National Repository of Grey Literature 4 records found  Search took 0.01 seconds. 
Some modifications of models ARCH for financial time series
Nekvinda, Matěj ; Cipra, Tomáš (advisor) ; Zichová, Jitka (referee)
This work deals with modelling time series, especially their volatility, by methods based on the ARCH model. In the beginning, we describe the general features of financial time series, afterwards we focus on the ARCH model modifications. The described modifications are GARCH, EGARCH, GJR-GARCH and briefly GARCH-M, IGARCH, FIGARCH and QGARCH. Along with the models, there is a description of their behaviour, which frequently reflects some features of financial time series. We also mention the process of practical financial time series analysis. In the end, we demonstrate the application of GARCH, EGARCH and GJR-GARCH models for modelling values of FTSE 100 index together with diagnostic tests and prediction. Powered by TCPDF (
Bootstrap in financial time series
Krnáč, Ján ; Prášková, Zuzana (advisor) ; Hušková, Marie (referee)
In this diploma thesis we explain the main principles and properties of bootstrap methods, that can be used to conduct the statistical inference in linear and nonlinear financial time series. We will introduce basic ideas of bootstrap methods for the case when observations can be considered as independent random variables, and afterwards we will describe more advanced methods, that can be successfully used when we are dealing with time series. Thesis deals with both parametric bootstrap methods, that we can use when the underlying parametric model of observations is available, as well as with nonparametric bootstrap methods that are used when more general nonparametric model of time series data is considered. The main objective is to compare particular bootstrap methods and show the usage of these methods on real world data. There is also a basic time series theory included in the work. 1
Capital protected funds
Houdek, Ondřej ; Witzany, Jiří (advisor) ; Prokop, Martin (referee)
This thesis is mainly focused on pricing securities of selected capital protected funds. In its theoretical part, there are summarized approaches and principals that are generally used for derivatives pricing because capital protected funds' securities contain embedded options. Emphasis is put on risk-neutral pricing using Monte Carlo simulation at that point because complicated pay-off functions of these funds are hard to be evaluated analytically. There are also presented main approaches to constructions and portfolio management of these funds from their portfolio manager's viewpoint. Finally, there is made an overview of basic types of capital protected funds issued both in The Czech republic and Europe. Analytical part is focused on evaluation of selected capital protected funds. There is applied a standard approach that is based on a simulation of Geometric Brownian Motion with constant conditional variance and correlation in contrast with an advanced approach where the conditional variance and conditional correlation matrix are simulated as well. That is accomplished with GARCH-in-mean and DCC-GARCH models. Estimated prices are compared with real market prices and there is also performance of the standard models compared with performance of advanced ones.
Volatility Modeling of the PX Index
Dvořáčková, Anna ; Borovička, Adam (advisor) ; Zouhar, Jan (referee)
This thesis is focused on modeling of the real financial time series of the PX Index using linear and nonlinear volatility models. In the theoretical part the major terms and typical properties of the financial time series are presented and it is followed by the theoretical description of the linear and nonlinear volatility models including a general volatility model building. The key part of this thesis is the practical application of chosen linear and nonlinear volatility models on the time series of log returns of the PX Index. By using the real data set we verify if the volatility models are really capable of explaining the theoretical properties of the financial time series, such as volatility clustering, leptokurtic distribution and leverage effect.

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