XVA Metrics for CCP Optimisation - Archive ouverte HAL
Article Dans Une Revue Statistics & Risk Modeling with Applications in Finance and Insurance Année : 2020

XVA Metrics for CCP Optimisation

Résumé

Based on an XVA analysis of centrally cleared derivative portfolios, we consider two capital and funding issues pertaining to the efficiency of the design of central counterparties (CCPs). First, we consider an organization of a clearing framework, whereby a CCP would also play the role of a centralized XVA calculator and management center. The default fund contributions would become pure capital at risk of the clearing members, remunerated as such at some hurdle rate, i.e. return-on-equity. Moreover, we challenge the current default fund Cover 2 EMIR sizing rule with a broader risk based approach, relying on a suitable notion of economic capital of a CCP. Second, we compare the margin valuation adjustments (MVAs) resulting from two different initial margin raising strategies. The first one is unsecured borrowing by the clearing member. As an alternative, the clearing member delegates the posting of its initial margin to a so called specialist lender, which, in case of default of the clearing member, receives back from the CCP the portion of IM unused to cover losses. The alternative strategy results in a significant MVA compression. A numerical case study shows that the volatility swings of the IM funding expenses can even be the main contributor to an economic capital based default fund of a CCP. This is an illustration of the transfer of counterparty risk into liquidity risk triggered by extensive collateralization.
Fichier principal
Vignette du fichier
CCP-NEW.pdf (934.47 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03910114 , version 1 (21-12-2022)

Identifiants

  • HAL Id : hal-03910114 , version 1

Citer

Claudio Albanese, Yannick Armenti, Stéphane Crépey. XVA Metrics for CCP Optimisation. Statistics & Risk Modeling with Applications in Finance and Insurance, 2020. ⟨hal-03910114⟩
18 Consultations
115 Téléchargements

Partager

More