Survival Analysis in LGD Modeling

The paper proposes an application of the survival time analysis methodology to estimations of the Loss Given Default (LGD) parameter. The main advantage of the survival analysis approach compared to classical regression methods is that it allows exploiting partial recovery data. The model is also modified in order to improve performance of the appropriate goodness of fit measures. The empirical testing shows that the Cox proportional model applied to LGD modeling performs better than the linear and logistic regressions. In addition a significant improvement is achieved with the modified “pseudo” Cox LGD model.credit risk, recovery rate, loss given default, correlation, regulatory capital

Scoring Models in Finance (Skóringové modely ve financích)

The aim of the present work is to describe the application of the logistic regression model to the field of probability of default modeling, and provide a brief introduction to the scoring development process used in financial practice. We start by introducing the theoretical background of the logistic regression model; followed by a consequent derivation of three most common scoring models. Then we present a formal definition of the Gini coefficient as a diversification power measure and derive the Somers-type formulas for its estimation. Finally, the key part of this work gives an overview of the whole scoring development process illustrated on the examples of real business data

Mathematical Models for LGD

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

Nové metody ve schvalování úvěrů

This thesis contributes to the field of applied statistics and financial modeling by analyzing mathematical models used in retail credit underwriting processes. Specifically, it has three goals. First, the thesis aims to challenge the performance criteria used by established statistical approaches and propose focusing on predictive power instead. Secondly, it compares the analytical leverage of the established and other suggested methods according to the newly proposed criteria. Third, the thesis seeks to develop and specify a new comprehensive profitability-based underwriting model and critically reflect on its strengths and weaknesses. In the first chapter I look into the area of probability of default modeling and argue for comparing the predictive power of the models in time rather than focusing on the random testing sample only, as typically suggested in the scholarly literature. For this purpose I use the concept of survival analysis and the Cox model in particular, and apply it to a real Czech banking data sample alongside the commonly used logistic regression model to compare the results using the Gini coefficient and lift characteristics. The Cox model performs comparably on the randomly chosen validation sample and clearly outperforms the logistic regression approach in the predictive power. In the second chapter, in the area of loss given default modeling I introduce two Cox-based models, and compare their predictive power with the standard approaches using the linear and logistic regression on a real data sample. Based on the modified coefficient of determination, the Cox model shows better predictions. Third chapter focuses on estimating the expected profit as an alternative to the risk estimation itself and building on the probability of default and loss given default models, I construct a comprehensive profitability model for fix-term retail loans underwriting. The model also incorporates various related risk-adjusted revenues and costs, allowing more precise results. Moreover, I propose four measures of profitability, including the risk-adjusted expected internal rate of return and return on equity and simulate the impact of the model on each of the measures. Finally, I discuss some weaknesses of these approaches and solve the problem of finding default or fraud concentrations in the portfolio. For this purpose, I introduce a new statistical measure based on a pre-defined expert critical default rate and compare the GUHA method with the classification tree method on a real data sample. While drawing on the comparison of different methods, this work contributes to the debates about survival analysis models used in financial modeling and profitability models used in credit underwriting

Step by step credit risk model construction

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..