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The Impact of an Announcement of a New Car Model on the Price of Stocks of Automobile Companies
Micenko, Ján ; Krištoufek, Ladislav (advisor) ; Benčík, Daniel (referee)
This work studies the impact of an introduction of a new car model on the stocks of the introducing company and its rivals and also the impact of an earnings announcement on the stocks of the introducing company. I use two different approaches to explore these effects, one focusing on the stock returns through the CAPM and the other focusing on the volatility of stocks using GARCH model. I found that the new model introduction has a significant positive effect on the returns of stocks of the announcing company but I found no definite effect on the returns of stocks of the competition. Moreover, I found that the new model introduction has no effect on the volatility of stocks of the announcing company and similarly I found no definite effect on the volatility of stocks of the competition. Furthermore, I found that the earnings announcement has no definite effect on the stock returns of the announcing company but that it has a significant positive effect on the volatility.
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Price elasticity of household water demand in Czech Republic
Hortová, Jana ; Krištoufek, Ladislav (advisor) ; Šopov, Boril (referee)
Během posledních desetiletí se v eské republice stále zvyšují ceny vody, což vede k následnému snižování její spotřeby. Tomuto tématu se věnuje velké množství studií, ale dle našich zjištění se žádná z nich nezabývá eskou republikou. Obecně lze říci, že hlavním cílem této práce je nalezení krátkodobé a dlouhodobé cenové elasticity poptávky po vodě v eské republice a následně v Kladně. Při zkoumání poptávky po vodě v Kladně vyšetřujeme cenovou elasticitu v závislosti na odlišném počtu člen· v domácnostech. V obou případech dojdeme k závěru, že poptávka je neelastická v krátkém i dlouhém časovém období. Dále zjistíme, že cenová elasticita roste s klesajícím počtem člen· domácností v obou časových obdobích. Naše výsledky jsou v souladu se závěry ostatních studií.
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Multifractal Analysis of Stock Market Prices
Čechová, Kristýna ; Krištoufek, Ladislav (advisor) ; Vošvrda, Miloslav (referee)
The aim of this thesis is to provide an empirical evidence of multifractality in financial time series and to discuss the relevance of this concept for the current financial theory. We have applied two methods, the Multifractal Detrended Fluctuation analysis and the Generalized Hurst exponent method, on components of the Dow Jones Industrial Average. We analyzed daily data of 30 companies traded on U.S. stock markets from 2002 to 2012. We present results supporting presence of multiscaling in open-close returns. Contrary to published literature, we were not able to find any significant multiscaling in volatility. Moreover based on our analysis, multiscaling is not present in standardized returns and as multifractality requires relatively complicated models, this is our most valuable result. 1
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A growth maximizing contrarian trading strategy
Janča, Marek ; Krištoufek, Ladislav (advisor) ; Havránek, Tomáš (referee)
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%.
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Comovements of Central European Stock Markets: What Does the High Frequency Data Tell Us?
Roháčková, Hana ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
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.
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Power Spot Market of the European Energy Exchange and Its Influence on the Czech Power Market
Vavřičková, Jana ; Červinka, Michal (advisor) ; Krištoufek, Ladislav (referee)
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
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Range-based volatility estimation and forecasting
Benčík, Daniel ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
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...
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Ising model in finance: from microscopic rules to macroscopic phenomena
Dvořák, Pavel ; Krištoufek, Ladislav (advisor) ; Kukačka, Jiří (referee)
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
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Analysis of stock market anomalies: US cross-sectoral comparison
Jílek, Lukáš ; Krištoufek, Ladislav (advisor) ; Šopov, Boril (referee)
The purpose of this thesis is to analyze anomalies in the US stock market. Special attention is put on Day of the week effect, January effect, and Part of the month effect. We focus on comparison of companies with low and high capitalization. We perform an analysis across 6 major industrial sectors. Then, we discuss the findings with results of past projects and finally, we try to find a speculative investment strategy. We found out that neither Day of the week effect nor January effect do not appear in US stock market nowadays. Part of the month effect was the only anomaly, which was observed in our data. Keywords Stock market anomalies, financial markets, cross-sectoral analysis, Jannuary effect, Day of the week effect, Part of the month effect Author's e-mail jileklukas@gmail.com Supervisor's e-mail kristoufek@gmail.com
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Modeling Dynamics of Correlations between Stock Markets with High-frequency Data
Lypko, Vyacheslav ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
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.
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