Credit Risk Modeling in Python: Assess and Manage Risk
Master the principles and practical applications of credit risk modeling using Python. Develop robust models to assess and manage financial risk effectively.
Course Syllabus: Credit Risk Modeling in Python
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Introduction:
Overview of credit risk, its importance in finance, and how Python is used in modeling and analysis.
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Setting up the working environment:
Configure your Python environment with necessary libraries and tools for credit risk modeling projects.
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Dataset description:
Understand the structure and characteristics of real-world credit risk datasets used for modeling.
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General preprocessing:
Learn essential data cleaning, transformation, and preparation techniques specific to financial datasets.
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PD model data preparation:
Prepare data specifically for Probability of Default (PD) model development, handling missing values and feature engineering.
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PD model estimation:
Implement and estimate various PD models using Python, understanding statistical techniques and model selection.
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PD model validation (test):
Validate your PD models using appropriate statistical tests and metrics to ensure their accuracy and robustness.
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Applying the PD model for decision making:
Learn how to integrate PD models into decision-making processes for credit assessment and risk management.
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PD model monitoring:
Understand strategies and techniques for continuously monitoring PD model performance over time to detect degradation.
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LGD and EAD models:
Explore Loss Given Default (LGD) and Exposure At Default (EAD) models, crucial components in comprehensive credit risk assessment.
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LGD model:
Deep dive into building and validating LGD models to quantify the loss expected if a default occurs.
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EAD model:
Learn to develop EAD models to estimate the outstanding exposure at the time of default.
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Calculating expected loss:
Integrate PD, LGD, and EAD models to calculate the Expected Loss (EL), a key metric in credit risk management.