National Repository of Grey Literature 5 records found  Search took 0.00 seconds. 
Kapitálový požadavek ke kreditnímu riziku
Burešová, Jana ; Charamza, Pavel (referee) ; Keprta, Stanislav (advisor)
In the present work we study the process of determination of capital requirement for credit risk that is recommended by the Basel Commitee for Banking Supervision to implement into national legislation and that is also obligatory for all European banks since it is a part of the Capital Requirement Directive. At the beginning of this thesis, basic principles and three pillars of The New Basel Capital Accord (2004), better known as Basel II, are described. After focusing on the part of the rst pillar dealing with credit risk, di erent approaches to credit risk measurement are introduced. The most important formula for the advanced internal-ratings based approach is then analyzed under the settings of mathematical models it is based on. In the last chapter, the output of the formula for capital requirement calculated for a given hypothetic portfolio is compared to the estimate of unexpected loss, that the requirement should correspond to.
Step by step credit risk model construction
Rychnovský, Michal ; Hanzák, Tomáš (referee) ; Charamza, Pavel (advisor)
Nazev pracc: Postnpna vyslavba modelu ohoduoconi kroditniho ri/,ika Autor: Michal Ryclmovsky Katedra: Kaledra pravdepoelobnejsti a maternal icke statistiky Vedouci bakalafske pracc: RNDr. Pavel Charam/a, CSc. E-mail vedouciho: pavol.charani/a''^media research.ex Abstrakt: Ciloni toto pracc je pfibli/it podstatu vvstavby skoringovych mo- eleln. Popisnjeme zde metodu logisticke regrese, odhaelovani jejich paramotrn a testovani jcjicli vy/,nanmosti. Na /aklado, proiiioiniych odds ratio potoin zavadimo indei>endence model jako odhad podminone saneo s]>laceni klienta.. Tento ... dale zoljecnHJinne pfidavanini vah jedmjtlivyni sku])inani a ka- tegoriini charakt.eristik klienta.. Ta.kto pficha/Jnie k WOE niodeln a jjlnemu logistickemn niodeln. Vennjeine se take nicfeni divcr/ilikacni schopnosti ino- deln pomoci Lorenxovy kfivky a Somerovy d statistiky jako odhadu Giuiho koeficientn. Nakonec a])likujeine popsane nietody na praktiekon vystavbn yk(')riiigovych niodeln a na realnych dateeh porovnanie vhodnost a di\erx,ifi- kacni scho])nost pi'edstavovanych niodelu. Soneast.i ])race je take vystup na. int.ernetovon encyklo]>edii \\ikiiiedia. Klicova slova: kreditni rixiko, skoringove niodely, logisticka. 1'egrese. Title: Step by step credit risk model construction Author: Michal Rychnovsky Department: Department...
Mathematical Models for LGD
Rychnovský, Michal ; Zvára, Karel (referee) ; Charamza, Pavel (advisor)
The aim of the present work is to describe possible models for LGD estimation and to test them on the real data. Besides common linear and logistic regression models we aim to describe the methods using running and censored observations - based on the Cox model and the two-step regression. This work first briefly outlines the principle of the capital requirement according to the Basel II. Then, individual methods are described and finally applied to the real banking data.
Credit Risk Valuation
Pleška, Martin ; Benková, Markéta (referee) ; Charamza, Pavel (advisor)
According to the rules stated in the Basel II document banks are obliged to calculate risk capital on the basis of expected value of credit risk and in particular on the basis of some of its characteristics among which is Value at Risk (VaR) also ranked. It can be calculated for example by the method stated in Creditmetrics paper. In this thesis we will focus on this method of calculation of VaR which is considered to be a measure of credit risk. Determination of expected value of portfolio which credit risk we are concerned about is in this paper demonstrated by two methods. First one is the method of discounted cash řow and the second one is the method of risk costs. Estimations of VaR are being performed through the use of simulation of distribution of the value of the portfolio. The work is amended by a particular calculation with real data.

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