Analysis and Management of Risks in Securitization

This study employs a comprehensive set of empirical techniques and statistical methods to analyze credit risk within securitization. Detailed information on the dataset components provides insights into loans granted over a specified period and region. Key methods used include the Chi-Squared Test for Independence to determine associations between categorical variables and credit risk, the Variance Inflation Factor (VIF) to detect multicollinearity in regression models, and the Gini Coefficient to measure the discriminatory power of credit scoring models. Additionally, Support Vector Machines (SVM) and Logistic Regression are used for classification and probability modeling, respectively, while a Cross-Validation Approach ensures the robustness and accuracy of predictive models. These methods are detailed in sections discussing binary classifiers and the Population Stability Index (PSI), underscoring the empirical significance of credit risk and its impact on securitization. The primary objective of this research is to understand and quantify the impact of credit risk on securitized assets. By utilizing empirical data and robust statistical techniques, this study aims to identify key determinants of credit risk in securitization and evaluate effective risk management practices. The study's relevance is heightened by the evolving financial landscape and the complexity of securitization products, coupled with advancements in AI and machine learning. The expected results will highlight significant determinants affecting the credit risk of securitized loans, emphasizing the importance of robust risk assessment methodologies and regulatory frameworks. Ultimately, this research aims to contribute valuable insights into the management of credit risks, supporting the stability and efficiency of the securitization market.