The main goal of this thesis is to suggest an appropriate approach to interest rate risk modeling in the Czech financial market environment in various situations. Three distinct periods are analyzed. These periods, which are the period before the global financial crisis, period during the financial crisis and in the aftermath of the global financial crisis and calming subsequent debt crisis in the eurozone, are characterized by different evaluation of liquidity and credit risk, different relationship between financial variables and market participants and different degree of market regulations. Within this goal, an application of the BGM model in the Czech financial market environment is crucial. Use of the BGM model for the purpose of predicting a dynamics of a yield curve is not very common. This is firstly due to the fact that primary use of this model is a valuation of interest rate derivatives while ensuring the absence of arbitrage and secondly its application is relatively difficult. Nevertheless, I apply the BGM model to obtain predictions of the probability distributions of interest rates in the Czech and eurozone market environment, because its complexity, direct modeling of a yield curve based on market rates and especially a possibility of parameter estimation based on current swaptions volatilities quotations may lead to a significant improvement of predictions. This improvement was also confirmed in this thesis. Use of swaptions volatilities market quotations is especially useful in the period of unprecedented mone- tary easing and increased number of central banks and other regulators interventions into financial markets that occur after the financial crisis, because it reflects current market expectations which also include future interventions. As a consequence of underdevelopment of the Czech financial market there are no market quotations of Czech koruna denominated swaptions volatilities. I suggest their approximations based on quotations of euro denominated swaptions volatilities and also using volatilities of koruna and euro forward rates. Use of this approach ensures that predictions of the Czech yield curve dynamics contain current market expectations. To my knowledge, any other author has not presented similar application of the BGM model in the Czech financial market environment. In this thesis I further predict a Czech and Euro area money market yield curve dynamics using the CIR and the GP models as representatives of various types of interest rates models to compare these predictions with BGM predictions. I suggest a comprehensive system of three criteria, based on comparison of predicti- ons with reality, to describe a predictive power of selected models and an appropria- teness of their use in the Czech market environment during different situations in the market. This analysis shows that predictions of the Czech money market yield curve dynamics based on the BGM model demonstrate high predictive power and the best 8 quality in comparison with other models. GP model also produces relatively good qua- lity predictions. Conversely, predictions based on the CIR model as a representative of short rate model family completely failed when describing reality. In a situation when the economy allows negative rates and there is simultaneously a significant likelihood of their implementation, I recommend to obtain predictions of Czech money market yield curve dynamics using GP model which allows existence of negative interest rates. This analysis also contains a statistical test for validating the predictive power of each model and information on other tests. Berkowitz test rejects a hypothesis of accurate predictions for each model. However, this fact is common in real data testing even when using relatively good model. This fact is especially caused by difficult fulfilment of test conditions in real world. To my knowledge, such an analysis of the predictive power of selected interest rate models moreover in the Czech financial market environment has not been published yet. The last goal of this thesis is to suggest an appropriate approach to obtaining pre- dictions of Czech government bonds risk premium dynamics. I define this risk premium as a difference between government bond yields and fixed rate of CZK IRS with the same length. I apply the GP model to describe the dynamics of this indicator of the Czech Republic credit risk. In order to obtain a time series of the risk premium which are necessary for estimation of GP model parameters I firstly estimate yield curves of Czech government bonds using Svensson model for each trading day since 2005. Resulting si- mulations of risk premium show that the GP model predicts the real development of risk premiums of all maturities relatively well. Hence, the proposed approach is suitable for modeling of Czech Republic credit risk based on the use of information extracted from financial markets. I have not registered proposed approach to risk premium modeling moreover in the Czech financial market environment in other publications.

The thesis deals with the rating agencies as the significant and influential subjects on the financial markets. The main aim of the thesis is a description of rating, especially sovereign rating and its impact in international investment environment. The thesis also focuses on individual approaches of agencies in the rating process as well as distinctive legislative framework and efforts to change the regulation of agencies since 2007. The thesis is divided into four chapters. The first chapter deals with the definition and specification of rating and its alternatives. The second chapter deals with the rating agencies as the specific entities, their oligopolistic market position and criteria for awarding rating marks. The third chapter specifies the legislative measures relating to credit rating agencies and the major post-crisis changes. Lastly, the fourth chapter analyzes sovereign rating and its importance in the process of international investment.

The financial markets have undergone a very dramatic evolution in the last 100 years. Multiple attempts to regulate the evolution were part of the development. They reacted mostly on economic crises, whether on the capital market or in the banking sector. This thesis describes the developments in the US and European financial markets. Most important part of the thesis contains an analysis of the causes, course of action and impacts of the financial crisis, it evaluates current crisis in the terms of functionality of the financial crisis regulation system as well as suggestions and discussions how tho improve regulation in the USA and the European Union.

