# An Enhanced Initial Margin Methodology to Manage Warehoused Credit Risk

## TL;DR

This paper introduces a novel initial margin methodology that adjusts for counterparty credit risk, enabling better management of warehoused credit risk and improving access to hedging for high-risk entities.

## Contribution

It extends the CVA risk-neutral framework to compute a counterparty-specific initial margin based on credit quality, transforming effective credit ratings to AAA for risk management.

## Key findings

- Proposes a methodology for counterparty-specific initial margin calculation.
- Transforms CVA requirements into initial margin to improve risk management.
- Ensures operational viability of warehoused credit risk management.

## Abstract

The use of CVA to cover credit risk is widely spread, but has its limitations. Namely, dealers face the problem of the illiquidity of instruments used for hedging it, hence forced to warehouse credit risk. As a result, dealers tend to offer a limited OTC derivatives market to highly risky counterparties. Consequently, those highly risky entities rarely have access to hedging services precisely when they need them most. In this paper we propose a method to overcome this limitation. We propose to extend the CVA risk-neutral framework to compute an initial margin (IM) specific to each counterparty, which depends on the credit quality of the entity at stake, transforming the effective credit rating of a given netting set to AAA, regardless of the credit rating of the counterparty. By transforming CVA requirement into IM ones, as proposed in this paper, an institution could rely on the existing mechanisms for posting and calling of IM, hence ensuring the operational viability of this new form of managing warehoused risk. The main difference with the currently standard framework is the creation of a Specific Initial Margin, that depends in the credit rating of the counterparty and the characteristics of the netting set in question. In this paper we propose a methodology for such transformation in a sound manner, and hence this method overcomes some of the limitations of the CVA framework.