Comparison of Central Counterparty Risk Assessment Approaches

Artem Potapov and Marat Kurbangaleev
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Artem Potapov: National Research University Higher School of Economics, Moscow, Russia
Marat Kurbangaleev: National Research University Higher School of Economics, Moscow, Russia

HSE Economic Journal, 2023, vol. 27, issue 2, 196–219

Abstract: The exchange uses statistical risk models to estimate derivatives' margin requirements. These models may use rough simplifications to speed up and simplify the calculation of margin requirements for open positions. Such simplifications include: limitation of the set of risk factors taken into account, use of simple distribution functions and assumption of zero or fixed correlation between risk factors. The paper assesses the impact of these simplifications on the assignned margin level. To achieve this, several models of varying complexity have been built to esti­mate the risk of positions in futures and options. The list of models includes those used in practice (the Moscow Exchange model, the Standard Portfolio Analysis of Risk), as well as stochastic ones. The confidence level of the models’ results measured by the share of realized losses exceeding the level of margin requirements. The burden on the exchange participants estimated by using different models and compared by the distribution and the average value of the margin requirements. The results of the study show that simplifications proposed in practice can lead to an underestimation of the risk of changes in the value of instruments, not allowed by Principle 7 of paragraph 3 of the CPSS – IOSCO 2012. No systematic underestimation occurs when using the stochastic model, consideration of the correlation of risk factors in this case is critical. It is also found that, in average, margin estimates based on the stochastic model lower than those of the Moscow Exchange, which can be interpreted as a lower burden on the exchange's clients.

Keywords: derivatives; margin; Value-at-Risk; backtesting; principal component analysis; GARCH (search for similar items in EconPapers)
JEL-codes: C32 G17 G32 (search for similar items in EconPapers)
Date: 2023
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