‘A device for being able to book P&L’: The organizational embedding of the Gaussian copula

Abstract

This article, the second of two articles on the Gaussian copula family of models, discusses the attitude of 'quants' (modellers) to these models, showing that contrary to some accounts, those quants were not 'model dopes' who uncritically accepted the outputs of the models. Although sometimes highly critical of Gaussian copulas - even 'othering' them as not really being models --they nevertheless nearly all kept using them, an outcome we explain with reference to the embedding of these models in inter- and intra-organizational processes: communication, risk control and especially the setting of bonuses. The article also examines the role of Gaussian copula models in the 2007-2008 global crisis and in a 2005 episode known as 'the correlation crisis'. We end with the speculation that all widely used derivatives models (and indeed the evaluation culture in which they are embedded) help generate inter-organizational co-ordination, and all that is special in this respect about the Gaussian copula is that its status as 'other' makes this role evident.

Toward a political economy of complex interdependence

“…CDOs and the underlying models used to construct them developed in the context of and became an important element of this risk revolution. JP Morgan developed an early version of the GCM in the mid-1990s (MacKenzie and Spears, 2014a: 404). In 1999 and 2000, David X. Li drew on actuarial research, where copula functions are used to model the broken-heart syndrome, to model default dependence in corporate bond-based CDOs.…”

Taming Uncertainty: The Limits to Quantification

“…Such disregard of uncertainty was not because of naivety on behalf of modellers, who were perfectly aware of the limitations of the Gaussian copula model. Operational factors, such as the need for a clear way to book P&L and thus set bonuses, provided the organisational context in which the Gaussian copula model became indispensable (MacKenzie and Spears, 2014). Furthermore, the sensitivity of model outputs to assumptions that could in practice never be validated using historical data, given the context of a young market with complex products and transaction patterns, was not fully considered (ontological uncertainty).…”

Tail dependence of the Gaussian copula revisited

“…The bivariate Gaussian copula arises from the bivariate normal distribution. As such, it is arguably the most popular and well-studied copula, extensively used in financial and insurance mathematics (MacKenzie and Spears, 2014). We recall that the Gaussian…”