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A time-varying copula approach to equity market contagion
Horáčková, Petra ; Baruník, Jozef (advisor) ; Buzková, Petra (referee)
The dependence structures in financial markets count among the most frequently discussed topics in the recent literature. However, no general consensus on modeling of the cross-market linkages has been reached. This thesis analyses the dependence structure and contagion in the financial markets in Central and Eastern Europe. Tail dependence, symmetry and dynamics of the dependence structure are examined. A conditional copula framework extended by recently developed dynamic generalized autoregressive score (GAS) model is used to capture the conditional time-varying joint distribution of stock market returns. Considering the Czech, Croatian, Hungarian, Austrian and Polish stock market indices over the 2005-2012 period, we find that time-varying Student's t GAS copula provides the best fit. The results show, that the degree of dependence increases substantially during the global financial crisis, having a direct impact on portfolio optimization.
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Neural network models for conditional quantiles of financial returns and volatility
Hauzr, Marek ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
This thesis investigates forecasting performance of Quantile Regression Neural Networks in forecasting multiperiod quantiles of realized volatility and quantiles of returns. It relies on model-free measures of realized variance and its components (realized variance, median realized variance, integrated variance, jump variation and positive and negative semivariances). The data used are S&P 500 futures and WTI Crude Oil futures contracts. Resulting models of returns and volatility have good absolute performance and relative performance in comparison to the linear quantile regression models. In the case of in- sample the models estimated by Quantile Regression Neural Networks provide better estimates than linear quantile regression models and in the case of out-of-sample they are equally good.
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Artificial Intelligence Approach to Credit Risk
Říha, Jan ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
This thesis focuses on application of artificial intelligence techniques in credit risk management. Moreover, these modern tools are compared with the current industry standard - Logistic Regression. We introduce the theory underlying Neural Networks, Support Vector Machines, Random Forests and Logistic Regression. In addition, we present methodology for statistical and business evaluation and comparison of the aforementioned models. We find that models based on Neural Networks approach (specifically Multi-Layer Perceptron and Radial Basis Function Network) are outperforming the Logistic Regression in the standard statistical metrics and in the business metrics as well. The performance of the Random Forest and Support Vector Machines is not satisfactory and these models do not prove to be superior to Logistic Regression in our application.
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The time-frequency relationship between spot and futures prices of crude oil
Tran Quang, Tuan ; Baruník, Jozef (advisor) ; Červinka, Michal (referee)
This thesis investigates the relationship between daily spot and futures prices for maturities of one, two, three and four months of West Texas Intermediate (WTI) crude oil. The data cover period January 1987-April 2015. Based on economic theory, the futures prices should be closely related to the spot price, which - in the case of crude oil market - this thesis analyses using wavelet-based approach. Main contributions of this thesis are findings in the field of time-frequency relationship of spot-futures prices of crude oil, where an alternative methodology - wavelet transformation - is used. The usage of this advanced method is also an additional contribution of this thesis because it allows us to rigorously study how co-movement (relationship) differs across frequencies/scales and time. In this thesis wavelet Coherence, wavelet bivariate correlation and relatively new method wavelet band spectral regression (WBLS) are used. This thesis brings 4 main findings. First, relationship between Futures and spot prices of crude oil is strong in all time-periods (frequencies/scales), which supports economic theory. Second and In contrary to the first finding, in the gasoline spot-futures market, we find that the relationship is strong mainly in higher scales (lower frequencies) while in lower scales (higher...
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Estimation of Financial Agent-Based Models
Kukačka, Jiří ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee) ; ZWINKELS, REMCO C. J. (referee) ; GERBA, EDDIE EDIN (referee)
This thesis proposes computational framework for empirical estimation of Finan- cial Agent-Based Models (FABMs) that does not rely upon restrictive theoretical assumptions. First, we develop a two-step estimation methodology for one of the his- torically first FABMs-the stochastic cusp catastrophe model. Our method al- lows us to apply catastrophe theory to stock market returns with time-varying volatility and to model stock market crashes. The methodology is empirically tested on nearly 27 years of U.S. stock market returns. We find that the U.S. stock market's downturns were more likely to be driven by the endogenous market forces during the first half of the studied period, while during the sec- ond half of the period, the exogenous forces seem to be driving the market's instability. The results suggest that the proposed methodology provides an important shift in the application of catastrophe theory to stock markets. Second, we customise a recent methodology of the Non-Parametric Simu- lated Maximum Likelihood Estimator (NPSMLE) based on kernel methods by Kristensen & Shin (2012) and elaborate its capability for FABMs estimation purposes. To start with, we apply the methodology to the most famous and widely analysed model of Brock & Hommes (1998). We extensively test finite sample properties of the...
