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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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Stock Markets Analysis Using New Genetic Annealed Neural Network
Verner, Robert ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
The presented master thesis is focused on the stock markets returns analysis using a new type of neural network. First chapter of the thesis describes the underlying theory of the financial time series prediction, Efficient Market Hypothesis and conventional forecasting models. Following part illustrates biological framework, basic principles, functioning of neural networks, their architecture and several well-known learning algorithms such as Gradient descent, Levenberg-Marquardt algorithm or Conjugate gradient. It also mentions certain disadvantages which influence the performance and effectiveness of neural networks. Third chapter is devoted to two applied metaheuristic techniques, i.e. genetic algorithms and simulated annealing that were integrated into neural networks framework to eliminate above mentioned drawbacks. Next chapter describes details of presented hybrid network, whereas the last section is aimed at evaluation of overall results of all models. It shows that on the examined sample hybrid network clearly outperformed standard techniques as well as ordinary neural networks and in most cases achieved the least mean squared error among all explored methods. Keywords: stock returns analysis, neural networks, genetic algorithms, simulated annealing, hybrid networks JEL classification:...
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Application of quantile autoregressive models in minimum Value at Risk and Conditional Value at Risk hedging
Svatoň, Michal ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
Futures contracts represent a suitable instrument for hedging. One conse- quence of their standardized nature is the presence of basis risk. In order to mitigate it an agent might aim to minimize Value at Risk or Expected Shortfall. Among numerous approaches to their modelling, CAViaR models which build upon quantile regression are appealing due to the limited set of assumptions and decent empirical performance. We propose alternative specifications for CAViaR model - power and exponential CAViaR, and an alternative, flexible way of computing Expected Shortfall within CAViaR framework - Implied Expectile Level. Empirical analysis suggests that ex- ponential CAViaR yields competitive results both in Value at Risk and Ex- pected Shortfall modelling and in subsequent Value at Risk and Expected Shortfall hedging. 1
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Volatility modeling : evidence from CEE stock markets
Brabcová, Eva ; Bubák, Vít (referee) ; Baruník, Jozef (advisor)
The thesis applies newly developed heterogenous autoregressive model of realized volatility on high frequency data of three stock market indices: Prague, Budapest and Warsaw with the aim to capture behavior of three different market participants and to quantify their role in forecasting daily realized volatility. Also, the presence of jumps in volatility is investigated and the predictive power assessed. In addition, wavelet analysis is used to detect periods and frequencies of comovements between the three indices. The main contribution of the thesis lies especially in its primary empirical analysis conducted in CEE region. The estimation results indicate that future realized volatility is determined very similarly in all markets with an insignificant impact of participants trading on monthly basis. Moreover, occurrence of a jump proves to be of a high relevance when predicting future volatility. Moreover, wavelet analysis indicates a strong degree of comovement at a frequency of few months across the whole period examined.
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Value-at-risk based extreme value theory method and copulas : empirical evidence from Central Europe
Avdulaj, Krenar ; Seidler, Jakub (referee) ; Baruník, Jozef (advisor)
Assessing the extreme events is crucial in financial risk management. All risk managers and and financial institutions want to know the risk of their portfolio under rare events scenarios. We illustrate a multivariate Monte Carlo and semi-parametric method to estimate Value-at-Risk (VaR) for a portfolio of stock exchange indexes in Central Europe. It is a method that uses the non-parametric empirical distribution to capture the small risks and the parametric Extreme Value theory to capture large risks. We compare this method with historical simulation and variance-covariance method under low and high volatility samples of data. In general historical simulation method over estimates the VaR for extreme events, while variance-covariance underestimates it. The method that we illustrate gives a result in between because it considers historical performance of the stocks and also corrects for the heavy tails of the distribution. We conclude that the estimate method that we illustrate here is useful in estimating VaR for extreme events, especially for high volatility times.
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Alternative field curve modelling approach : regional models
Šopov, Boril ; Seidler, Jakub (advisor) ; Baruník, Jozef (referee)
In this thesis, we focus on thorough yield curve modelling. We build on extended classical Nelson-Siegel model, which we further develop to accommodate unobserved regional common factors and principal components. We centre our discussion on central European currencies' yield curves: CZK, HUF, PLN and SKK. We propose two novel models to capture regional dynamics; one based purely on state space formulation and the other relying also on principal components of the regional yield curves. Moreover, we supplement the models with two application examples in risk management and structural break detection. The main contribution of this thesis is a creation of a complete framework that enables us to analyse yield curves, to design risk scenarios and to detect structural breaks of various types.
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Alternative yield curve modelling approach : regional models
Šopov, Boril ; Seidler, Jakub (advisor) ; Baruník, Jozef (referee)
In this thesis, we focus on thorough yield curve modelling. We build on extended classical Nelson-Siegel model, which we further develop to accommodate unobserved regional common factors and principal components. We centre our discussion on central European currencies' yield curves: CZK, HUF, PLN and SKK. We propose two novel models to capture regional dynamics; one based purely on state space formulation and the other relying also on principal components of the regional yield curves. Moreover, we supplement the models with two application examples in risk management and structural break detection. The main contribution of this thesis is a creation of a complete framework that enables us to analyse yield curves, to design risk scenarios and to detect structural breaks of various types.
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Portfolio selectio : clustering algorithm approach
Jenček, Petr ; Pečená, Magda (referee) ; Baruník, Jozef (advisor)
Prices of assets (stocks, commodities etc.) are dependent on many economic factors. These factors may be explicitly known but most of them are hidden. This dependency causes that price of an asset influences prices of another assets which makes it quite complicated to select optimal portfolio. Portfolio management is usually based on various mathematic models in conjunction with Value-at-Risk model. The aim of this thesis is to provide an alternative approach for optimal portfolio selection with mutual assets' prices correlation consideration using cluster analysis.
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Causal relationship between Uncertainty and Crude Oil Prices: A Quantile Regression approach
Ruiz Vargas, Andrés Mauricio ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
This work considers the causal relationship between the news-based uncertainty measures and WTI crude oil price within the quantile causality framework by using daily data for a period from January 4, 2000, to November 14, 2016. We find that the Granger non-causality test in quantiles between crude oil returns and the news-based uncertainty measures uncover the causal relationship over different levels of conditional quantiles of the crude oil returns. In particular, there exists a strong causal relationship in the tails of the crude oil returns distribution. Powered by TCPDF (www.tcpdf.org)
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