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Frequency Connectedness of Financial, Commodity, and Forex Markets
Šoleová, Juliána ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
This Thesis is dedicated to the variance decompositions from the VAR model un- der the Diebold, Yilmaz (2012) methodology combined with the Baruník, Křehlík (2017) method of frequencies that was used to create traditional and directional spillover tables to be compared under different frequencies. Diverse markets vari- ables were used for the analysis during the period 1/6/1999 to 29/6/2018. The S&P 500 Index represented the financial markets, EUR/USD and YEN/USD rep- resented the Forex markets, and eight types of commodities: Crude Oil, Natural Gas, Gasoline, and Propane represented energy commodities and Corn, Coffee, Wheat, and Soybeans represented food commodities. This analysis contribute to understanding of the dynamic frequency connectedness in case of a differentiated system of markets. The main finding was the strongest short-frequency reaction to shocks in case of all variables, which is opposite behavior than usually observed in banking sector frequency dynamics analyses. JEL Classication: F12, F21, F23, H25, H71, H87 Keywords: connectedness, financial market, forex market, commodity market, systemic risk, spillovers, frequency analysis Author's e-mail: 93414233@fsv.cuni.cz Supervisor's e-mail: barunik@fsv.cuni.cz
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Modeling financial markets using heterogenous agent models
Benčík, Daniel ; Vácha, Lukáš (advisor) ; Baruník, Jozef (referee)
This thesis deals with the application of heterogeneous agent models (HAM) in the area of financial markets. In the first part, we introduce the concept of HAMs, review examples of several earlier models in order to provide the reader with a general picture of applications of HAMs in finance. Subsequently, we move on to describe the original model developed by Brock, Hommes (1998) and continue by describing modifications proposed by Barunik, Vacha and Vosvrda (2009). Next, we move to the analysis of the modified model's behavior, including its ability to simulate stylized facts observed in real financial markets. In the last part of this work, we provide descriptions of our simulation/experimental setups and conclude by summarizing the results of these. We finish this thesis by suggesting possible future research topics regarding the investigated model that might shed more light on its behavior and thus hopefully enhance our understanding of how real financial markets operate.
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Financial markets modeling - experimental and agent based approach
Štefanová, Hana ; Vácha, Lukáš (advisor) ; Korbel, Václav (referee)
Tato práce se zabývá problémem modelování finančních trhů. K modelování používáme dva přístupy: simultánní a experimentální. Nejprve představíme agentní modelování a experimentální ekonomii. Poté vysvětlíme silné a slabé stránky těchto přístupů a ukážeme jejich společný přínos v oblasti modelování finančních trhů. Aby čtenář získal komplexnější představu o celé problematice, uvedeme několik modelů používajících kombinovanou metodologii. Následně představíme model dvojité aukce, jehož autory jsou Gode a Sunder (1993). Naši práci zakončíme výsledky experimentu, který jsme sami provedli, a jehož základní myšlenkou je právě práce od Goda a Sundera.
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European Real Estate Investment Trusts: Analyzing Correlation with a DCC-GARCH Model
Jílek, Jiří ; Jandík, Tomáš (advisor) ; Vácha, Lukáš (referee)
Bibliographic Record JÍLEK, Jiří. European Real Estate Investment Trusts: Analyzing Correlation with a DCC- GARCH Model. Prague, 2012. 50 p. Master thesis (Mgr.) Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies. Supervisor: Tomáš Jandík MA MSc MRICS. Abstract The main goal of this thesis is to study the interdependencies between returns of European real estate investment trusts (REITs) and other investment asset classes such as European equities, government bonds and commodities. The thesis is divided into two parts: in the first part, we describe the necessary background that led to the emergence of first REIT structures and also provide an overview of the European REITs market. In the second part, we apply the Dynamic Conditional Correlation GARCH (DCC-GARCH) model to examine correlations between the above mentioned asset classes. The general understanding of real estate is that it provides diversification benefits to a diversified portfolio. However, our results suggest that returns of European REITs and stocks show a relatively high correlation and more importantly, the correlation increases in time. These findings have significant implications for investors and portfolio managers who seek protection for their portfolios in time of market downturns. Our results...
