Margin in Derivatives Trading
First published:
ISBN: 978-1-78272-390-5
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Robust Computation of XVA Metrics for Central Counterparty Clearing Houses
Introduction
Variation and Initial Margin in the ISDA Credit Support Annex
Variation and Initial Margin Required by Central Counterparty Clearing Houses
Margin Requirements for Over-the-Counter Derivatives: A Supervisory Perspective
The Emergence and Concepts of the SIMM Methodology
The ISDA Standard Initial Margin Model Backtesting Framework
The Impact of Margin on Regulatory Capital
XVA for Margined Trading Positions
Modelling Forward Initial Margin Requirements for Bilateral Trading
Forward Valuation of Initial Margin in Exposure and Funding Calculations
Margin Value Adjustment for CCPs with Q-Simulated Initial Margin
Bilateral Exposure in the Presence of Margin
Central Counterparty Risk
Robust Computation of XVA Metrics for Central Counterparty Clearing Houses
Efficient Initial Margin Optimisation
Procyclicality in Sensitivity-Based Margin Requirements
Systemic Risks in Central Counterparty Clearing House Networks
13.1 INTRODUCTION
This chapter continues the discussion of central counterparty clearing houses (CCPs) that we began in Chapter 12. Our focus here will be on practical techniques for calculation of XVA metrics (credit valuation adjustment (CVA) and margin valuation adjustment (MVA), specifically), for both house and client positions of a clearing member. As we saw in Chapter 12, detailed information about the inner structure of a CCP is rarely available, so we emphasise methods with a high degree of robustness and simplicity. Given that default exposures to CCPs are triggered by rare events, the chapter also pays special attention to the modelling of distribution tails.
Most of the key ideas we shall invoke in this chapter originate in Andersen and Dickinson (2018) and can be characterised as a creative use of scaling relationships to condense a large number of unknown micro-structure variables into a few intuitive macroscopic “levers” that can be estimated conservatively from available data and then applied to exposure computations that are easy to execute on a standard exposure calculation engine. A key building block for this approach is the analytical proportionality
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