• ABS background and purpose
• ABS model implementation

Every structured deal consists of a pool of assets (the Loans) and a group of liabilities (the asset-backed securities). The objective of structuring is to create and sell customized securities to investors, which are backed by the pool of loans.

Implement the actual asset-backed securities (the liabilities) in addition to the Waterfall mechanism that calculates the cashflows at each time period. The objective is to create well-designed tranche classes which will seamlessly work with your existing Loan classes. The outcome is to be able to take an input CSV of loan data and output a CSV with the all the cashflows at each time period (the Waterfall)

Implement metrics on the Waterfall. This includes Internal Rate of Return (IRR), Reduction in Yield (DIRR), and Average Life (AL). The objective and outcome is to be able to calculate and provide useful metrics on the structure.

The last part is to value and rate the ABS. This entails creating a Monte Carlo simulation to simulate thousands of different credit default scenarios, all of which help determine the rating of the structure. The objective here is to get a taste of implementing an actual Monte Carlo simulation for finance in Python, utilizing the existing classes, random number generation and multiprocessing. The outcome will be a rate, rating, and Weighted Average Life (WAL) for each tranche of our very simple structure.

    since my MC is relatively slow, I only test small NSIM 
    when NSIM = 60:
    num_processes = 10   MC time cost: 131.306999922 s
    num_processes = 20   MC time cost: 118.375 s  
    num_processes = 30   MC time cost: 114.0849998 s
    In this case, num_processes = 30  is the best choice.
    when NSIM = 80:
    num_processes = 20   MC time cost: 135.541999817 s
    The optimal process number is also dependent on NSIM.