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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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Investment horizon in the CAPM: A comparison of a wavelet-based decomposition and the fractal regression
Spousta, Radek ; Krištoufek, Ladislav (advisor) ; Vácha, Lukáš (referee)
This thesis study two promising methods used to define the multiscale CAPM - the wavelet-based decomposition and the fractal regression. Their estimates, obtained on monthly excess return on ten portfolios formed on beta in the US market, are compared in the period from November 2000 to October 2020 and, subsequently, in the period from November 1965 to October 2020. In the first period, the multiscale beta is not significantly different from the original single-scale beta for most of the portfolios. Contrary, both methods uncover significant multiscale behavior of the beta in the second period. Specifically, the high-beta portfolios have higher multiscale beta at longer investment horizons, mainly at wavelet scale 3 and scales 12-24 of the fractal regression. Overall, both methods deliver consistent results, and seem suitable for extending the CAPM with an investment horizon. JEL Classification Keywords G12, C20 CAPM, asset pricing, multiscale analysis, wavelets, fractal regression Title Investment horizon in the CAPM: A comparison of a wavelet-based decomposition and the fractal regression
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On multifractality and predictability of financial time series
Heller, Michael ; Krištoufek, Ladislav (advisor) ; Vácha, Lukáš (referee)
The aim of this thesis is to examine an empirical relationship between multifrac- tality of financial time series and its returns. We approach the multifractality of a given time series as a measure of its complexity. Multifractal financial time series exhibit repeating self-similar patterns. Multifractality could be a good predictor of stock returns or a factor which can be used in asset pricing. We expected that capturing the complexity of a given time series by a model, a positive or a negative risk premia for investing into "more multifractal assets" could be found. Daily prices of 31 stock indices and daily returns of 10-years US government bonds were downloaded. All the data were recorded between 2012 and 2021. After estimation the multifractal spectra, applying MF-DFA method, of all stock indices, we ordered all stock indices from the lowest to the most multifractal. Then, we constructed a "multifractal portfolio" holding a long position in the 7 most multifractal and holding a short position in the 7 least multifractal stock indices. Fama-MacBeth regression with market risk premia and multifractal variable as independent variables was applied. Multi- fractality in all examined financial time series was found. We also found a very low negative risk premia for holding "a multifractal...
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Acquisition of Costly Information in Data-Driven Decision Making
Janásek, Lukáš ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
This thesis formulates and solves an economic decision problem of the acquisi- tion of costly information in data-driven decision making. The thesis assumes an agent predicting a random variable utilizing several costly explanatory vari- ables. Prior to the decision making, the agent learns about the relationship between the random variables utilizing its past realizations. During the deci- sion making, the agent decides what costly variables to acquire and predicts using the acquired variables. The agent's utility consists of the correctness of the prediction and the costs of the acquired variables. To solve the decision problem, the thesis divides the decision process into two parts: acquisition of variables and prediction using the acquired variables. For the prediction, the thesis presents a novel approach for training a single predictive model accepting any combination of acquired variables. For the acquisition, the thesis presents two novel methods using supervised machine learning models: a backward es- timation of the expected utility of each variable and a greedy acquisition of variables based on a myopic increase in the expected utility of variables. Next, the thesis formulates the decision problem as a Markov decision process which allows approximating the optimal acquisition via deep...
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Can Machines Explain Stock Returns?
Chalupová, Karolína ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Can Machines Explain Stock Returns? Thesis Abstract Karolína Chalupová January 5, 2021 Recent research shows that neural networks predict stock returns better than any other model. The networks' mathematically complicated nature is both their advantage, enabling to uncover complex patterns, and their curse, making them less readily interpretable, which obscures their strengths and weaknesses and complicates their usage. This thesis is one of the first attempts at overcoming this curse in the domain of stock returns prediction. Using some of the recently developed machine learning interpretability methods, it explains the networks' superior return forecasts. This gives new answers to the long- standing question of which variables explain differences in stock returns and clarifies the unparalleled ability of networks to identify future winners and losers among the stocks in the market. Building on 50 years of asset pricing research, this thesis is likely the first to uncover whether neural networks support the economic mechanisms proposed by the literature. To a finance practitioner, the thesis offers the transparency of decomposing any prediction into its drivers, while maintaining a state-of-the-art profitability in terms of Sharpe ratio. Additionally, a novel metric is proposed that is particularly suited...
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Stock Market Prediction: A Multiclass Classification on Emotions and Sentiment Analysis for Tweets and News Headlines
Lazeski, Dejan ; Kočenda, Evžen (advisor) ; Vácha, Lukáš (referee)
i Abstract In this thesis, we look beyond extracting binary sentiment in regards to News Headlines and Tweets. As a data source, we target tweets and headlines from well-known financial newspapers, explicitly addressing the top 5 Big Tech com- panies. To examine the effectiveness of sentiment and Ekman's emotions in predicting future stock price movements, we develop multiclass emotion and sentiment classifiers utilizing a supervised learning approach. Moreover, we manually annotate our corpora for positive, negative, and neutral sentiment as well as one of Ekman's emotions: anger, joy, surprise, sadness. We did not confirm any robust correlation between daily stock price movements and the distribution of sentiment and emotions. However, we did observe that tweets are less neutral than news headlines. Finally, we implement a simple invest- ing strategy by extracting sentiment polarity scores using VADER and other metrics such as followers and shares. Two classifiers, SVM and ANN, delivered robust predictions for Google and Amazon compared to weak predictions for the rest of the companies. Nevertheless, the results suggest that sentiment polarity can effectively predict future stock price movements compared to finer-grained emotion classification. JEL Classification C53, G41, G17, C61 Keywords News...
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