjusted for div- idends. The results further provide a two-period distinction to better account for di erent market periods, namely Covid-19 and the geopolitical conflict in Ukraine. The results fail to show evidence of a significant di erence between a covered call strategy and the buy-and-hold strategy. However, we provide possible applications of the strategy in certain market settings. The perfor- mance is evaluated on the basis of annualized returns and standard deviation, as ratios based on the mean-variance framework are omitted due to possible bias of negatively skewed distribution of returns of the covered call strategy. JEL Classification G10, G11, G12, G13, C02 Keywords Covered calls, ETF, Black-Scholes model, Op- tions pricing, Portfolio performance Title E ect of covered calls on portfolio performance
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Predicting stock price movements from financial news using deep neural networks
Kramoliš, Richard ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Financial media are an important source of information and many articles about companies and stocks are released every day. This thesis assesses the informa- tion value of the articles and utilizes these articles for the stock price move- ment prediction task. For this purpose, models with transformer architecture are used, specifically Bidirectional Encoder Representations from Transform- ers. These models are able to process the text data and create the contextual representation of the text sequence. After adding the classification layer, the models are applied for the stock price movement predictions. The thesis evalu- ates multiple models including different techniques and parameters to find the best performing model. It focuses on two data filters that are expected to de- crease the noise in the data. Moreover, it introduces a new method to recognize the company of interest. As a result of the hyperparameter optimization, the final model is constructed. JEL Classification C45, C51, C52, C53, G11, G14, G17 Keywords BERT, Transformer, Financial Articles, Stock Trading Title Predicting stock price movements from financial news using deep neural networks
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Price Prediction Using Machine Learning Methods on the European Market of Used Cars
Dvořáček, Petr ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
This master's thesis proposes accurate predictions of the prices of used cars. It builds its fundamentals on the available research and broadens the academic literature by applying several modern techniques to the European market. Us- ing machine learning models and unique data, high accuracy of predictions was obtained. The precise prediction of the residual value of a used car might benefit both the buyers and the sellers, and also reduce market inefficiencies. We are not aware of any similar work in the particular field focusing on the European market. An application programming interface (API) was exploited in order to col- lect the data. Therefore, a large set of data consisting of 221,704 used car classifieds was gathered and used in various models (MLR, PCR, LASSO, De- cision Tree, Random Forests, and ANNs). This study aims to find the most precise model for estimating the prices of used cars with the help of several performance statistics (R2, RMSE, and MAE). We support the available lit- erature as the random forest approach provided the highest accuracy when predicting the used car prices. A model using ANNs seemed to be the second best in terms of predictive performances, however, required comparably much more computing power. The effects of various attributes of used vehicles on their...
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Machine Learning Methods in Payment Card Fraud Detection
Sinčák, Jan ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Protection of clients from fraudulent transactions is a complicated task. Banks tend to rely on rule-based systems which require manual creation of rules to identify fraud. These rules have to be set up by employees of the bank who need to look for any trends in fraudulent transactions themselves. This thesis deals with the problem of detection of fraudulent card transactions as it com- pares multiple machine learning models for fraud detection. These models can find complex relationships in the data and potentially outperform standard fraud detection systems, Logistic regression, neural network, random forest, and extreme gradient boosting (XGBoost) models are trained on a simulated dataset that closely follows properties of real card transactions. Performance of the models is measured by sensitivity, specificity, precision, AUC, and time to predict on the testing dataset. XGBoost shows the highest performance among the tested models. It is then compared to a standard fraud detection system used in a Czech bank. The bank system achieves higher specificity but XGBoost still shows promising performance. It is possible that certain machine learning models could outperform today's fraud detection systems if they are well-tuned. JEL Classification G21, K42 Keywords machine learning, card fraud, fraud...
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