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CRUDE OIL PREDICTION FOR COMPANIES IN ENERGY DEMANDING PRODUCTION
Vícha, Tomáš ; Dohnal, Mirko (advisor)
The dissertation deals with prediction of crude oil price and is tailor-made for such companies which are heavily crude oil related. The main dissertation target is to make sure that such companies can get ready for price changes and safeguard themselves against negative consequences. Crude oil prices are the main factor which affects prices of such final products as petrol. It is a well known fact that quantitative predictions are not reliable and all those who are forced to real on such vague data set for their decision-making are reluctant to use them. That’s how we would like to have at least the correct trend information. The dissertation introduces some concepts originally developed within artificial intelligence theory for the crude oil predictions. Specifically common sense algorithms and qualitative interpretation of some aspects of theory of chaos are the main contribution towards expanding of available prediction tools described by the dissertation. A systematic analysis of a sequence of qualitative solutions is the key part of the dissertation.
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Technical analysis of financial time series
Faltýnková, Anežka ; Petrásek, Jakub (advisor) ; Hurt, Jan (referee)
The thesis studies the problem of inefficiencies in the finan- cial markets. The first section describes the fundamental concepts, such as the efficient market hypothesis and futures contracts. The necessary mathematics is summarized in the second part, which deals with the link between the futures price and the martingale. The nonlinear regression is introduced and the greatest emphasis is placed on the description of the functional linear model with a scalar response. The main part focuses on the application of this theory. Two models are proposed for predicting prices based on their historical changes. The first model is nonlinear and is based on the assumption that the impact of the price change on the prediction process diminishes exponentially with time. The second one is linear and directly estimates the effect of particular changes. Both models are compared in terms of their ability to predict inefficiencies, calculation costs and stability. 1
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Multivariate Financial Time Series
Veselý, Daniel ; Cipra, Tomáš (advisor) ; Kopa, Miloš (referee)
In this work we will describe methods for modeling multivariate financial time series. We will concentrate on both modeling expected value by multi- variate Box-Jenkins processes and primarily on modeling conditional corre- lations and volatility. Our main object will be DCC (Dynamic Conditional Correlation) model, estimation of its parameters and some other general- izations. Then we will programme DCC model in statistical software R and apply on real data. In applications we will concentrate on problem of high dimension of financial time series and on modeling conditional correlations data with outliers.
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Analysis of financial time series with economical news headlines
Kalibán, František ; Petrásek, Jakub (advisor) ; Zichová, Jitka (referee)
This thesis is focused on options of improving the estimate of volatility of the given financial time series by analysing the economical news headlines. Because of very large volume of data and correlation between word occurence in headlines, the Principal Component Analysis is used to reduce the dimension of data space. For the elimination of significantly large skewness of dependent variable and the preservation of its normality a Box-Cox transformation is used. Finally, a linear model is constructed and its robustness is analyzed by cross-validation method. The computations were made by R software.
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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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Multivariate models of volatility
Vejmělka, Petr ; Cipra, Tomáš (advisor) ; Zichová, Jitka (referee)
In this work, we deal with the modeling of multivariate financial time series. First, linear models of multivariate time series are described and further special features of the financial time series. In the next part of the thesis, we focus on modeling multivariate volatility and present several models that can be used in this context. In the practical part of the work, we apply some of these models on real data using the software systems EViews 9 and RATS 8. As the first one, we analyze gradually two-dimensional and five-dimensional financial time series. The aim of thesis is to survey the temporary state of multivariate volatility modeling in financial time series including practical experience with specialized software. 1
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Korelační analýza akciových indexů pomocí Empirical Mode Decomposition
Ulyanin, Alexey ; Černý, Michal (advisor) ; Formánek, Tomáš (referee)
This thesis studies dependence of ?nancial time series, represented by stock indices of geographically separated economics. Daily prices of selected stock indices from 01.05.1988 to 20.04.2017 are used for the analysis. In the fi?rst section the dataset is described. The second section goes through methodology for the analysis, including empirical mode decomposition (EMD) algorithm, with the help of which the initial time series can be transformed into a few time series, that are mostly independent on each other, for further analysis (for example, correlation analysis). EMD is an interesting method of signal processing, which may help to look at the time series analysis from a different perspective. The thesis should extend the work of Guhathakurta et al. (2008) and extend the time scale and number of indices. Correlation analysis is also performed on the initial time series and the transformed ones. The aim of this thesis is to prove, whether stock indices of geographically separated economics have similarities in their behavior and test whether they are dependent on each other, by using methods from Guhathakurta et al. (2008) extended by correlation analysis.
