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A blog on financial markets and their regulation
I have blogged about the sociology of finance several times (for example in 2010, and in 2011). Two pieces that I read (or in one case re-read) recently have reinforced my view that this literature is important for understanding modern finance.
When the penalties imposed on Barclays by the UK FSA and the US CFTC brought Libor back into the limelight, I found myself re-reading MacKenzie’s fascinating description of the Libor fixing (Donald MacKenzie, “What’s in a Number?”, London Review of Books, 30(18), 25 September 2008, pages 11-12) based on his ethnographic study carried out prior to the financial crisis.
None of the finance textbooks describe the actual mechanics of the Libor fixing as well as this piece. Every source on Libor recites the standard definition that Libor is “The rate at which an individual contributor panel bank could borrow funds, were it to do so by asking for and then accepting interbank offers in reasonable market size, just prior to 11.00am London time.” But one has to read MacKenzie to understand how this hypothetical condition (“were it to do so”) is actually operationalized. Similarly, MacKenzie tells us very casually that a mere $50 million or so may fall short of reasonable market size which for the major currencies would be of the order of several hundred millions.
The second paper that I have been reading also co-authored by MacKenzie is weightier and more recent (Donald MacKenzie and Taylor Spears, “‘The Formula That Killed Wall Street’? The Gaussian Copula and the Material Cultures of Modelling”, June 2012). This paper discusses the well known (and by now notorious) Gaussian copula model for pricing CDOs.
The crucial claim in this paper is that Gaussian copula models were and are crucial to intra- and inter-organizational co-ordination, while simultaneously being ‘othered’ by the modellers themselves. The word ‘other’ might be a simple word, but it has a complex meaning. What is being argued is that the modellers steeped in the culture of no-arbitrage modelling never ‘naturalized’ the Gaussian copula and did not even regard it as a proper model. The dissonance between actuarial models and no-arbitrage models is also brought out very well. I found myself thinking that the battle between CreditMetrics and CreditRisk+ more than a decade ago was also one between actuarial models and no-arbitrage models.
As an aside, the authors also bring up the issue of counterperformativity (models being invalidated by their widespread adoption): “models used for governance are undermined by being gamed; models used to hedge derivatives are undermined by the effects of that hedging on the market for the underlying asset” They also speculate on the possibility of ‘deliberate counterperformativity’: “the employment of a model that one knows overestimates the probability of ‘bad’ events, with a view to reducing the likelihood of those events.”