this by installing additional constraints and backstops to close gaps that internal models cannot close – most prominently the leverage ratio and the output floor. further safeguards are implemented by more rigorous methods, data rules and input floors. this means that regulation has become multi - polar – supervisors rely on various, complementary requirements. 3 / 5 bis central bankers'speeches but at the same time there is also a need to support the benefits of internal models. on the basel committee, the german representatives resolutely argued in favour of maintaining risk sensitivity in regulation, because this is the best way to capture the actual risks of a financial institution and to set the right incentives, thereby discouraging excessive risk - taking. this especially concerns the subject of calibrating the output floor, which is – as most of you know – a limit to internal model calculations based on the standardised approaches. with the advantages of internal modelling in mind, this topic is understandable. and for me, the current state of negotiations – an output floor of 72. 5 per cent – is too high ; but it is still enough for models to remain an attractive tool. while risk sensitivity will be diminished by setting the output floor at this level, it still represents a far better outcome than the originally envisioned output floor of 80 per cent. basel iii is better than its critics claim : while some countries may gold - plate their national regulations through a ban of internal models – the new standard also enables the basel countries to continue the use of internal models. and this is an important outcome. 5. you can count on that : better models for the future now we have to look ahead. we should take the basel iii reforms and implement them in a manner that improves risk models further. banks have to build better models, models that not only focus on the efficient use of capital but also ensure that a bank can weather future storms. both goals must weigh equally, meaning that the storm - forecasting part has to be given much more attention. authorities like the ssm and eba on the other hand will have to roll up their sleeves and build a regulatory and supervisory framework for the future of risk measurement. this will be challenging not only for the sheer technical complexity, but also because we have to strike two balances at once : the first balance is to maintain the incentives for fine - tuned risk measurement and management on the one hand, while improving the checks and balances on risk models on the other. when pursuing this balance, we obviously have

29th ( ec ) 2 conference on “ big data econometrics with applications ” opening remarks by salvatore rossi senior deputy governor of the bank of italy and president of the institute for the supervision of insurance ( ivass ) rome, 13 december 2018 it is my great pleasure to open this conference and to welcome all the speakers and participants. i thank the organizers for designing such a broad and topical programme. with the increasing digitalization of the economy, data has become central in all the social sciences, and economics is no exception. in fact, some have talked about data being the new oil of today ’ s world. econometrics and statistics are therefore becoming increasingly dependent on the availability of huge and heterogeneous datasets. only few years ago, the econometric profession was rather cautious about big data. classical econometrics is based on theory and so relies on approaches that differ significantly from those of big data analytics. econometricians have been taught for decades to start with a theory and then use data to prove or disprove it. big data and machine learning work in the opposite way : they allow one to look for patterns simply by processing huge amounts of data, regardless of possible underlying models. it ’ s important to note that most econometricians have expressed concern about the reliability and representativeness of such vast datasets. however, it ’ s becoming evident that big data could disrupt traditional econometrics. data collection from social media has produced unprecedentedly large and complex, though unstructured, datasets on economic agents ’ behaviour and interactions, and these are proving to be a goldmine of economic information. we have been working with data analysis for many years, but it is now time to adapt our computational approaches to the new context. the bank of italy, because of its multiple functions, follows the evolution of big data very attentively. we will discuss this topic in a workshop organized with the bank of international settlements next 15 january here in rome. in the last few years we have progressed along our learning curve. we have combined skills in economics, econometrics, statistics and computer science to deal with the increasing volume, variety and velocity of data, which we have then used to estimate unemployment and inflation, to improve our economic forecasts, and to measure consumer and business sentiment. we, like all central banks, must also be ready to cope with the increasing public demand for access to granular data. as a