Projects - Credit Risk - ENPC

Project 2. Project 3. Project 4. Credit Risk. Projects. Lo¨ıc BRIN and François CRENIN. École Nationale des Ponts et Chaussées. Département Ingénieurie ...
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Credit Risk Projects Lo¨ıc BRIN and Fran¸cois CRENIN

´ Ecole Nationale des Ponts et Chauss´ ees D´ epartement Ing´ enieurie Math´ ematique et Informatique (IMI) – Master II

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Project 1 Using boosting algorithms to estimate the Probability of Default

Project 1: Using boosting algorithms to estimate the Probability of Default Description of the project: This project aims at comparing several tree-based boosting algorithms when trying to predict the probability of default. Target: Clear explanations of three boosting algorithms, their implementations and a structured comparison of their results after training them on the Credit database. More common algorithms such as RF or SVM could be used as benchmark. Database: Retail Banking Credit Data

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Project 2 Feature engineering on market data to estimate the Probability of Default

Project 2: Feature engineering on market data to estimate the Probability of Default Description of the project: This project consists in transforming raw market data into features used as inputs of Machine learning algorithms to predict a probability of default of a set of companies. Target: Use what you have learnt from the lectures and experiments on the database to built features. These features will only be used as inputs in the algorithms seen in the lectures (logistic regression, basic trees, bagging, RF and SVM). Database: Market Data

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Project 3 Using different copulas to price Collateralized Synthetic Obligations

Project 3: Using different copulas to price Collateralized Synthetic Obligations Description of the project: The purpose of this project to test different extensions of Vasicek models using different copulas to link defaults, and see how the pricing of the tranches’ spreads evolves and compare to the spreads quoted for the CSO based on the NA IG CDX 5Y S29 index on past data. Target: Pricers with different underlying copulas to be used. References: [Burtschell et al., 2009] Database: NA IG CDX 5Y S29 CSO tranches spreads in octobre 2017.

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Project 4 CVA Wrong Way Risk Multiplier Decomposition and Efficient CVA Curve

Project 4: CVA Wrong Way Risk Multiplier Decomposition and Efficient CVA Curve Description of the project: The purpose of this project is to price a 3-year Italian CDS that you want to buy to Intesa Sanpaolo taking into account Counterparty Value Adjustement (CVA) and Wrong Way Risk (WWR). Target: A pricer. References: [Pang et al., 2015] Database: CDS data from Intesa Sanpaolo, Italy and 3M EURIBOR rates, daily, from 2009 to october 2017.

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Burtschell, X., Gregory, J., and Laurent, J. (2009). A comparative analysis of CDO pricing models. Link. Pang, T., Chen, W., and Li, L. (2015). CVA Wrong Way Risk Multiplier Decomposition and Efficient CVA Curve. Link.

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