Essays about: "probability of default"

Showing result 6 - 10 of 97 essays containing the words probability of default.

  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

  2. 7. Peeking Through the Leaves : Improving Default Estimation with Machine Learning : A transparent approach using tree-based models

    University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

    Author : Elias Hadad; Angus Wigton; [2023]
    Keywords : Machine learning; Expected credit loss; Probability of default; ECL; PD; Risk Management; Credit Risk Management; Default Estimation; AI; Artificial intelligence; Fintech; Supervised learning; Decision tree; Random forest; XG boost; Transparency; Machine learning transparency;

    Abstract : In recent years the development and implementation of AI and machine learning models has increased dramatically. The availability of quality data paving the way for sophisticated AI models. Financial institutions uses many models in their daily operations. READ MORE

  3. 8. A multi-gene symbolic regression approach for predicting LGD : A benchmark comparative study

    University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

    Author : Hanna Tuoremaa; [2023]
    Keywords : Symbolic regression; loss given default; credit risk; logit transformed regression; beta regression; multi-gene genetic programming; regression tree;

    Abstract : Under the Basel accords for measuring regulatory capital requirements, the set of credit risk parameters probability of default (PD), exposure at default (EAD) and loss given default (LGD) are measured with own estimates by the internal rating based approach. The estimated parameters are also the foundation of understanding the actual risk in a banks credit portfolio. READ MORE

  4. 9. Credit Where Credit's Due: An Empirical Study of Defaults in the Swedish Corporate Bond Market Between 2004 and 2023

    University essay from Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi

    Author : Kristoffer Östlin; Ludvig Sviberg; [2023]
    Keywords : Corporate Bonds; Yield Spread; Default; Swedish Bond Market; Logit Regression Model;

    Abstract : This paper studies the relationship between bond defaults and yield spread, and other bond and company variables observable at issuance using a comprehensive dataset of matured and defaulted bonds from non-financial Swedish firms, covering the period 2004-2023. We find that higher yield spreads are correlated with higher default probabilities, particularly in the high-yield (HY) bond segment. READ MORE

  5. 10. Artificial Neural Networks and Inductive Biases for Multi-Instance Multi-Modal Tabular Data : A Case Study for Default Probability Estimation in Small-to-Medium Enterprise Lending

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

    Author : Gustav Röhss; [2022]
    Keywords : ;

    Abstract : The success of artificial neural networks in homogeneous data domains such as images, textual data, and audio and other signals has had considerable impact on Machine Learning and science in general. The domain of heterogeneous tabular data, while arguably much more common, remains much less explored with regards to artificial neural networks and deep learning. READ MORE