03745ntm a22005537i 4500 000349315 CZ-PrVSE 20170326102757.0 m d cr n|||||||||| 170326s2016 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Zváč, Marek ISIS:81910 dis Portfólio Value at Risk a Expected Shortfall s použitím vysoko frekvenčních dat eng Portfolio Value at Risk and Expected Shortfall using High-frequency data / Marek Zváč 2016 ?? stran : digital, PDF soubor Vedoucí práce: Milan Fičura Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví, 2017 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2017 The main objective of this thesis is to investigate whether multivariate models using Highfrequency data provide significantly more accurate forecasts of Value at Risk and Expected Shortfall than multivariate models using only daily data. Our objective is very topical since the Basel Committee announced in 2013 that is going to change the risk measure used for calculation of capital requirement from Value at Risk to Expected Shortfall. The further improvement of accuracy of both risk measures can be also achieved by incorporation of high-frequency data that are rapidly more available due to significant technological progress. Therefore, we employed parsimonious Heterogeneous Autoregression and its asymmetric version that uses high-frequency data for the modeling of realized covariance matrix. The benchmark models are chosen well established DCC-GARCH and EWMA. The computation of Value at Risk (VaR) and Expected Shortfall (ES) is done through parametric, semi-parametric and Monte Carlo simulations. The loss distributions are represented by multivariate Gaussian, Student t, multivariate distributions simulated by Copula functions and multivariate filtered historical simulations. There are used univariate loss distributions: Generalized Pareto Distribution from EVT, empirical and standard parametric distributions. The main finding is that Heterogeneous Autoregression model using high-frequency data delivered superior or at least the same accuracy of forecasts of VaR to benchmark models based on daily data. Finally, the backtesting of ES remains still very challenging and applied Test I. and II. did not provide credible validation of the forecasts. Způsob přístupu: Internet finanční inženýrství [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas HAR Realized covariance Expected Shortfall Value at Risk Portfolio Copula EVT High-frequency data Fičura, Milan vse2015859798 ths Janda, Karel xx0053102 opn Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví kn20010709398 dgg https://insis.vse.cz/zp/55453/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/55453 Hlavní práce https://insis.vse.cz/zp/55453/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/55453/posudek/oponent/51075 Oponentura https://insis.vse.cz/zp/55453/podrobnosti dc:identifier NEPOSILAT VSKP vse55453 170324 55453