The application of mathematical models to measure collateral concentration risk

Abstract

The financial system is shifting towards greater use of collateral to mitigate counterparty credit risk. On a systemic basis, this is reducing credit risk; however, it is creating new market and liquidity risk on the collateral held, potentially resulting in weak points in the resilience of the financial system. Use of ‘cheapest to deliver’ collateral optimisation algorithms also may lead to firms allocating larger quantities of lower quality collateral to counterparties. Managing the concentration risk of the collateral portfolio is therefore becoming more important. This is particularly the case as high-quality liquid assets become scarcer due to rising demand, leading to a move to non-cash collateral or acceptance of lower-rated assets. In addition, many firms are using basic methods to monitor concentration at the trade or counterparty level; they have no clear view of collateral concentration at the firm level. This paper looks at some of the trends in monitoring collateral portfolio concentration. It also reviews the mathematical models available to measure build ups of concentration risk. This includes measures such as diversity scores, the Herfindahl-Hirschman index (HHI) and Gini coefficients. The paper concludes that market participants and particularly central clearing counterparties (CCPs) have incentives to move down the liquidity spectrum in terms of the collateral they will accept. More advanced models and technology solutions to manage concentration limits are therefore important tools in preventing future defaults and crises.