benoit cœure : policy analysis with big data speech by mr benoit cœure, member of the executive board of the european central bank, at the conference on " economic and financial regulation in the era of big data ", organised by the bank of france, paris, 24 november 2017. * * * the recent financial crisis, and the euro area sovereign debt crisis that followed, were characterised by periods of increased heterogeneity, market fragmentation and sudden turns in economic activity. this often made it difficult for economic policymakers to understand and assess in real time the underlying forces driving economic behaviour. both traditional statistical datasets and our models proved at times inadequate to support the decision - making process, reflecting long time lags, linear assumptions and the absence of more granular information. these events increasingly boosted the efforts in policy circles to obtain timelier and richer data for policy analysis, in short big data. 1 this push towards more granular information was not a revolution, however. it can be argued that big data, under different guises, have been used as an input into policymaking since adolphe quetelet ’ s memoire in 1848. since then, big data has been central to business cycle analysis, from the early work of clement juglar to the contributions of both wesley mitchell and the cowles commission, right up until today. according to central bank mythology, former federal reserve chair alan greenspan would sit in his bathtub perusing sheets of statistics. and indeed, economic historians have analysed how the power of governments has been shaped by statistics – and vice versa. 2 the most recent push towards more granular data was thus an evolution rather than a revolution, triggered in part by the emergence of new opportunities – themselves a reflection of rapid technological progress – and the experience gained over several years of crisis management. this evolution is already bearing fruit. policymakers today have access to a large number of micro datasets, often very different in nature and scope. some are the result of new financial regulations. others are by - products of increased use of technology. what they have in common, however, is that, if used appropriately and responsibly, they can help policymakers to extract more timely and diverse economic signals, and thus are a meaningful complement to existing official data. in my remarks this morning, i will take stock of the progress made at the ecb, and in the central banking community more generally, on the use of more granular

. carrying out checks by algorithm, using machine learning techniques and artificial intelligence is one way of ensuring that data remain of high quality. the eurosystem has a steep learning curve in front of itself. these efforts will no doubt pay off. indeed, mmsr data have proven highly useful to policymakers in their short period of existence. let me give you an example. the data help us assess the impact of the ecb ’ s asset purchase programme ( app ) on market functioning, as i explained in more detail in a speech i gave last week in brussels. 6 mmsr data show that since the ecb launched the app there has been a marked shift away from trades backed by general collateral, which are traditionally used to manage cash. instead, there has been a growing share of repos off the general collateral curve, termed specials. take for example the german bund market. traditionally, only around 5 % of bonds in the german repo market traded as special, but in the second half of last year, that share rose to 50 %. being aware of the distribution of trades and the premium placed on special bonds enables us to assess the impact of the app on market functioning. to ease potential frictions, we decided last december to allow cash to be used as collateral in our securities lending programme, and to permit app purchases below the deposit facility rate. as a result of these decisions, the “ specialness ” premium on long - term german bonds has declined, and the share of bonds that trade special is now around 30 %. a further notable area of use for mmsr data has emerged from growing concerns about the reliability and robustness of current risk - free benchmark rates for the euro area. 7 banks have become increasingly reluctant to participate in benchmark panels, owing to concerns about potential litigation, compliance risks and costs. the resulting uncertainty in the integrity of reference rates represents a potentially serious source of vulnerability and systemic risk. against this backdrop, the ecb recently announced that it intends to provide a new overnight unsecured interest rate, using mmsr transaction data. the new rate is intended to complement existing benchmark rates produced by the private sector and serve as a backstop reference rate, with the aim to start publishing by 2020. 8 this move is motivated by our desire to mitigate the potential adverse impact on the monetary transmission mechanism and on financial stability from the lack of reliable benchmarks. the market facilitating role played by the ecb in this field is consistent with the tasks