National Repository of Grey Literature 183 records found  previous11 - 20nextend  jump to record: Search took 0.00 seconds. 
Financial time series modelling with trend
Studnička, Václav ; Zichová, Jitka (advisor) ; Prášková, Zuzana (referee)
Various models can be used for the analysis of financial time series. This thesis focuses mainly on two models; non-linear trend model and linear trend model. First chapter is theoretial, there is an introduction to the theory of time series and to the autoregressive process. Second chapter is also theoretical and it focuses on a description of both non-linear and linear trend model including derivations of im- portant properties of these models; moreover, it contains theory for the modelling of financial time series and predictions. Last chapter contains simulations of two mentioned models and estimations of their parameters, Wolfram Mathematica is used for all simulations. 1
Nonlinear ARMA model
Šabata, Marek ; Lachout, Petr (advisor) ; Prášková, Zuzana (referee)
The thesis regards theory of nonlinear ARMA models and its application on financial mar- kets data. First of all, we present general framework of time series modeling. Afterwards the theory of linear ARMA models is layed out, since it plays a key role in the theory of nonlinear models as well. The nonlinear models presented are threshold autoregressive model (TAR), autoregressive conditional heteroscedastic model (ARCH) and generalized autoregressive conditional heteroscedastic model (GARCH). For each model, we derive a method for esti- mating the model's parameters, asymptotic properties of the estimators and consequently confidence regions and intervals for testing hypotheses about the parameters. The theory is then applied on financial data, speficically on the data from Standard and Poor's 500 index (S&P500). All models are implemented in statistical software R. 1
Analysis of Economic Indicators of the Selected Company Using Statistical Methods
Komárková, Ivana ; Šustrová, Tereza (referee) ; Novotná, Veronika (advisor)
The bachelor thesis deals with the financial situation of the company RAPOS, Ltd. The financial analysis reveals weaknesses of the company. On these weaknesses, will be proposed measure. Results of analyses are analysed using statistical methods. Statistical methods determine the prediction of individual economic indicators for the following year.
Maximum likelihood estimators in time series
Tritová, Hana ; Pawlas, Zbyněk (advisor) ; Zikmundová, Markéta (referee)
The thesis deals with maximum likelihood estimators in time series. The reader becomes familiar with three important models for time series: autoregressive model (AR), moving average model (MA) and autoregressive moving average (ARMA). Thereafter he can find out the form of their main characteristics, e.g. population mean and variance. Then there is the derivation of parameter estimates - generally and for mentioned models of times series. There are also stated two other methods for finding estimators of AR(1) and MA(1) parameters - method of moments and least squares method. The end is dedicated to examples which compares all three methods.
Statistical analysis and modeling of inflation
Baniar, Matúš ; Zichová, Jitka (advisor) ; Hurt, Jan (referee)
Title: Inflation modeling Author: Matúš Baniar Department: Department of probability and mathematical statistics Supervisor: RNDr. Jitka Zichová Dr., Department of probability and mathematical statistics Abstract: Inflation, the growth of the general price level, is a common economic phenomenon, which is a macroeconomic problem. The thesis deals with the me- thods by which it is possible to model inflation and therefore to understand its de- velopment. In the first case, the correlation and regression analysis, which deal with the relationship of two or more variables and the following selection of the appro- priate mathematical model. The model of linear regression is described also with methods by which we analyze its adequacy. Another described method is the analy- sis of one-dimensional time series, which we apply so called Box-Jenkins methodol- ogy. Both approaches are illustrated on real financial data using the software Wol- fram Mathematica 8. Keywords: inflation, correlation analysis, regression analysis, time series
Hypotheses Testing in Financial Time Series
Kubů, Jan ; Zichová, Jitka (advisor) ; Jonáš, Petr (referee)
Financial data often take the form of time series. In such cases, their analysis is performed using statistical methods for time series. The thesis describes selected parametric and nonparametric tests of random walk hypothesis. Tests are designed against common mutual correlation alternatives but also against trend and cyclic data structure alternatives. The thesis provides the theoretical basis of these tests and their application to real financial data.
Nonlinear nonparametric models for financial time series
Klačanská, Júlia ; Zichová, Jitka (advisor) ; Cipra, Tomáš (referee)
The thesis studies nonlinear nonparametric models used in time series analy- sis. It gives basic introduction to the time series and states different nonlinear nonparametric models including their estimates. Special attention is paid to three of them, CHARN, FAR and AFAR model. Their properties and esti- mation techniques are presented. We also show techniques that select values of the parametres used further in estimation methods. The properties of time series models are investigated in simulation and real data studies. 1
Software products for financial time series analysis
Vlasáková, Romana ; Zichová, Jitka (advisor) ; Cipra, Tomáš (referee)
The present work deals with selected methods suitable to work with financial time series. Firstly, univariate linear models ARMA are introduced, followed by the description of volatility models ARCH and their generalization to GARCH models. There are many modifications of standard GARCH models designed with respect to the nature of financial data, some of which are presented. Another part of the work dealing with multiple time series focuses on VAR models and bivariate GARCH models. The most important part of the work are practical examples of building the theoretically described models in various types of software with built-in procedures for time series analysis. We apply five different types of commercial and non-commercial software, namely EViews, Mathematica, R, S-PLUS and XploRe. The used software products are presented and compared in terms of their capabilities and the results obtained for particular methods.
Seasonality and periodicity in time series
Musil, Karel ; Jonáš, Petr (advisor) ; Cipra, Tomáš (referee)
This work deals with periodicity and seasonality in time series. After a time series periodicity topic is introduced, a seasonal component of time series and a seasonal adjustment is presented. Then basic approaches, used in current practice, are introduced. These are classic model approach, Box-Jenkins methodology, and spectral analysis. The described seasonal adjustment techniques are applied to the time series of the Czech import, export, and foreign trade balance. A brief description of potential problems, which are connected to the seasonal adjustment and are common in practice, is a part of the example as well.
Backtesting of Time Series Models
Stroukalová, Marika ; Houfková, Lucia (advisor) ; Zichová, Jitka (referee)
Title: Backtesting of Time Series Models Author: Marika Stroukalová Department: Department of Probability and Mathematical Statistics Supervisor: Mgr. Lucia Jarešová Supervisor's e-mail address: Abstract: In the present work we study the basic models of financial time series (ARMA, GARCH), we focus on parameter estimation and forecasting in estimated models. We describe the means of estimating parametres and future values in the program R. In the theoretical section we also discuss the features of financial time series, define simple returns and log returns and we introduce the benefits of the log returns. We also apply the white noise model, ARMA(1,1) and GARCH(1,1) on historic time series of logarithmic returns of chosen stock exchange indices, we also backtest 1-step ahead fore- cats and 5-step ahead forecasts and we compare the results of these models. By empirical comparison of real data we also analyze how the models reac- ted on the present financial crisis and evaluate how the normal distribution assumption for the data held up. Keywords: time series, ARMA, GARCH, backtesting. 1

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