Dynamic sensitivities and Initial Margin via Chebyshev Tensors

TL;DR

This paper introduces a method using Chebyshev Tensors to efficiently compute dynamic sensitivities and initial margin in financial risk management, demonstrating high accuracy and computational efficiency for FX swaps and spread options.

Contribution

It presents a novel application of Chebyshev Tensors for dynamic sensitivities and initial margin calculation, improving speed and accuracy over traditional risk engine methods.

Findings

High accuracy in sensitivity computation

Significant computational gains

Effective for FX swaps and spread options

Abstract

This paper presents how to use Chebyshev Tensors to compute dynamic sensitivities of financial instruments within a Monte Carlo simulation. Dynamic sensitivities are then used to compute Dynamic Initial Margin as defined by ISDA (SIMM). The technique is benchmarked against the computation of dynamic sensitivities obtained by using pricing functions like the ones found in risk engines. We obtain high accuracy and computational gains for FX swaps and Spread Options.