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FORMAL MODEL OF DECISION MAKING PROCESS FOR HIGH-FREQUENCY DATA PROCESSING
Zámečníková, Eva ; Rábová, Ivana (referee) ; Šaloun, Petr (referee) ; Kreslíková, Jitka (advisor)
Tato disertační práce se zabývá problematikou zpracování vysokofrekvenčních časových řad. Zaměřuje se na návrh algoritmů a metod pro podporu predikce těchto dat. Výsledkem je model pro podporu řízení rozhodovacího procesu implementovaný do platformy pro komplexní zpracování dat. Model navrhuje způsob formalizace množiny podnikových pravidel, které popisují rozhodovací proces. Navržený model musí vyhovovat splnění požadavků na robustnost, rozšiřitelnost, zpracování v reálném čase a požadavkům ekonometriky. Práce shrnuje současné poznatky a metodologie pro zpracování vysokofrekvenčních finančních dat, jejichž zdrojem jsou nejčastěji burzy. První část práce se věnuje popisu základních principů a přístupů používaných pro zpracování vysokofrekvenčních časových dat v současné době. Další část se věnuje popisu podnikových pravidel, rozhodovacího procesu a komplexní platformy pro zpracování vysokofrekvenčních dat a samotnému zpracování dat pomocí zvolené komplexní platformy. Důraz je kladen na výběr a úpravu množiny pravidel, které řídí rozhodovací proces. Navržený model popisuje množinu pravidel pomocí maticové gramatiky. Tato gramatika spadá do oblasti gramatik s řízeným přepisováním a pomocí definovaných matic umožňuje ovlivnit zpracování dat.
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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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Variance structure of the Bitcoin currency
Pátek, Martin ; Krištoufek, Ladislav (advisor) ; Skuhrovec, Jiří (referee)
The purpose of this thesis is to explain how Bitcoin works, analyze the Bitcoin total variation and to separate the jump component of realized variance from the continuous part. In order to do so, we use estimates of quadratic variation and integrated variance. We detect jumps using a test which is based on the difference between realized variance and bipower variation. The results for BTC/USD exchange rate are then compared with the results for EUR/USD exchange rate, price of gold and for the S&P 500 index. In case of all datasets, we use data with five-minute frequency. It seems that no other work analyzing the Bitcoin total variation using the same methods to separate the jump component from the continuous part of a price process has been written so far. We found that jumps in the Bitcoin total variation are stronger than for other analyzed instruments. The results also suggest that the duration between jumps for Bitcoin considerably prolonged during the monitored period which may indicate that the behavior of price of bitcoin has stabilized over time. We also found out that the variance of price of bitcoin is higher during the monitored period in comparison with other analyzed instruments. Powered by TCPDF (www.tcpdf.org)
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Analysis of Interdependencies among Central European Stock Markets
Mašková, Jana ; Baruník, Jozef (advisor) ; Princ, Michael (referee)
The objective of the thesis is to examine interdependencies among the stock markets of the Czech Republic, Hungary, Poland and Germany in the period 2008-2010. Two main methods are applied in the analysis. The first method is based on the use of high-frequency data and consists in the computation of realized correlations, which are then modeled using the heterogeneous autoregressive (HAR) model. In addition, we employ realized bipower correlations, which should be robust to the presence of jumps in prices. The second method involves modeling of correlations by means of the Dynamic Conditional Correlation GARCH (DCC-GARCH) model, which is applied to daily data. The results indicate that when high-frequency data are used, the correlations are biased towards zero (the so-called "Epps effect"). We also find quite significant differences between the dynamics of the correlations from the DCC-GARCH models and those of the realized correlations. Finally, we show that accuracy of the forecasts of correlations can be improved by combining results obtained from different models (HAR models for realized correlations, HAR models for realized bipower correlations, DCC-GARCH models).
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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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Co-jumping of yield curve
Fišer, Pavel ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
The main focus of the thesis is on jumps and co-jumps and their influence on the term structure of the U.S. Treasury bond futures contracts. Using high frequency data I am able to quantify to which extent co-jumps affect the correlation between bond futures pairs with different maturities which is not common in the literature. In order to separate the price process into continuous and discontinuous components represented by jumps and to pre- cisely localize significant co-jumps a new wavelet-based estimator is used for the analyses. Furthermore, I am studying the co-jump behavior in response to scheduled macroeconomic news announcements. Empirical findings re- veal strong influence of co-jumps to the correlation structure of bond futures across all maturity pairs as well as a significant link between Federal Open Market Committee news announcements and higher probability of co-jump occurrence.
