t what you don ’ t know that gets you into trouble. it ’ s what you know for sure that just ain ’ t so. ” ) but unfortunately even that is not good enough. from a risk point of view, ultimately you need to be able to measure what you don ’ t know. that, i believe, is inherently impossible. because it is impossible, we should make society as robust as possible to uncertainty, while realising that it is not possible to insulate it completely from uncertainty. i do not believe that “ the truth is out there ” waiting to be discovered. the quant faction in financial markets, to some extent, is on a quest to find “ the truth ”, the data - generating process that is at the root of financial markets. 4 while a valiant quest, with much knowledge to be gained in its pursuit, like lancelot ’ s quest for the holy grail, it is likely to be in vain. ( this is of course, itself, a qualitatively probabilistic statement about an uncertain event! ) a critical issue is : how important is all of this? the answer is that it depends on the circumstances. in the good times, not being able to quantify the risk of the unknowable downside, or take into account the uncertainties, is not that important while ever the good times last. indeed, there is little to be gained from doing so. every once in a while, however, it becomes the main game. the “ great moderation ” of the 1990s and 2000s lulled investors and risk managers into a false sense of security. the decline in volatility led many to conclude that a new, more stable, regime had been established. let me make it clear that i am not saying that the stable macroeconomic environment of this period was a bad thing. far from it. the improvement in welfare generated by the low unemployment rates, stable growth and low inflation was large and welcome. i do not subscribe to the somewhat austrian view that the stability and decline in volatility sowed the seeds of its own destruction. models could do a good job of measuring what was taking place during the great moderation. the models were also doing a good job of out - of - sample prediction because the future was unfolding broadly in line with the samples used to estimate the models. when shocks occurred, they were still consistent with the error distribution that underpinned the models and hence provided further

guy debelle : on risk and uncertainty address by mr guy debelle, assistant governor of the reserve bank of australia, at the risk australia conference, sydney, 31 august 2010. * * * “ the future ’ s uncertain and the end is always near ” – jim morrison today i want to talk about the role of risk and uncertainty in the financial crisis. the primary reason why i want to do that is that i believe risk assessment, or more precisely, misassessment has been one of the key elements of the crisis. while that is undoubtedly true in any crisis, i think it has played a more central role in the current episode than in the past. risk was mis - assessed by financial institutions, risk managers, investors and regulators. there was a false comfort taken from a misplaced belief that risk was being accurately and appropriately measured. to some extent, the technology provided risk managers with a false sense of security. risk may well have been accurately measured for the particular regime that the economy and financial markets were operating in. but the risk assessment was not robust to a regime change that took the models out of their historical comfort zone. not enough account was taken of uncertainty. one of the messages i want to leave you with is that risk measurement based on historical models can only take you so far. judgement must play an important role. ultimately, the future is uncertain, in the sense that it cannot be quantified. the goal should be to design systems that are as robust as possible to this uncertainty. a system with less leverage is one obvious means of enhancing robustness. risk versus uncertainty in discussing risk, i would like to highlight the key distinction between risk and uncertainty : risk is quantifiable, uncertainty is not. this is a distinction with a long tradition. keynes made much of it, 1 as did frank knight who lends his name to “ knightian ” uncertainty, on which he elaborated in risk, uncertainty and profit in 1921. 2 knightian uncertainty arises when you don ’ t know the underlying probability distribution, which makes quantifying the risks impossible. more recently, this distinction has been given prominence by donald rumsfeld with his knowns and unknowns, as well as by satyajit das in traders, guns and money and nassim taleb in the black swan. in addition to the distinction between risk and uncertainty in terms of measurability, keynes made the similar distinction between cardinal and ordinal probability. indeed his treatise on