On Deep Learning for computing the Dynamic Initial Margin and Margin Value Adjustment

TL;DR

This paper introduces a neural network approach to efficiently compute Dynamic Initial Margin in credit risk, reducing computational costs by using a single Monte Carlo simulation and a parameterized input structure.

Contribution

It presents a novel neural network methodology that significantly lowers data generation costs and enhances robustness for DIM computation across various interest rate models.

Findings

Reduces dataset generation to a single Monte Carlo simulation

Demonstrates convergence and robustness across models

Validates efficiency in realistic portfolio scenarios

Abstract

The present work addresses the challenge of training neural networks for Dynamic Initial Margin (DIM) computation in counterparty credit risk, a task traditionally burdened by the high costs associated with generating training datasets through nested Monte Carlo (MC) simulations. By condensing the initial market state variables into an input vector, determined through an interest rate model and a parsimonious parameterization of the current interest rate term structure, we construct a training dataset where labels are noisy but unbiased DIM samples derived from single MC paths. A multi-output neural network structure is employed to handle DIM as a time-dependent function, facilitating training across a mesh of monitoring times. The methodology offers significant advantages: it reduces the dataset generation cost to a single MC execution and parameterizes the neural network by initial market state variables, obviating the need for repeated training. Experimental results demonstrate the approach's convergence properties and robustness across different interest rate models (Vasicek and Hull-White) and portfolio complexities, validating its general applicability and efficiency in more realistic scenarios.