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This thesis investigates relationship between fuel (gasoline and diesel) prices in the Czech Republic and world crude oil prices over the period from 2004 to 2011. Using daily data we estimate an asymmetric error correction model and we find that in the short-run fuel prices are adjusted upwards to the long-run equilibrium faster than they are adjusted downwards to the equilibrium. However, the difference in responses is found to be not statistically significant.

The main objective of this thesis is to inspect the abilities of the Ising model to exhibit selected statistical properties, or stylized facts, that are common to a wide range of financial assets. The investigated properties are heteroskedasticity of returns, rapidly decaying linear autocorrelation, volatility clustering, heavy tails, negative skewness and non-Gaussianity of the return distribution. In the first part of the thesis, we test the presence of these stylized facts in S&P 500 daily returns over the last 30 years. The main part of the thesis is dedicated to the Ising model-based simulations and to discussion of the results. New features such as Poisson process governed lag or magnetisation dependent trading activity are incorporated in the model. We conclude that the Ising model is able to convincingly replicate most of the examined statistical properties while even more satisfactory results can be obtained with appropriate tuning. 1

The main purpose of the thesis is providing a detailed description of the clearing process on power spot market of European Energy Exchange (EEX), in view of the algorithmic methods employed. The thesis encompasses the mathematical formulation of the discrete optimization problem solved in the price determination process while the algorithm searches for such combination of block and hourly orders to be executed that would minimize the social cost associated to the chosen combination of orders. The market and network constraints typical for electricity trading are considered by the algorithm in the price determination process. The first chapter also provides the reader with basic knowledge of the trading on the Prague-based Power Exchange Central Europe. The last chapter contains an empirical analysis of the spot prices of the Czech and German power markets, which uses vector autoregression to test the hypothesis of dependance of the development of Czech spot energy prices on their German counterparts. Key words: EEX, PXE, spot markets, clearing process, COSMOS, discrete optimization, branch- and-bound

In this thesis we focus on modelling correlation between selected stock markets using high-frequency data. We use time-series of returns of following indices: FTSE, DAX PX and S&P, and Gold and Oil commodity futures. In the first part of our empirical work we compute daily realized correlations between returns of subject instruments and discuss the dynamics of it. We then compute unconditional correlations based on average daily realized correlations and using feedforward neural network (FFNN) to assess how well the FFNN approximates realized correlations. We also forecast daily realized correlations of FTSE:DAX and S&P:Oil pairs using heterogeneous autoregressive model (HAR), autoregressive model of order p (AR(p)) and nonlinear autoregressive neural network (NARNET) and compare performance of these models.

In this thesis, we analyze new possibilities in predicting daily ranges, i.e. the differences between daily high and low prices. The main focus of our work lies in investigating how models commonly used for daily ranges modeling can be enhanced to provide better forecasts. In this respect, we explore the added benefit of using more efficient volatility measures as predictors of daily ranges. Volatility measures considered in this work include realized measures of variance (realized range, realized variance) and range-based volatility measures (Parkinson, Garman & Klass, Rogers & Satchell, etc). As a subtask, we empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to simple daily ranges. As another venue of research in this work, we analyze the added benefit of slicing the trading day into different sessions based on trading activity (e.g. Asian, European and American session). In this setting we analyze whether whole-day volatility measures reliably aggregate information coming from all trading sessions. We are led by intuition that different sessions exhibit significantly different characteristics due to different order book thicknesses and trading activity in general. Thus these sessions are expected to provide valuable information concealed in...

This thesis focuses on several classes of risk measures, related axioms and properties. We have introduced and compared monetary, coherent, convex and deviation classes of risk measures and subsequently their properties have been discussed and in selected cases demonstrated on data. Furthermore the relatively promising and advanced class of risk measures, the spectral risk measures, has been introduced. In addition to that we have outlined selected topics from portfolio theory that are relevant for applications of selected risk measures and then derived theoretical solution of portfolio selection using chosen risk measures. In the end we have highlighted the potential consequences of improper employment of certain risk measures in portfolio optimization.

A time series has long range dependence if its autocorrelation function is not absolutely convergent. Presence of long memory in a time series has important consequences for consistency of several time series estimators and forecasting. We present a self-contained theoretical treatment of time series models necessary for study of long range dependence and survey a large list of parametric and semiparametric estimators of long range dependence. In a Monte Carlo study, we compare size and power properties of four estimators, namely R/S, DFA, GPH and Wavelet based method, when relying on asymptotic normality of the estimators and distributions obtained from the moving block bootstrap. We find out that the moving block bootstrap can improve the size of the R/S estimator. In general however, the moving block bootstrap did not perform satisfactorily for other estimators. GPH and Wavelet estimators offer the most reliable asymptotic confidence intervals.

This work studies the behaviour of the four most traded stocks on the Prague Stock Exchange from January 2007 to July 2010. Its main goal is to describe how the financial crisis influenced the Prague Stock Exchange. Employing standard statistical methods, ARMA, GARCH, and VAR models I examine on daily data the following phenomena: volatility, price jumps, the day of the week effect, validity of the efficient market hypothesis, and information flow between the stocks. The results imply that the financial crisis had stronger impact on the banking sector stocks than on other stocks. The crisis was mainly characterized by rapid growth in volatility and correlation between the stocks. It also influenced the information flow and the day of the week effect. However, the crisis did not trigger growth in the number of extreme price movements, and it did not cause the market to be less information efficient.

