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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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Forecasting realized volatility: Do jumps in prices matter?
Lipták, Štefan ; Baruník, Jozef (advisor) ; Šopov, Boril (referee)
This thesis uses Heterogeneous Autoregressive models of Realized Volatility on five-minute data of three of the most liquid financial assets - S&P 500 Futures index, Euro FX and Light Crude NYMEX. The main contribution lies in the length of the datasets which span the time period of 25 years (13 years in case of Euro FX). Our aim is to show that decomposing realized variance into continuous and jump components improves the predicatability of RV also on extremely long high frequency datasets. The main goal is to investigate the dynamics of the HAR model parameters in time. Also, we examine whether volatilities of various assets behave differently. Results reveal that decomposing RV into its components indeed improves the modeling and forecasting of volatility on all datasets. However, we found that forecasts are best when based on short, 1-2 years, pre-forecast periods due to high dynamics of HAR model's parameters in time. This dynamics is revealed also in a year-by-year estimation on all datasets. Consequently, we consider HAR models to be inappropriate for modeling RV on such long datasets as they are not able to capture the dynamics of RV. This was indi- cated on all three datasets, thus, we conclude that volatility behaves similarly for different types of assets with similar liquidity. 1
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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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Essays in Financial Econometrics
Avdulaj, Krenar ; Baruník, Jozef (advisor) ; Di Matteo, Tiziana (referee) ; Kočenda, Evžen (referee) ; Witzany, Jiří (referee)
vi Abstract Proper understanding of the dependence between assets is a crucial ingredient for a number of portfolio and risk management tasks. While the research in this area has been lively for decades, the recent financial crisis of 2007-2008 reminded us that we might not understand the dependence properly. This crisis served as catalyst for boosting the demand for models capturing the dependence structures. Reminded by this urgent call, literature is responding by moving to nonlinear de- pendence models resembling the dependence structures observed in the data. In my dissertation, I contribute to this surge with three papers in financial econo- metrics, focusing on nonlinear dependence in financial time series from different perspectives. I propose a new empirical model which allows capturing and forecasting the conditional time-varying joint distribution of the oil - stocks pair accurately. Em- ploying a recently proposed conditional diversification benefits measure that con- siders higher-order moments and nonlinear dependence from tail events, I docu- ment decreasing benefits from diversification over the past ten years. The diver- sification benefits implied by my empirical model are, moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of...
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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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Forecasting realized volatility: Do jumps in prices matter?
Lipták, Štefan ; Baruník, Jozef (advisor) ; Šopov, Boril (referee)
This thesis uses Heterogeneous Autoregressive models of Realized Volatility on five-minute data of three of the most liquid financial assets - S&P 500 Futures index, Euro FX and Light Crude NYMEX. The main contribution lies in the length of the datasets which span the time period of 25 years (13 years in case of Euro FX). Our aim is to show that decomposing realized variance into continuous and jump components improves the predicatability of RV also on extremely long high frequency datasets. The main goal is to investigate the dynamics of the HAR model parameters in time. Also, we examine whether volatilities of various assets behave differently. Results reveal that decomposing RV into its components indeed improves the modeling and forecasting of volatility on all datasets. However, we found that forecasts are best when based on short, 1-2 years, pre-forecast periods due to high dynamics of HAR model's parameters in time. This dynamics is revealed also in a year-by-year estimation on all datasets. Consequently, we consider HAR models to be inappropriate for modeling RV on such long datasets as they are not able to capture the dynamics of RV. This was indi- cated on all three datasets, thus, we conclude that volatility behaves similarly for different types of assets with similar liquidity. 1
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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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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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