To day I would like to reflect on that complexity and its consequences, drawing extensively on a recent illuminating paper by Andrew Haldane from the Bank of England as well as on previous work published by the Federal Reserve Bank of New York. Complexity comes in two forms. First, it appears in financial instruments themselves, as financial innovation has led, in recent years to a proliferation of so called structured – and, indeed, very complex – products. Second, complexity shows up in the structure of the financial systems, which are based on interdependence between multiple actors and counterparties. Transmissions of shocks occur through networks whose structure and architecture is constantly transformed by financial innovation and regulatory arbitrage. This potentially creates numerous feedback loops and amplification effects. Looking more specifically at the last decade, this increase in complexity had four main features and consequences: •  As techniques for managing and allocating risk became more sophisticated, the network of counterparties expanded in scale and in complexity. This was, truly, a systemic change that was properly understood but not fully captured by regulators at the time. Credit and market risk was supposedly more broadly spread. But counterparty risk increased. Overall, the overall impact on financial stability may well have been negative. •  Complexity led to loss of information, a point underlined by Gorton. Through the chain of tranching and distributing the risk, fundamental values and risk profiles of underlying assets became impossible to reconstruct, even for the most informed investors. There may be a deep paradox here.

Hence the spectacular failure of models during the crisis: most, if not all, were constructed on the assumption that stable and predictable (usually normal) distribution probabilities could be used to describe the different states of the financial system and the economy. They collapsed when extreme events occurred with a frequency that no one ever thought would be possible. -  Uncertainty itself has huge consequences. As shown by Caballero and Krishnamurthy episodes of flight to quality and liquidity hoarding can be best explained by a regime shift, from an environment where risk can be measured and probabilities attached to different states of nature to a world of total uncertainty. Such regime changes are "tipping points" for the system as a whole, whose behavior is suddenly transformed. When they occur, agents respond by making decisions using “worst-case” scenarios and covering themselves against the possibility of their own demise. Doing so, they faced and created a collective action problem since the general rush for protection only aggravates the stress . This is the best way to rationalize and understand the liquidity and market freezes which occurred in August 2007 and, again, in September 2008. Facing such unprecedented reactions, policy makers may have wondered, "does the market know something that we don't". Actually, markets participants knew that they could not know. Because they were directly involved, they were aware it was impossible for them to fully master the complexity they had themselves created. And they acted accordingly.