National Repository of Grey Literature 87 records found  previous11 - 20nextend  jump to record: Search took 0.00 seconds. 
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.
The Impact of High Frequency Trading on Price Volatility
Vondřička, Jakub ; Vácha, Lukáš (advisor) ; Vošvrda, Miloslav (referee)
This thesis examines an impact of high frequency trading on equity market qualities. As an indicator of market quality, stock prices realized volatility is used. To estimate the high frequency trading activity, we implement a special method of identification of high frequency orders from quote data. Study of relation between high frequency trading and market qualities is incited by growing concerns about the welfare impacts of high frequency trading and connected activities. In order to test the dependence and causality between high frequency trading activity and volatility, we implement time-scale estimation techniques. Wavelet coherence is used to study localized dependence. The analysis is amended by a robustness check, using wavelet correlation. Results show inconsistent dependence at short trading horizons and regions of significant continuous dependence at trading horizons within hours. Powered by TCPDF (www.tcpdf.org)
Estimating the Euro effect with Synthetic Control Method for Eastern Europe
Janota, Martin ; Teplý, Petr (advisor) ; Vošvrda, Miloslav (referee)
Estimating the Euro effect with Synthetic Control Method for Eastern Europe Abstract This thesis estimates the effect of Euro adoption on newest Eurozone members using synthetic control method. The effect is estimated on income per capita and GDP growth. Estimates indicate overall indecisive effect for Slovakia and Malta, neutral effect for Estonia and negative effect for Slovenia and Cyprus. The cost of Euro for Cyprus is estimated to be as high as 1/3 of GDP per capita. In some cases the direction of the effect changed before and after the financial crisis. The quality of inference suffers from low number of observations. Methodological assumptions are discussed, concluding that quality of Eastern European time series likely causes substantial bias in the results.
Three Essays on Empirical Analysis of Economic Policy
Baxa, Jaromír ; Vošvrda, Miloslav (advisor) ; Vašíček, Osvald (referee) ; Hančlová, Jana (referee) ; Slačálek, Jiří (referee)
This dissertation thesis is focused on the empirical analysis of monetary and fiscal policy using nonlinear models. In the first part, I examine the evolution of monetary policy rules in a group of inflation targeting countries. I apply a moment-based estimator in a time-varying parameter model with endogenous regressors. The main findings are twofold. First, with adoption of inflation targeting, coefficients in the monetary policy rules changed rather gradually. Second, the response of interest rates to inflation is particularly strong during periods when central bankers want to break a record of high inflation. Contrary to common view, the response of interest rates to inflation becomes less aggressive after the adoption of inflation targeting, suggesting a positive anchoring effect of this regime on inflation expectations. The second part discusses whether and how the selected central banks responded to episodes of financial stress over the last three decades. The time-varying monetary policy rule is extended for an indicator of financial stress, in order to show the departures of policy rules under financial instability. The findings suggest that central banks often decrease policy rates in the face of high financial stress. However, the size of the policy response varies substantially over time as well...
Does wavelet decomposition and neural networks help to improve predictability of realized volatility?
Křehlík, Tomáš ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
I perform comprehensive comparison of the standard realised volatility estimators including a novel wavelet time-frequency estimator (Barunik and Vacha 2012) on wide variety of assets: crude oil, gold and S&P 500. The wavelet estimator allows to decompose the realised volatility into several investment horizons which is hypothesised in the literature to bring more information about the volatility time series. Moreover, I propose artificial neural networks (ANN) as a tool for forecasting of the realised volatility. Multi-layer perceptron and recursive neural networks typologies are used in the estimation. I forecast cumulative realised volatility on 1 day, 5 days, 10 days and 20 days ahead horizons. The forecasts from neural networks are benchmarked to a standard autoregressive fractionally integrated moving averages (ARFIMA) model and a mundane model. I confirm favourable features of the novel wavelet realised volatility estimator on crude oil and gold, and reject them in case of S&P 500. Possible explanation is an absence of jumps in this asset and hence over-adjustment of data for jumps by the estimator. In forecasting, the ANN models outperform the ARFIMA in terms of information content about dynamic structure of the time series.
