The ability to accurately model securitization deals is the cornerstone of effective risk management and investment analysis in modern finance. As financial products become increasingly complex, professionals must move beyond conceptual understanding to develop practical, hands-on skills for cash flow modeling, scenario analysis, and valuation. This specialized expertise, combining the agility of Excel with the power of Python for automation and advanced analytics, is highly sought after. Mastering these tools is crucial for anyone looking to structure, price, and manage securitized products with confidence and precision in a data-driven world.

This comprehensive 10-day training course is meticulously designed to bridge the gap between theoretical knowledge and practical application. Participants will start with the foundational principles of securitization modeling in Excel, progressively building a full-scale cash flow model from the ground up. The program then transitions to Python, demonstrating how to replicate, automate, and scale these models using powerful libraries. By the conclusion, you will be equipped to tackle real-world modeling challenges, perform advanced risk analysis, and generate robust, transparent results, solidifying your expertise in the field of securitized finance.

Duration: 10 days

Target Audience:

• Financial Analysts
• Quantitative Analysts
• Structured Finance Associates
• Portfolio Managers
• Credit Analysts
• Risk Managers
• Financial Engineers
• Treasury and Corporate Finance Professionals

Objectives:

• Build a complete securitization cash flow model from scratch in Excel.
• Utilize advanced Excel functions for efficient and accurate modeling.
• Translate and replicate Excel models into Python scripts.
• Automate key modeling processes using Python libraries like NumPy and Pandas.
• Perform stress testing and sensitivity analysis on securitization structures.
• Understand and model prepayment and default curves.
• Analyze waterfall structures and tranche-level cash flows.
• Generate and interpret key performance metrics for securitized products.
• Develop a practical understanding of how modeling informs deal structure and pricing.

Course Modules:

Module 1: Excel Fundamentals for Financial Modeling

• Mastering key functions: VLOOKUP, INDEX/MATCH, SUMIFS
• Building a dynamic and transparent model layout
• Data validation and conditional formatting
• Naming conventions and cell referencing best practices
• Introduction to macros and VBA for basic automation

Module 2: Securitization Cash Flow Basics in Excel

• Setting up the initial asset pool and assumptions
• Calculating gross and net cash flows from assets
• Creating a dynamic amortization schedule
• Modeling a simple waterfall distribution
• Verifying model integrity and error checking

Module 3: Advanced Excel Modeling Techniques

• Implementing dynamic prepayment and default curves
• Modeling excess spread and credit enhancement mechanisms
• Building a prepayment assumption builder
• Using data tables for sensitivity analysis
• Incorporating loan-level data into the model

Module 4: Introduction to Python for Finance

• Setting up a Python environment (Jupyter, VS Code)
• Introduction to fundamental data structures: lists, dictionaries, tuples
• The basics of functions and control flow
• Installing and using key libraries: NumPy and Pandas
• Reading and writing data from Excel files with Python

Module 5: Data Manipulation with Pandas

• Loading and cleaning loan-level data
• Filtering and aggregating dataframes
• Performing calculations on multiple columns
• Merging and joining dataframes
• Visualizing data with Matplotlib or Seaborn

Module 6: Building a Securitization Model in Python

• Replicating the core cash flow logic from Excel
• Creating functions to calculate monthly payments and defaults
• Building the waterfall distribution logic in Python
• Generating the amortization table for the entire pool
• Comparing Python and Excel outputs for accuracy

Module 7: Advanced Modeling in Python

• Creating a dynamic prepayment model using Python
• Implementing various default and loss curves
• Building a full-deal simulation loop
• Calculating key metrics like WAL, yield, and duration
• Running multiple scenarios and stress tests programmatically

Module 8: Waterfall Simulation and Tranche Analysis

• Coding a flexible waterfall function with multiple tranches
• Simulating cash flow distributions to different tranches
• Calculating tranche-level internal rates of return (IRRs)
• Modeling different credit enhancement levels
• Generating performance reports for each tranche

Module 9: Monte Carlo Simulation

• The theory behind Monte Carlo simulation in finance
• Generating random variables for key model drivers
• Running thousands of simulations to build a distribution of outcomes
• Interpreting the results and confidence intervals
• Using simulation to estimate expected loss and risk

Module 10: Model Validation and Auditing

• Best practices for model validation
• Cross-referencing Excel and Python model results
• Building a reconciliation report
• Identifying and troubleshooting modeling errors
• The importance of model documentation and governance

Module 11: Deal Structure and Optimization

• Using the model to inform deal structure decisions
• Optimizing tranche sizes and credit enhancement levels
• The relationship between model outputs and credit ratings
• Exploring different collateral types in the model
• Analyzing the impact of deal terms on investor returns

Module 12: Visualizing Results and Reporting

• Creating dynamic charts and graphs in Python (Matplotlib, Plotly)
• Building a professional dashboard in Excel
• Generating automated reports from Python scripts
• Key visualizations for prepayment, default, and cash flow
• Presenting complex model results to non-technical stakeholders

Module 13: Introduction to Advanced Securitized Products

• Modeling collateralized loan obligations (CLOs)
• The role of the collateral manager and manager fees
• Synthetic securitization concepts
• Modeling deal triggers and covenants
• Discussion of more complex products like CDOs of ABS

Module 14: Integrating Python with Excel

• Using Python to update and run Excel models
• Automating report generation in Excel with Python
• The benefits of a hybrid Excel-Python workflow
• Using libraries like OpenPyXL for Excel interaction
• Hands-on workshop: building a simple automation tool

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.