Credit Risk Modeling: Using Excel and VBA
Risk modeling is one of the most important tasks for financial institutions. Credit risk modeling is the process of assessing how much potential losses a borrower might incur if the borrower defaults on a loan. It is an essential part of any credit decision, and it is increasingly important as the world moves away from centralized banking systems toward decentralized systems, such as cryptocurrency and blockchain-based transactions. There are many different ways to model credit risk.
Credit Risk Modeling in Excel ́& VBA is a complete and detailed out-of-the-box solution for professional financial risk managers. The current recession we are living through has shown that financial risk is a matter for many differently sized
One common approach is to use a credit scoring model, which assigns a rating to each individual based on their overall creditworthiness and looks at their history of debt repayments and other factors. Another method is to use a statistical model that uses historical data to predict the future performance of an individual or set of individuals based on factors such as age, income, job tenure and loan type.
A statistical model can take into account factors such as age distribution, income distribution and loan type distribution to assess the likelihood of defaulting among borrowers with different characteristics.
The book credit risk modeling using Excel and VBA is written by Gunter Loffler and Peter N. Posch.
This book discussed about estimating credit Scores with Logit, Linking scores , Linking scores, default probabilities and observed default behavior, Estimating logit coefficients in Excel, Computing statistics after model estimation, Interpreting regression statistics, Prediction and scenario analysis, Treating outliers in input variables, Choosing the functional relationship between the score and explanatory variables.
The Structural Approach to Default Prediction and Valuation,
- Default and valuation in a structural model
- The iterative approach
- A solution using equity values and equity volatilities
- Cohort approach
- Multi-period transitions
- Hazard rate approach
- Obtaining a generator matrix from a given transition matrix
- Confidence intervals with the Binomial distribution
- Bootstrapped confidence intervals for the hazard approach
Prediction of Default and Transition Rates
- Candidate variables for prediction
- Predicting investment-grade default rates with linear regression
- Predicting investment-grade default rates with Poisson regression
- Backtesting the prediction models
- Predicting transition matrices
- Adjusting transition matrices
- Representing transition matrices with a single parameter
- Shifting the transition matrix
- Backtesting the transit
- Default correlation,
- joint default probabilities
- and the asset value approach
- the method of moments
- Estimating asset correlation with maximum likelihood
- Exploring the reliability of estimators with a Monte Carlo study
- Concluding remarks
- Cumulative accuracy profile and accuracy ratios
- Receiver operating characteristic (ROC)
- Bootstrapping confidence intervals for the accuracy ratio
- Interpreting CAPs and ROCs
Validation of Credit Portfolio Models
- Testing distributions with the Berkowitz test
- Example implementation of the Berkowitz test
- Representing the loss distribution
- Simulating the critical chi-squared value
- Testing modeling details: Berkowitz on subport
After reading this digital book, readers will be fully proficient in designing and building a credit model. Topical areas covered include frequency independence, working with trend data, conditionality of losses, calibration, sensitivity on balance transfers and debt ratio.
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