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Though risk in derivatives contracts is understood within the setting of derivatives markets itself, little is known about the interaction of derivatives markets with the wider financial system through potential channels of contagion. In existing stress-testing methodologies, derivatives are commonly treated as balance sheet line items that are only liable to changes in valuation. This neglects a key aspect of derivatives markets, liquidity risk: a change in valuation may increase the demand on high-quality liquid assets as collateral, risking illiquidity defaults and exhaustion of the liquidity provided by repo markets, for instance, which threatens to undermine financial stability.
The European Markets Infrastructure Regulation (EMIR) has allowed regulators to collect granular data on the European (EU) derivatives markets to better measure risk.
Our novel software tool, OXCART (Oxford Computational Analysis of Reported Trades), enables us to combine EMIR data with information on legal entities as well as with market data on derivative underliers to construct approximately 90\% of the EU multi-layered derivatives network consisting of layers of contractual edges (i.e. interest rate, credit, foreign exchange, equities and commodities) and entity nodes (including CCPs, banks, various types of non-banks and non-financial entities).
We develop a new risk metric in the context of derivative markets, borrowing an existing network metric called maximum flow with buffers from the network literature, that aims to highlight short-term liquidity risk for nodes in the network. Based on the calibrated multi-layered network, we compute this metric for key types of nodes in the network, such as CCPs, shared clearing members and clients, and non-clearing members in the wider bilateral market.
Our results are first to quantitatively reveal that multiple central clearing parties (CCPs) face a substantial liquidity exposure to a small number of shared clearing members to deliver collateral-grade assets, which in turn rely on non-clearing members for their collateral demands. This finding suggests that risk models of CCPs should consider their mutual interconnectedness through shared members and clients, by increasing the independent collateral amount demanded from shared members that act as intermediaries in the market, since they are likely to face multiple margin calls.