This thesis focuses on the theory of asset pricing models and their usage in the design of credit contracts. We describe the evolution of structural models start- ing from the basic Mertonian framework through the introduction of a default barrier, and ending with stochastic interest rate environment. Further, with the use of game theory analysis, the parameters of an optimal capital struc- ture and safety covenants are examined. To the author's best knowledge, the first EBIT-based structural model is built up that considers stochastic default barrier. This set-up is able to catch the different optimal capital structures in various business cycle periods, as well as bankruptcy decisions dependent on the state of the economy. The effects of an exogenous change in the risk-free interest rate on the asset value, probability of default, and optimal debt ratio are also explained. JEL Classification C73, G12, G32, G33 Keywords credit contracts, stochastic default barrier, asset pricing, EBIT-based models, struc- tural models Author's e-mail martin@dozsa.cz Supervisor's e-mail Karel-Janda@seznam.cz Abstrakt Tato práce se zabývá teoretickými modely pro oceňování finančních aktiv a je- jich použitím při návrhu optimálních úvěrových smluv mezi dlužníky a věřiteli. V první části je popsán...

Účelem práce je vytvořit obchodní strategii, která by využívala jevu "contrarian profitability". První část práce se věnuje samotnému odvozování strategie. Nejprve využijeme faktu, že strategie maximalizuje růst, právě pokud v každé periodě maximalizuje logaritmus hodnoty našeho bohatství. Poté log-optimální portfolio approximujeme portfoliem, které leží na efektivní hranici (termín z oblasti moderní teorie portfolia). První a druhé podmíněné momenty specifikujeme pomocí dynamického ekonometrického modelu. V druhé části prodiskutujeme nedostatky naší strategie a pomocí Monte Carlo simulací ji modifikujeme. V závěrečné části demonstrujeme životaschopnost strategie na historických datech. Za předpokladu neomezené páky a rozumných transakčních nákladu jsme byli schopni dosáhnout průměrného ročního zhodnocení kolem 24%.

In this thesis, we inquire interdependencies and comovements between CEE capital markets within each other. German market is also included in the analysis as a benchmark to CEE capital markets. We have chosen German capital market as it represents more developed market from the same geographical region. We study a unique high-frequency dataset of 5 minutes, 30 minutes and 1 hour data frequencies covering the the crisis period and post-crisis "tranquil" period. Daily data frequency is also involved in the analysis. Using different econometric techniques, we found no steady long-term relationships among stock market indices. The only strong relationship was detected between the DAX and WIG20 indices during both crisis and "tranquil" periods. The frequency of interactions changed across periods. The strongest interdependencies were recognized in 5 minute data frequency which indicates fast reactions between markets. Information inefficiency was revealed between markets according to cointegration tests in most cases.

The Least Weighted Squares (LWS) is a robust method for computing coefficients in linear regression models. An inherent problem of LWS is the complexity of its estimator and, consequently, the lack of an analytical solution or fast exact algorithms for its evaluation. To remedy this situation a novel exact algorithm running in polynomial time has been proposed. The algorithm implemented in MATLAB programming language has been employed for testing computationally more efficient non-exact LWS methods. In addition to many potential uses of LWS in robust econometrics (e.g. outlier diagnostics) the method has been applied to the problem of regression estimation in the presence of heteroscedasticity. It has been demonstrated that the combined use of the LWS estimator and White's test for heteroscedasticity significantly improves the efficiency of the robust regression estimation.

The aim of this thesis is to investigate the behavior of the interest rate transmission from money market rates to bank retail rates on the Czech banking market during the period from January 2004 to November 2010, and to detect potential changes occurred as a result of the current financial crisis. Using Ordinary Least Squares, Recursive Coefficients estimates and Impulse Response analysis we explore that bank retail rates reflect Pribor rate changes more strongly than changes in Euribor rates. We reveal that interest rate pass-through is rather incomplete and sluggish in the majority of cases and the adjustment level decreases noticeably during the period influenced by the financial crises.

This thesis focuses on the theory of asset pricing models and their usage in the design of credit contracts. We describe the evolution of structural models starting from the basic Mertonian framework through the introduction of a default barrier, and ending with stochastic interest rate environment. Further, with the use of game theory analysis, the parameters of an optimal capital structure and safety covenants are examined. To the author's best knowledge, the first EBIT-based structural model is built up that considers stochastic default barrier. This set-up is able to catch the different optimal capital structures in various business cycle periods, as well as bankruptcy decisions dependent on the state of the economy. The effects of an exogenous change in the risk-free interest rate on the asset value, probability of default, and optimal debt ratio are also explained.

The thesis shows the relationship between the persistence in the financial markets returns and their efficiency. It interprets the efficient markets hypothesis and provides various time series models for the analysis of financial markets. The concept of long memory is broadly presented and two main types of methods to estimate long memory are analysed - time domain and frequency domain methods. A Monte Carlo study is used to compare these methods and selected estimators are then used on real world data - exchange rate and stock market series. There is no evidence of long memory in the returns but the stock market volatilities show clear signs of persistence.

This thesis describes on one hand the evolution of the real estate market in The Czech Republic and more speci cally in Prague after the Velvet revolution in 1989 - the creation of new institutional and legislative environment. On the other hand this thesis focuses on the data of the rst decade of the 21st century to try and detect the symptoms that should have alarmed the soci- ety, banks and investors of the upcoming burst of the bubble as a consequence of the global nancial crisis in 2008. Finally we are going to setup a simple econometric model which would partly describe the in uence of some factors on the price of real estate.

This thesis aims to offer a comprehensive introduction into the topic of efficiency measurement in the public sector. Firstly, usual definitions and concepts of efficiency are introduced. Attention is then turned to the description of various factors and problems specific for public sector that are crucial to efficiency measurement. It is shown that these factors preclude the use of general (private sector) efficiency measurement methods or demand their modification. The most common methods of analysis are then introduced and their relative advantages and disadvantages in the environment of public sector are explained. Finally the thesis outlines the possible uses and benefits of efficiency measurement, including the use in the economic policy-making, but also points out the limits inherent to this analysis in the current stage of development.