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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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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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The Feltham-Ohlson Model: Goodwill and Price Volatility
Janský, Michael ; Novák, Jiří (advisor) ; Baruník, Jozef (referee)
This paper derives and tests the hypothesis that there exists a positive relationship between the amount of unrecognized goodwill a company has in relation to the book value of its equity, and the volatility of the price of its stock and the average trading volume of its shares, and that further this relationship is stronger when the source of that goodwill cannot be traced to items recognized in accounting. The hypothesis is derived from the theory of residual income valuation and the Feltham-Ohlson model of company valuation, and is tested on the accounting and market data of 92 companies listed on the New York Stock Exchange. While the results do not offer sufficient reason to reject any of the paper's hypotheses, they provide only partial support to them, and further research is required.
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International Stock Market Co movements and the Global Financial Crisis
Poldauf, Petr ; Horváth, Roman (advisor) ; Baruník, Jozef (referee)
International Stock Market Co-movements and The Global Financial Crisis Petr Poldauf May 16, 2011 Abstract This thesis investigates development of co-movements among international equity returns at the market and industry level over the period 2000 - 2010. Emphasis is put on the influence of the Global Financial Crisis of 2008/2009. We analyze daily data from major markets in Australia, Brazil, Canada, China, Germany, Japan, Russia, South Africa, the UK, and the USA using GARCH family of models. We find that there are still weakly correlated markets and the influence of the Crisis differs from country to country. The sectoral indices, including the financial sector, were significantly less correlated than the market indices over the whole period. 1
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Predictive Accuracy of Competing Value-at-Risk Specifications during Crisis: An Application to CEE Financial Markets
Kroutil, Tomáš ; Baruník, Jozef (advisor) ; Seidler, Jakub (referee)
The recent worldwide Financial Crisis has increased the need for reliable financial risk measurement and management. In this thesis we evaluate and compare the accuracy of one-day-ahead out-of-sample forecasts of various Value-at-Risk models through a comprehensive assessment framework using crisis data of three CEE stock market indices (PX, WIG20 and BUX) and two benchmark stock indices (S&P 500, DAX). For building the VaR specifications we employ several GARCH extensions allowing either for asymmetry in volatility such as EGARCH, TGARCH and APARCH or long memory like FIGARCH and HYGARCH. Apart from conditional heteroscedasticity models, we also utilize realized volatility estimated by long memory ARFIMA and HAR. Individual volatility models are combined with full parametric approach, filtered historical simulation or filtered extreme value theory. This thesis shows that while VaR specifications based on logarithmic realized volatility, TGARCH and APARCH perform best overall, the benchmark - RiskMetrics model - is not significantly outperformed. The best performing model proves to be the TGARCH-t FHS, which is a combination of asymmetric and heavy-tailed GARCH filter with a historical simulation based approach. Keywords: Value-at-Risk, realized volatility, GARCH extensions, quantile modeling,...
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Comparison of Value-at-Risk using various empirical methods for the portfolios of BRICT and G-7 countries in the long run
Gül, Özgür ; Baruník, Jozef (advisor) ; Gapko, Petr (referee)
This master's thesis deals with Value-at-Risk for equity portfolios. The distribution of daily returns of equity returns is not perfectly normal. Therefore, the use of the Delta- Normal Value-at-Risk (VaR) method is misleading. Accuracy of estimation may turn out to be failure for portfolios to measure VaR time to time. Therefore, two further methods, Modified VaR and Filtered Historical Simulation, are used for VaR estimation. The former estimates using Cornish-Fisher (1937) expansion and then the latter estimates using autoregressive model for mean equation, EGARCH for volatility and Filtered Historical Simulation (FHS) for VaR estimation i.e. AR (1) - EGARCH (1,1) - FHS methods; and also the performance of both the VaR estimates with Delta- Normal VaR estimate are compared. Last but not the least the implementation of various methods are discussed and analyzed on the two passive historical index portfolios, which represent some of the most attractive financial markets in the world economy.
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Predictive accuracy of competing Value-at Risk specifications during crisis : an application to CEE financial markets
Kroutil, Tomáš ; Baruník, Jozef (advisor) ; Seidler, Jakub (referee)
The recent worldwide Financial Crisis has increased the need for reliable financial risk measurement and management. In this thesis we evaluate and compare the accuracy of one-day-ahead out-of-sample forecasts of various Value-at-Risk models through a comprehensive assessment framework using crisis data of three CEE stock market indices (PX, WIG20 and BUX) and two benchmark stock indices (S&P 500, DAX). For building the VaR specifications we employ several GARCH extensions allowing either for asymmetry in volatility such as EGARCH, TGARCH and APARCH or long memory like FIGARCH and HYGARCH. Apart from conditional heteroscedasticity models, we also utilize realized volatility estimated by long memory ARFIMA and HAR. Individual volatility models are combined with full parametric approach, filtered historical simulation or filtered extreme value theory. This thesis shows that while VaR specifications based on logarithmic realized volatility, TGARCH and APARCH perform best overall, the benchmark - RiskMetrics model - is not significantly outperformed. The best performing model proves to be the TGARCH-t FHS, which is a combination of asymmetric and heavy-tailed GARCH filter with a historical simulation based approach.
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Behavioural Breaks in the Heterogeneous Agent Model
Kukačka, Jiří ; Baruník, Jozef (advisor) ; Víšek, Jan Ámos (referee)
This thesis merges the fields of Heterogeneous Agent Models (HAMs) and Be- havioural Finance in order to bridge the main deficiencies of both approaches and to examine whether they can complement one another. Our approach suggests an alternative tool for examining HAM price dynamics and brings an original way of dealing with problematic empirical validation. First, we present the original model and discuss various extensions and attempts at empirical estimation. Next, we develop a unique benchmark dataset, covering five par- ticularly turbulent U.S. stock market periods, and reveal an interesting pattern in this data. The main body applies a numerical analysis of the HAM extended with the selected Behavioural Finance findings: herding, overconfidence, and market sentiment. Using Wolfram Mathematica we perform Monte Carlo sim- ulations of a developed algorithm. We show that the selected findings can be well modelled via the HAM and that they extend the original HAM consider- ably. Various HAM modifications lead to significantly different results and HAM is also able to partially replicate price behaviour during turbulent stock market periods. Bibliographic Record Kukačka, J. (2011): Behavioural Breaks in the Heterogeneous Agent Model. Master thesis, Charles University in Prague, Faculty of Social Sciences,...
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The Role of Advanced Option Pricing Techniques Empirical Tests on Neural Networks
Brejcha, Jiří ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
This thesis concerns with a comparison of two advanced option-pricing techniques applied on European-style DAX index options. Specifically, the study examines the performance of both the stochastic volatility model based on asymmetric nonlinear GARCH, which was proposed by Heston and Nandi (2000), and the artificial neural network, where the conventional Black-Scholes-Merton model serves as a benchmark. These option-pricing models are tested with the use of the dataset covering the period 3rd July 2006 - 30th October 2009 as well as of its two subsets labelled as "before crisis" and "in crisis" data where the breakthrough day is the 17th March 2008. Finding the most appropriate option-pricing method for the whole periods as well as for both the "before crisis" and the "in crisis" datasets is the main focus of this work. The first two chapters introduce core issues involved in option pricing, while the subsequent third section provides a theoretical background related to all of above-mentioned pricing methods. At the same time, the reader is provided with an overview of the theoretical frameworks of various nonlinear optimization techniques, i.e. descent gradient, quassi-Newton method, Backpropagation and Levenberg-Marquardt algorithm. The empirical part of the thesis then shows that none of the...
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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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