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Volatility models in R
Vágner, Hubert ; Bašta, Milan (advisor) ; Flimmel, Samuel (referee)
This diploma thesis focuses on modeling volatility in financial time series. The main approach to modelling volatility is using GARCH models which can capture the variability of conditional volatility of time series. For modelling a conditional mean value in time series are used ARMA models. In the series there are usually not fulfilled the assumption of earnings normality, therefore, are the earnings in most cased characterized by the leptokurtic shape of distribution. The thesis introduces some more distribution types, which can be more easily used for the earnings distribution - above all the Students t distribution. The aim of the thesis in the first part is to present the topic of financial time series and description of the GARCH models including their further modification. There are used e.g. IGARCH or other models capturing asymmetric impact of shocks such as GJR-GARCH. The second part deals with generated data, where are more in detail explored the volatility models and their behavior in corresponding financial time series. The third part focuses on the volatility estimation and forecasting for the financial time series. Firstly this concerns development of stock index MICEX secondly currency pair Russian Ruble to Czech Crown and eventually price development of the Brent crude oil. The goal of the third part is to present the impacts on volatility of chosen time series applied on the example of economic sanctions against Russia after annexation of the Crimea peninsula which happened in the first quarter 2014.
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Selected problems of financial time series modelling
Hendrych, Radek ; Cipra, Tomáš (advisor) ; Arlt, Josef (referee) ; Prášková, Zuzana (referee)
Title: Selected problems of financial time series modelling Author: Radek Hendrych Department: Department of Probability and Mathematical Statistics (DPMS) Supervisor: Prof. RNDr. Tomáš Cipra, DrSc., DPMS Abstract: The present dissertation thesis deals with selected problems of financial time series analysis. In particular, it focuses on two fundamental aspects of condi- tional heteroscedasticity modelling. The first part of the thesis introduces and discusses self-weighted recursive estimation algorithms for several classic univariate conditional heteroscedasticity models, namely for the ARCH, GARCH, RiskMetrics EWMA, and GJR-GARCH processes. Their numerical capabilities are demonstrated by Monte Carlo experiments and real data examples. The second part of the thesis proposes a novel approach to conditional covariance (correlation) modelling. The suggested modelling technique has been inspired by the essential idea of the multivariate orthogonal GARCH method. It is based on a suitable type of linear time-varying orthogonal transformation, which enables to employ the constant conditional correlation scheme. The correspond- ing model is implemented by using a nonlinear discrete-time state space representation. The proposed approach is compared with other commonly applied models. It demon- strates its...
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MCMC methods for financial time series
Tritová, Hana ; Pawlas, Zbyněk (advisor) ; Komárek, Arnošt (referee)
This thesis focuses on estimating parameters of appropriate model for daily returns using the Markov Chain Monte Carlo method (MCMC) and Bayesian statistics. We describe MCMC methods, such as Gibbs sampling and Metropolis- Hastings algorithm and their basic properties. After that, we introduce different financial models. Particularly we focus on the lognormal autoregressive model. Later we theoretically apply Gibbs sampling to lognormal autoregressive model using principles of Bayesian statistics. Afterwards, we analyze procedu- res, that we used in simulations of posterior distribution using Gibbs sampling. Finally, we present processed output of both simulated and real data analysis.
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