Credit score model via GAS model and logistic regression – Nela Hendrichová
Nela Hendrichová
Master's thesis
Credit score model via GAS model and logistic regression
Credit score model via GAS model and logistic regression
Abstract:
Tato práce zkoumá a porovnává efektivitu modelů kreditního skóre a představuje nový model s časově proměnnými parametry, tzv. GAS-logistický regresní model. Tento model kombinuje logistickou regresi, tradiční metodu pro předpovídání pravděpodobnosti selhání ve finančních institucích, s dynamikou modelů Generalized Autoregressive Score (GAS). Cílem je zjistit, zda-li integrace GAS modelů přináší zlepšení …moreAbstract:
This thesis examines and compares the effectiveness of credit scoring models and intro- duces a new one with time-varying parameters, the so-called GAS-logistic regression model. The model integrates logistic regression, the traditional approach for predicting the probability of default in financial institutions, with dynamic features of Generalized Autoregressive Score (GAS) models. The objective …more
Language used: English
Date on which the thesis was submitted / produced: 29. 4. 2024
Identifier:
https://vskp.vse.cz/eid/92596
Thesis defence
- Date of defence: 6. 6. 2024
- Supervisor: Ondřej Sokol
- Reader: Petra Tomanová
Citation record
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- autentizovaným zaměstnancům ze stejné školy/fakulty
Other ways of accessing the text
Institution archiving the thesis and making it accessible: Vysoká škola ekonomická v Prazehttps://vskp.vse.cz/eid/92596
Vysoká škola ekonomická v Praze
Master programme:
Ekonometrie a operační výzkum
Theses on a related topic
-
Finite Sample Properties of Generalized Autoregressive Score Models
Jan Švrčina -
Prediction models in e-sports: Generalized autoregressive score and common opponent models
Miroslav Pikhart -
Score-driven Models for Value at Risk and Expected Shortfall
Kateřina Nováková -
Finite Sample Properties of Maximum Likelihood Estimators in Score-Driven Volatility Models
Vojtěch Beneš -
Dynamic Score-Driven Models
Petra Tomanová -
Score-Driven Models for Air Temperature Analysis
Alžběta Šumníková -
Novel Score-Driven Models Using Extreme Value Distributions
Tereza Mokrenová -
Portfolio Value at Risk and Expected Shortfall using High-frequency data
Marek Zváč