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FORMAL MODEL OF DECISION MAKING PROCESS FOR HIGH-FREQUENCY DATA PROCESSING
Zámečníková, Eva ; Rábová, Ivana (referee) ; Šaloun, Petr (referee) ; Kreslíková, Jitka (advisor)
Tato disertační práce se zabývá problematikou zpracování vysokofrekvenčních časových řad. Zaměřuje se na návrh algoritmů a metod pro podporu predikce těchto dat. Výsledkem je model pro podporu řízení rozhodovacího procesu implementovaný do platformy pro komplexní zpracování dat. Model navrhuje způsob formalizace množiny podnikových pravidel, které popisují rozhodovací proces. Navržený model musí vyhovovat splnění požadavků na robustnost, rozšiřitelnost, zpracování v reálném čase a požadavkům ekonometriky. Práce shrnuje současné poznatky a metodologie pro zpracování vysokofrekvenčních finančních dat, jejichž zdrojem jsou nejčastěji burzy. První část práce se věnuje popisu základních principů a přístupů používaných pro zpracování vysokofrekvenčních časových dat v současné době. Další část se věnuje popisu podnikových pravidel, rozhodovacího procesu a komplexní platformy pro zpracování vysokofrekvenčních dat a samotnému zpracování dat pomocí zvolené komplexní platformy. Důraz je kladen na výběr a úpravu množiny pravidel, které řídí rozhodovací proces. Navržený model popisuje množinu pravidel pomocí maticové gramatiky. Tato gramatika spadá do oblasti gramatik s řízeným přepisováním a pomocí definovaných matic umožňuje ovlivnit zpracování dat.
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Co-jumping of yield curve
Fišer, Pavel ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
The main focus of the thesis is on jumps and co-jumps and their influence on the term structure of the U.S. Treasury bond futures contracts. Using high frequency data I am able to quantify to which extent co-jumps affect the correlation between bond futures pairs with different maturities which is not common in the literature. In order to separate the price process into continuous and discontinuous components represented by jumps and to pre- cisely localize significant co-jumps a new wavelet-based estimator is used for the analyses. Furthermore, I am studying the co-jump behavior in response to scheduled macroeconomic news announcements. Empirical findings re- veal strong influence of co-jumps to the correlation structure of bond futures across all maturity pairs as well as a significant link between Federal Open Market Committee news announcements and higher probability of co-jump occurrence.
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Modeling of duration between financial transactions