Multifractal Analysis of Stock Market Prices
Čechová, Kristýna ; Krištoufek, Ladislav (advisor) ; Vošvrda, Miloslav (referee)
The aim of this thesis is to provide an empirical evidence of multifractality in financial time series and to discuss the relevance of this concept for the current financial theory. We have applied two methods, the Multifractal Detrended Fluctuation analysis and the Generalized Hurst exponent method, on components of the Dow Jones Industrial Average. We analyzed daily data of 30 companies traded on U.S. stock markets from 2002 to 2012. We present results supporting presence of multiscaling in open-close returns. Contrary to published literature, we were not able to find any significant multiscaling in volatility. Moreover based on our analysis, multiscaling is not present in standardized returns and as multifractality requires relatively complicated models, this is our most valuable result. 1
Wavelet-based Realized Variation and Covariation Theory
Baruník, Jozef ; Vošvrda, Miloslav (advisor) ; Kočenda, Evžen (referee) ; Di Matteo, Tiziana (referee) ; Veredas, David (referee)
English Abstract The study of volatility and covariation has become one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This dissertation contains a complete theory for realized variation and covariation estima- tion, generalizing current knowledge and taking the estimation into the time-frequency domain for the first time. The first part of the theory presents a wavelet-based realized variation theory, while the second part introduces its multivariate counterpart, a wavelet- based realized covariation theory. The results generalize the popular realized volatility framework by bringing robustness to noise as well as jumps and the ability to measure realized variation and covariation not only in the time domain, but also in the frequency domain. The theory is also tested in a numerical study of the small sample performance of the estimators and compared to other popular realized variation estimators under dif- ferent simulation settings with changing noise as well as jump level. The results reveal that our wavelet-based theory is able to estimate the realized measures with the greatest precision. Another notable contribution lies in the application of the presented theory. Our time-frequency estimators not only produce more efficient...
Stock Markets Analysis Using New Genetic Annealed Neural Network
Verner, Robert ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
The presented rigorosis 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:...
Yield Curve Modeling and the Effect of Macroeconomic Drivers: Dynamic Nelson-Siegel Approach
Patáková, Magdalena ; Šopov, Boril (advisor) ; Vošvrda, Miloslav (referee)
The thesis focuses on the yield curve modeling using the dynamic Nelson-Siegel approach. We propose two models of the yield curve and apply them on four currency areas - USD, EUR, GBP and CZK. At first, we distill the entire yield curve into the time-varying level, slope and curvature factors and estimate the parameters for individual currencies. Subsequently, we build a novel model investigating to what extent unobservable factors of the dynamic Nelson-Siegel model are determined by macroeconomic drivers. The main contribution of this thesis resides in the innovative approach to yield curve modeling with the application of advanced technical tools. Our primary objective was to increase the accuracy and the estimation power of the model. Moreover, we applied both models across different currency areas, which enabled us to compare the dynamics of the yield curves as well as the influence of the macroeconomic drivers. Interestingly, the results proved that both models we developed not only demonstrate strong validity, but also produce powerful estimates across all examined currencies. In addition, the incorporated macroeconomic factors contributed to reach higher precision of the modeling. JEL Classification: C51, C53, G17 Keywords: Nelson-Siegel, Kalman filter, Kalman smoother, Stace space formulation...
Regime-Switching Models and Their Application in the Financial Markets
Fišerová, Tereza ; Vošvrda, Miloslav (advisor) ; Skuhrovec, Jiří (referee)
The thesis is divided into two parts. The theoretical part introduces the reader to the theory of ARCH-type models and their extensions which are represented by the Markov regime-switching models that allow for capturing of structural breaks in the data dynamics. In addition, the first part summarizes the current state of art. In the empirical part, the model of Klaassen (2002) is adopted to find evidence on the existence of two distinct volatility regimes in four of the Central European stock markets (Austria, the Czech Republic, Germany and Poland). The model is also used as a tool for economic crises identification. Analysis of the daily and weekly observations covering the period from January 3, 2000 to December 31, 2010 provides three remarkable results. First, MRS-GARCH(1,1) model is adequate for modelling stock market volatility in Central Europe. Second, the high volatility regime tends to be associated with a financial crisis. Third, the current crisis is exceptional in terms of its duration in comparison with previous works' conclusions.

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