Essays about: "Credit modelling"

Showing result 6 - 10 of 49 essays containing the words Credit modelling.

  1. 6. Applying the Shadow Rating Approach: A Practical Review

    University essay from KTH/Matematik (Avd.)

    Author : Viktor Barry; Carl Stenfelt; [2023]
    Keywords : Shadow Rating; probability of default; low default portfolio; credit risk; statistical learning; financial regulation; Basel; Pluto and Tasche; Skuggrating; sannolikhet av fallissemang; lågfallissemangsportfölj; kreditrisk; statistisk inlärning; finansiella regelverk; Basel; Pluto och Tasche;

    Abstract : The combination of regulatory pressure and rare but impactful defaults together comprise the domain of low default portfolios, which is a central and complex topic that lacks clear industry standards. A novel approach that utilizes external data to create a Shadow Rating model has been proposed by Ulrich Erlenmaier. READ MORE

  3. 7. Credit Scoring Based on Behavioural Data

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Daniel Bouvin; Erik Hamberg; [2022]
    Keywords : Banking; Behavior; Behaviour; Credit Modelling; Klarna; Logistic Regression; Machine Learning; Neural Networks; Random Forests; XGBoost;

    Abstract : Credit modelling has traditionally been done by credit institutes based on financial data about the individuals requesting the credit. While this has been sufficient in lowering risk in developed economies with plenty of financial data it is inefficient in developing economies and fails to reach the unbanked population. READ MORE

  4. 8. Credit risk modelling and prediction: Logistic regression versus machine learning boosting algorithms

    University essay from Uppsala universitet/Statistiska institutionen

    Author : Linnéa Machado; David Holmer; [2022]
    Keywords : Machine learning; Credit risk; Logistic regression; Decision trees;

    Abstract : The use of machine learning methods in credit risk modelling has been proven to yield good results in terms of increasing the accuracy of the risk score as- signed to customers. In this thesis, the aim is to examine the performance of the machine learning boosting algorithms XGBoost and CatBoost, with logis- tic regression as a benchmark model, in terms of assessing credit risk. READ MORE

  5. 9. Pricing of Embedded Options: Implementing Stochastic Interest Rates & Stochastic Spread

    University essay from Lunds universitet/Matematisk statistik

    Author : Jan Müller; [2022]
    Keywords : Option pricing; Callable bonds; Affine term structure models; Hull-White one-factor; Hull White two-factor; Trinomial trees; Short rate; Default intensity; Swaption volatilities; Black-76; Credit derivatives; Calibration; Optimisation.; Mathematics and Statistics;

    Abstract : Given the current market climate, in an era of negative interest-rates, the Hull-White model has regained popularity in the eyes of investors. This thesis aims to extend this model to incorporate credit risk, to allow the modelling of credit derivatives such as diff swaps, defaultable corporate bonds and credit default swaps. READ MORE

  6. 10. ESTIMATING AND EVALUATING THE PROBABILITY OF DEFAULT – A MACHINE LEARNING APPROACH

    University essay from Uppsala universitet/Statistiska institutionen

    Author : Andreas Hild; [2021]
    Keywords : ;

    Abstract : In this thesis, we analyse and evaluate classification models for panel credit risk data. Variables are selected based on results from recursive feature elimination as well as economic reasoning where the probability of default is estimated. READ MORE