Voráčková, Andrea ; Zichová, Jitka (advisor) ; Pawlas, Zbyněk (referee)
❆❜str❛❝t ❚❤✐s ❞✐♣❧♦♠❛ t❤❡s✐s ❞❡❛❧s ✇✐t❤ ♣r♦♣❡rt✐❡s ♦❢ ❆❈❉ ♣r♦❝❡ss ❛♥❞ ♠❡t❤♦❞s ♦❢ ✐ts ❡st✐♠❛t✐♦♥✳ ❋✐rst✱ t❤❡ ❜❛s✐❝ ❞❡☞♥✐t✐♦♥s ❛♥❞ r❡❧❛t✐♦♥s ❜❡t✇❡❡♥ ❆❘▼❆ ❛♥❞ ●❆❘❈❍ ♣r♦❝❡ss❡s ❛r❡ st❛t❡❞✳ ■♥ t❤❡ s❡❝♦♥❞ ♣❛rt ♦❢ t❤❡ t❤❡s✐s✱ t❤❡ ❆❈❉ ♣r♦❝❡ss ✐s ❞❡☞♥❡❞ ❛♥❞ t❤❡ r❡❧❛t✐♦♥ ❜❡t✇❡❡♥ ❆❘▼❆ ❛♥❞ ❆❈❉ ✐s s❤♦✇♥✳ ❚❤❡♥ ✇❡ s❤♦✇ t❤❡ ♠❡t❤♦❞s ♦❢ ❞❛t❛ ❛❞❥✉st♠❡♥t✱ ❡st✐♠❛t✐♦♥✱ ♣r❡❞✐❝t✐♦♥ ❛♥❞ ✈❡r✐☞❝❛t✐♦♥ ♦❢ t❤❡ ❆❈❉ ♠♦❞❡❧✳ ❆❢t❡r t❤❛t✱ t❤❡ ♣❛rt✐❝✉❧❛r ❝❛s❡s ♦❢ ❆❈❉ ♣r♦❝❡ss✿ ❊❆❈❉✱ ❲❆❈❉✱ ●❆❈❉✱ ●❊❱❆❈❉ ✇✐t❤ ✐ts ♣r♦♣❡rt✐❡s ❛♥❞ t❤❡ ♠♦t✐✈❛t✐♦♥❛❧ ❡①❛♠♣❧❡s ❛r❡ ✐♥tr♦❞✉❝❡❞✳ ❚❤❡ ♥✉♠❡r✐❝❛❧ ♣❛rt ✐s ♣❡r❢♦r♠❡❞ ✐♥ ❘ s♦❢t✇❛r❡ ❛♥❞ ❝♦♥❝❡r♥s t❤❡ ♣r❡❝✐s✐♦♥ ♦❢ t❤❡ ❡st✐♠❛t❡s ❛♥❞ ♣r❡❞✐❝t✐♦♥s ♦❢ t❤❡ s♣❡❝✐❛❧ ❝❛s❡s ♦❢ ❆❈❉ ♠♦❞❡❧ ❞❡♣❡♥❞✐♥❣ ♦♥ t❤❡ ❧❡♥❣t❤ ♦❢ s❡r✐❡s ❛♥❞ ♥✉♠❜❡r ♦❢ s✐♠✉❧❛t✐♦♥s✳ ■♥ t❤❡ ❧❛st ♣❛rt✱ ✇❡ ❛♣♣❧② t❤❡ ♠❡t❤♦❞s st❛t❡❞ ✐♥ t❤❡♦r❡t✐❝❛❧ ♣❛rt ♦♥ r❡❛❧ ❞❛t❛✳ ❚❤❡ ❛❞❥✉st♠❡♥t ♦❢ t❤❡ ❞❛t❛ ❛♥❞ ❡st✐♠❛t✐♦♥ ♦❢ t❤❡ ♣❛r❛♠❡t❡rs ✐s ♣❡r❢♦r♠❡❞ ❛s ✇❡❧❧ ❛s t❤❡ ✈❡r✐☞❝❛t✐♦♥ ♦❢ t❤❡ ❆❈❉ ♠♦❞❡❧✳ ❆❢t❡r t❤❛t✱ ✇❡ ♣r❡❞✐❝t ❢❡✇ st❡♣s ❛♥❞ ❝♦♠♣❛r❡ t❤❡♠ ✇✐t❤ r❡❛❧ ❞✉r❛t✐♦♥s✳ ✶
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Statistical properties of the liquidity and its influence on the volatility prediction
Brandejs, David ; Krištoufek, Ladislav (advisor) ; Burda, Martin (referee)
This master thesis concentrates on the influence of liquidity measures on the prediction of volatility and given the magic triangle phenomena subsequently on the expected return. Liquidity measures Amihud Illiquidity, Amivest Liquidity and Roll adjusted for high frequency data have been utilized. Dataset used for the modeling was consisting of 98 shares that were traded on S&P 100. The time range was from 1st January 2013 to 31st December 2014. We have found out that the liquidity truly enters into the return-volatility relationship and influences these variables - the magic triangle interacts. However, contrary to our hypothesis, the model shows up that lower liquidity signifies lower realized risk. This inference has been suggested by all three models (3SLS, 2SLS and OLS). Furthermore, we have used the realized variance and bi-power variation to separate the jump. Our second hypothesis that lower liquidity signifies higher frequency of jumps was confirmed only for one of two liquidity proxies (Roll) included in the resulting logit FE model. Keywords liquidity, risk, volatility, expected return, magic triangle, price jumps, realized variance, bi-power variation, three-stage least squares model, logit, high-frequency data, S&P 100 Author's e-mail david.brandejs@seznam.cz Supervisor's e-mail...
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