Daniel Rosch

- 了解流动性，房屋净值和许多其他关键银行业特征变量的作用；- 选择并处理变量；- 预测违约、偿付、损失率和风险敞口；- 利用危机前特征预测经济衰退和危机后果；- 理解COVID-19对信用风险带来的影响；- 将创新的抽样技术应用于模型训练和验证；- 从Logit分类器到随机森林和神经网络的深入学习;- 进行无监督聚类、主成分和贝叶斯技术的应用；- 为CECL、IFRS 9和CCAR建立多周期模型；- 建立用于在险价值和期望损失的信贷组合相关模型；- 使用更多真实的信用风险数据并运行超过1500行的代码...- Understand the role of liquidity, equity and many other key banking features- Engineer and select features- Predict defaults, payoffs, loss rates and exposures- Predict downturn and crisis outcomes using pre-crisis features- Understand the implications of COVID-19- Apply innovative sampling techniques for model training and validation- Deep-learn from Logit Classifiers to Random Forests and Neural Networks- Do unsupervised Clustering, Principal Components and Bayesian Techniques- Build multi-period models for CECL, IFRS 9 and CCAR- Build credit portfolio correlation models for VaR and Expected Shortfal- Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code- Access real credit data and much more ...

Credit risk analytics in R will enable you to build credit risk models from start to finish. Accessing real credit data via the accompanying website www.creditriskanalytics.net, you will master a wide range of applications, including building your own PD, LGD and EAD models as well as mastering industry challenges such as reject inference, low default portfolio risk modeling, model validation and stress testing. This book has been written as a companion to Baesens, B., Roesch, D. and Scheule, H., 2016. Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS. John Wiley & Sons.