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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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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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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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Product processes as a tool for financial analysis
Krejčí, Kateřina ; Zichová, Jitka (advisor) ; Hurt, Jan (referee)
This bachelor thesis discusses product processes as a tool for modeling financial time series. The thesis is divided into the theoretical and the practical part. Basic issues are summarized in the theoretical part. Properties of some moments and correlations are described and derived in this part, parameter estimates of a product process are derived subsequently. The practical part deals further with the parameter estimates. The quality of derived parameter estimates is verified in a simulation study in software Mathematica 9 and the proposed estimates are applied to real financial data. Powered by TCPDF (www.tcpdf.org)
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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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Forecasting with neural network during covid-19 crisis
Luu Danh, Tiep ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
The thesis concerns the topic of forecasting using Neural Networks, particu- larly the return and volatility forecasting in the volatile period of Covid-19. The thesis uses adjusted close daily data from Jan 1, 2000, until Jan 1, 2021, of the S&P index and Prague Exchange Stock index (PX). The comparison was between the vanilla econometrical model, a neural network model, and a hybrid neural network model. Hybrid neural networks were constructed with an additional feature column of the fitted econometrical model. Additionally to comparing the prediction, a risk-return trade-o analysis of the forecasted series was conducted. The test period for all models was from Jan 1, 2020, until Jan 1, 2021, where predictions were made. During the test period, MSE be- tween predicted and true values was extracted and compared. The results are that the hybrid model outperformed both econometrical as well as only neural networks models. Furthermore, the risk-return trade-o forecast provided by the hybrid model fares better than the other ones. JEL Classification C53, C81 Keywords Financial Time Series, Forecasting, Neural Net- works, ARIMA, GARCH Title Forecasting with Neural Network during Covid- 19 Crisis Author's e-mail tiep.luud@gmail.com Supervisor's e-mail barunik@fsv.cuni.cz
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Recursive estimates of financial time series
Vejmělka, Petr ; Cipra, Tomáš (advisor)
This work aims to describe the method of recursive estimation of time series with conditional volatility, used mainly in finance. First, there are described the basic types of models with conditional heteroskedasticity (GARCH) and princi- ples of state-space modeling demonstrated by means of linear models AR and ARMA. Subsequently, there are derived algorithms for recursive estimation of parameters of the GARCH model and its possible modifications including the ones for which recursive estimation formulas have not been yet derived in lit- erature. These algorithms are tested in a simulation study, where their appli- cability in practice is investigated. Finally, we apply these algorithms to real high-frequency data from the stock exchange. The practical part is done us- ing the software Mathematica 11.3. The work also serves as an overview of the current state of online modeling of financial time series. 1
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Recursive estimates of financial time series
Vejmělka, Petr ; Cipra, Tomáš (advisor)
This work aims to describe the method of recursive estimation of time series with conditional volatility, used mainly in finance. First, there are described the basic types of models with conditional heteroskedasticity (GARCH) and princi- ples of state-space modeling demonstrated by means of linear models AR and ARMA. Subsequently, there are derived algorithms for recursive estimation of parameters of the GARCH model and its possible modifications including the ones for which recursive estimation formulas have not been yet derived in lit- erature. These algorithms are tested in a simulation study, where their appli- cability in practice is investigated. Finally, we apply these algorithms to real high-frequency data from the stock exchange. The practical part is done us- ing the software Mathematica 11.3. The work also serves as an overview of the current state of online modeling of financial time series. 1
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