This thesis focuses on automated trading systems for financial markets trading. It describes theoretical background of financial markets, different technical analysis approaches and theoretical knowledge about automated trading systems. The output of the present paper is a diversified portfolio comprising four different investment models aimed to trading futures contracts of cocoa and gold. The portfolio tested on market data from the first quarter 2013 achieved 46.74% increase on the initial equity. The systems have been designed in Adaptrade Builder software using genetic algorithms and subsequently tested in the MetaTrader trading platform. They have been finally optimized using sensitivity analysis.

This diploma thesis is focused on artificial intelligence and its application in financial markets. For the prediction values and trends of selected exchange rates are used artificial neural networks. Artificial neural network is created in Matlab. This solution is subsequently evaluated.

This thesis focuses on automated trading systems for foreign exchange markets. It describes theoretical background of financial markets, technical analysis approaches and theoretical knowledge about automated trading systems. The output of the thesis is set of two automated trading systems built for trading the most liquid currency pairs. The process of developing automated trading system as well as its practical start up in Spartacus Company Ltd. is documented in the form of project documentation. The project documentation captures choosing necessary hardware components, their installation and oricess of ensuring smooth operation, as well as the selection and installation of the necessary software resources. In the Adaptrade Builder enviroment there has been shown the process of developing strategies and consequently theirs characteristics, performance, as well as a graph showing the evolution of the account at the time. Selected portfolio strategy has been tested in the MetaTrader platform and in the end of the thesis is offered assessing achievements and draw an overall conclusion.

Over a long period of time, researchers have investigated the efficiency of financial markets. The widely accepted theory of the subject is the Efficient Market Hypothesis, which states that prices of financial assets are set efficiently. A common way to test this hypothesis is to analyze the returns generated by technical trading rules which uses historical prices in an attempt to predict future price development. This is also what this study aims to do. Using adjusted daily closing prices ranging over 2007 to 2019 for 5120 stocks listed on the U.S stock market, this study tests a momentum trading strategy called the stochastic oscillator in an attempt to beat a buy and hold strategy of the Russel 3000 stock market index. The stochastic oscillator is constructed in three different ways, the Fast%K, the Fast%D and the Slow%D, the difference being that a smoothing parameter is used in the Fast%D and Slow%D in an attempt to reduce the number of whiplashes or false trading signals. The mean returns of the technical trading strategies are tested against the mean returns of the buy and hold strategy using a non-parametric bootstrap methodology and also, the risk adjusted returns in terms of Sharpe Ratios are compared for the different strategies. The results find no significance difference between the mean returns of the buy and hold strategy and any of the technical trading strategies. Further, the buy and hold strategy delivers a higher risk adjusted return compared to the technical trading strategies, although, only by a small margin. Regarding the smoothing parameter applied to the strategies, it seems to fulfill its purpose by reducing the number of trades and slightly increasing the mean returns of the technical trading strategies. Finally, for deeper insight in the subject, a reading of "The efficiency of financial markets: A dual momentum trading strategy on the Swedish stock market" by Netzén Örn (2018) is recommended.

In this study, the authors examined the relationship between crude oil price and the Swedish and Norwegian stock markets. Using linear regression models the authors found that the Swedish stock market and Norwegian stock market both have a positive relation with crude oil price. This supports the hypothesis that crude oil price has a positive impact on Norwegian stock market, since Norway is an oil exporting country. However, this result contradicts a hypothesis of a negative relationship for an oil importing country like Sweden. The authors also looked into the relationship between exchange rates (Swedish krona and Norwegian krone) and oil price, which reveals that oil price is significantly negatively correlated with Swedish krona and Norwegian Krone. The study contributes with evidence from underexplored regions of the world.

Artificial intelligence (AI) is becoming smarter and faster and firms all over the world have entered a race to reap the benefits, investing in AI projects at an increasing rate. The benefits of AI are seemingly plenty and the value of AI in various business processes is often emphasized. However, seldom is the actual value of AI discussed in quantitative terms. Among the first to do so, this study finds a positive value of AI investments. Based on 76 announcements from 43 sample firms in the Nordic markets, findings show a significant positive impact of AI announcements on abnormal returns, with a mean of 0.91% for the event day and 1.62% for the event window of +/- one day. Further, this is to the best of our knowledge the first study ever to investigate abnormalities in stock price volatility after the announcements of AI investments. The study shows a cumulative abnormal volatility of 6.47 for the event window +/- ten days. The findings in this study indicate that AI holds significant value and that AI investments cause increased stock market risk as measured by abnormal volatility for the announcing firms.

Can the law of the natural distribution of random numbers expose malice in financial markets? This thesis aims to analyze the indices S&P 500 and STOXX 600, in an effort to identify days in which behavior in the market was the result of financial manipulation or non normal market movements. What was discovered by extending a previous study [10], was that we could accurately identify many days in which the market crashed or was affected by malpractice similar to the events in the 2007-2008 financial crisis.