A Framework for Waterfall Pricing Using Simulation-Based Uncertainty Modeling

TL;DR

This paper introduces a simulation-based framework for pricing waterfall structures in structured finance, modeling uncertainty in cashflows with calibrated probability distributions, implemented efficiently in PyTorch for risk analysis and optimization.

Contribution

It presents a novel, flexible framework that combines uncertainty modeling with efficient computation for pricing complex structured finance instruments.

Findings

Framework effectively models cashflow uncertainty.

Implementation in PyTorch enables scalable computation.

Facilitates risk sensitivity analysis and optimization.

Abstract

We present a novel framework for pricing waterfall structures by simulating the uncertainty of the cashflow generated by the underlying assets in terms of value, time, and confidence levels. Our approach incorporates various probability distributions calibrated on the market price of the tranches at inception. The framework is fully implemented in PyTorch, leveraging its computational efficiency and automatic differentiation capabilities through Adjoint Algorithmic Differentiation (AAD). This enables efficient gradient computation for risk sensitivity analysis and optimization. The proposed methodology provides a flexible and scalable solution for pricing complex structured finance instruments under uncertainty