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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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Pricing of bonds and credit default swaps: Evidence from a panel of European companies
Smotlachová, Eva ; Baruník, Jozef (advisor) ; Malinská, Barbora (referee)
The aim of the thesis is to investigate determinants of corporate bond and CDS contract pricing using a sample of 34 European companies over the period 2008-2014. This work extends existing literature by studying differences in determinants of bond and CDS spreads not only for different time periods, but also for different sets of companies grouped by geography, industry, and profitability. The results reveal that bond and CDS spreads are generally influenced by similar factors, with a company's credit rating being the most influential factor. Nevertheless, the investigation of time-specific estimations suggests that firm-specific factors play a more significant role in pricing bonds, whereas market factors have a higher impact on CDS spreads. The analysis of the subsamples reveals substantial differences in regression results for individual groups of companies, which suggests a presence of idiosyncratic factors. Our conclusion is that the pricing of bonds and CDS contracts is not only time-dependent, but also unique for different groups of companies, which implies a necessity to use different pricing models for individual contracts.
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Measuring systemic risk in time-frequency domain
Muzikářová, Ivana ; Baruník, Jozef (advisor) ; Bauer, Michal (referee)
This thesis provides an analysis of systemic risk in the US banking sector. We use conditional value at risk (∆CoVaR), marginal expected shortfall (MES) and cross-quantilogram (CQ) to statistically measure tail-dependence in return series of individual institutions and the system as a whole. Wavelet multireso- lution analysis is used to study systemic risk in the time-frequency domain. De- composition of returns on different scales allows us to isolate cycles of 2-8 days, 8-32 days and 32-64 days and analyze co-movement patterns which would oth- erwise stay hidden. Empirical results demonstrate that filtering out short-term noise from the return series improves the forecast power of ∆CoVaR. Eventu- ally, we investigate the connection between statistical measures of systemic risk and fundamental characteristics of institutions (size, leverage, market to book ratio) and conclude that size is the most robust determinant of systemic risk.
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Systemic Risk in the European Financial and Energy Sector: Dynamic Factor Copula Approach
Nevrla, Matěj ; Baruník, Jozef (advisor) ; Buzková, Petra (referee)
In the thesis we perform analysis of systemic risk in the financial and energy sector in Europe. As the econometric tool for estimating dependencies across the subjects we employ factor copula model with GAS dynamics of Oh & Patton (2013b). We apply this model to daily CDS spreads. Based on the estimated results we perform Monte Carlo simulations in order to obtain future values of CDS spreads and measure probability of systemic events. We conclude that substantially higher systemic risk is present within the financial sector. We also find that the most systemic companies from both sectors come from Spain. JEL Classification C53, C55, C58, G17 Keywords Credit Default Swap, Energy Sector, Factor Copula, Financial Sector, Generalized Autore- gressive Score Model, Systemic Risk Author's e-mail matej.nevrla@gmail.com Supervisor's e-mail barunik@fsv.cuni.cz
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Understanding co-jumps in financial markets
Thoma, Richard ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
This thesis focuses on impact of jumps and simultaneous jumps (co-jumps) in asset prices on future volatility. Our main contribution to the empirical literature lies in the use of panel Heterogeneous Autoregressive (HAR) model that allows us to obtain average effect of jumps for both the portfolio of 29 U.S. stocks and 8 individual market sectors our stocks belong to. On top of that we investigate the effect of sign for both jumps and co-jumps. The estimation results indicate that the impact of jumps on future volatility is positive whereas for co-jumps it is negative. We also document tendency of downward jumps and co-jumps to be followed by increase in volatility and that upward jumps and co-jumps are followed by decrease in volatility. Finally, results for individual sectors reveal that estimated effects vary across industries - for cyclical sectors volatility is in general more sensitive to negative jumps and less sensitive to positive jumps than for defensive sectors.
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