Author: Cruz Lopez, Jorge Abraham
The collateral systems commonly employed by many derivatives central counterparties (CCPs), such as the Standard Portfolio Analysis of Risk (SPAN) or the Value-at-Risk (VaR) approach, fail to consider the loss dependence of their clearing members. As a consequence, CCPs are often left exposed to simultaneous extreme losses that could undermine their stability and that of the entire financial system. In this context, this thesis proposes two new collateral methodologies that address this problem. Chapter 2 uses copulas to develop a methodology that accounts for the tail dependence of market participants. This method allows individual margins to increase when clearing firms are more likely to suffer simultaneous extreme losses; thus, reducing the probability and shortfall associated with joint margin exceedances. Chapter 3 proposes a collateral methodology, called CoMargin, which generalizes the VaR approach to a multivariate setting. This method targets and stabilizes the conditional probability of financial distress across clearing members, can be generalized to any number of market participants and can be backtested using formal statistical tests. The empirical sections of Chapter 3 use proprietary data from the Canadian Derivatives Clearing Corporation (CDCC), which include daily observations of the actual trading positions of all of its members from 2003 to 2011. This dataset is the first one of its kind in the economics and finance literature and opens the door to the development of new models that do not have to rely on the strong assumptions made in the past about the trading behaviour of market participants.
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Thesis advisor: Smith, Daniel
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