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Welcome remarks at the third annual conference of the ESRB

Mario Draghi, President of the ECB and Chair of the European Systemic Risk Board, third annual conference of the ESRB, Frankfurt am Main, 27 September 2018.

It is my pleasure to welcome you to the third annual conference of the European Systemic Risk Board (ESRB).

The EU economy has been growing now for more than five years. By ensuring price stability, monetary policy contributes to this growth being sustainable. But there is also an important contribution to be made by macroprudential policy. By applying appropriate tools in a timely fashion, policymakers can help prevent the incipient build-up of financial imbalances.

The ESRB plays an important role in supporting successful macroprudential policy in Europe. It provides a forum to collectively discuss emerging risks and vulnerabilities. Moreover, authorities can learn from each other by sharing experiences of using macroprudential instruments and so enable more effective implementation.

Authorities are willing to use the tools at their disposal. Yet progress is still needed along two dimensions.

The growth in importance of the non-bank financial sector requires commensurate additions to the policy toolkit. Policymakers also need access to – and the ability to process and understand – high-quality data to underpin their decisions.

The use of macroprudential tools over recent years

Since the new EU prudential rules for banks entered into force at the start of 2014, policymakers have been proactive in using macroprudential tools to address emerging areas of risk.

In particular, Member States have taken steps to address developments in the real estate sector. By now, 21 Member States have at least one macroprudential measure in place that is targeted at the residential real estate sector. 11 have activated policies for the commercial real estate sector.[1]

Reflecting the targeted nature with which macroprudential policy can be applied, some countries have considered varying implementation by geographical area, to strengthen the impact on local hotspots. These policy actions have helped mitigate movements in real estate prices.

Authorities have activated their tools to require banks to increase capital. Seven Member States have announced an increase in the countercyclical buffer rate since the start of this year. Once these announcements all enter into force, a positive buffer rate will apply in eight Member States.

Macroprudential tools have also been used to address structural risks, with 12 Member States now having a systemic risk buffer in place. Structural risks can vary greatly in nature, and this buffer provides authorities with a flexible tool that can address a wide variety of long-term non-cyclical risks. Its use accordingly differs across Member States, applying in some to all banks and all exposures, and in others to only a subset of banks or domestic exposures.[2]

The toolkit needs to keep pace with new developments

Policymakers’ ability to act, however, is hampered by an incomplete toolkit.

Macroprudential instruments in the EU are for the most part targeted at the banking sector, given the predominance of bank-based finance at the time that the initial response to the global financial crisis was designed.[3]

Yet non-bank finance is playing an increasingly important role in financing the economy. The shadow banking sector[4] accounts for around 40 per cent of the EU financial system, with total assets of just over €42 trillion.[5] As the Capital Markets Union (CMU) progresses, the role of non-bank finance is expected to increase further.[6]

Policymakers need a comprehensive macroprudential toolkit to act in case existing risks migrate outside the banking sector or new risks emerge.[7] And that means widening the toolkit so that policymakers are able to effectively confront risks emerging beyond the banking sector. Additional tools should deal with liquidity risk and those risks associated with leverage among some types of investment funds. Fund managers themselves also need to be given a broader range of tools to better manage such risks.[8]

The wider toolkit includes macroprudential tools for insurance. Indeed, at the ESRB General Board this morning we discussed what type of tools might usefully complement existing insurance regulation.

The need for high-quality data

Policymakers’ ability to act hinges crucially on the availability of high-quality data. Data allow policymakers to identify, analyse and quantify emerging risks. Data also provide policymakers with the necessary knowledge to be able to target and calibrate their tools and to be aware of possible spillovers, or attempts to circumvent regulations.

Yet gaps persist.

Scarcity of accurate and timely data that are comparable across the EU impedes analysis and monitoring of real estate markets. Several initiatives are under way to harmonise definitions and improve data availability, including some by the ESRB.[9] But progress is slower than desired.

We require a more detailed understanding of the inner workings of significant parts of the EU shadow banking system to assess whether or not transactions are associated with an increase in risk or vulnerabilities. For example, real estate funds can use derivatives not only to hedge exchange rate risks but also to gain market exposure.[10] Some countries, notably Ireland, have initiated detailed national data collection to further their understanding.[11]

Monitoring an interconnected financial system involves the availability of detailed and granular transactions data. But in order to have the full picture, it is vitally important to be able to link data across markets, instruments and counterparties. This requires accelerating efforts towards greater data standardisation at the global level, building on the successful use of the Legal Entity Identifier. Policymakers need to invest in adequate infrastructure and analytical tools.

The derivatives data that have become available through the European Market Infrastructure Regulation (EMIR) show the importance of such an investment. This is “big data”; it consists of approximately one hundred million observations per day, each containing nearly two hundred and fifty attributes, amounting to about one terabyte of daily information.[12]

The ESRB’s first foray into this wealth of information focused on analysing data provided by a single trade repository for a single day for the three largest derivatives markets (interest rates, foreign exchange and credit).[13] At that time, the preparation of the data alone took the researchers involved in this work several months. Investment in technology, collaboration between national authorities, ESMA, the ECB and the ESRB, as well as standardisation, now permit the data to be prepared in seconds.

That investment is paying off. The granularity of EMIR data help us understand, for example, the remarkable reduction in the outstanding notional amount of credit default swap contracts (CDS) – from USD 61.2 trillion at end-2007 to USD 9.4 trillion at end-2017.[14] The analysis uncovered a high concentration of notional among a few market participants and a tightly-knit structure of OTC derivatives markets. These features provide substantial netting opportunities – some 75 percent of notional was found to be composed of redundant trades that could be offset. Many of these redundant trades have been eliminated over the past decade through portfolio compression,[15] a process that reduces gross exposures, and hence counterparty risk, without changing the net position.

To further leverage the data, the ESRB is broadening its cooperation beyond its member institutions. The ECB and the ESRB have created a programme to develop novel analytical methods and to foster interaction between the policymaking and research communities.[16]

More in-depth analysis also benefits the private sector. While each reporting firm knows its own transactions, no firm has a picture of the market as a whole. By disseminating the analysis of the EMIR data, the ESRB provides individual institutions with a complete view of the market in which they transact, thereby enabling the private sector to reduce vulnerabilities by itself.


Let me conclude.

Policymakers across Europe have proven willing to use macroprudential policy to address risks and vulnerabilities.

These measures have helped counter the build-up of risks but continued monitoring and assessment remain necessary to be able to take timely and appropriate action to preserve financial stability.

Yet as the financial system moves to a greater use of market-based finance, authorities require a commensurate set of data to identify risks and vulnerabilities and tools to effectively address them.

Financial stability analysis is also entering new fields. This conference will highlight that the use of the EMIR data I mentioned is part of a wider endeavour to apply artificial intelligence and big data tools to identifying risks to financial stability. The conference will also consider the role of macroprudential policy in sustainable finance, such that the financial system contributes to sustainable growth.

I hope that when you return to your institutions after this conference you will feel inspired by the new methods of analysis and the possibilities macroprudential tools provide. On that note, I am pleased to open this third annual conference of the European Systemic Risk Board.

  1. [1]For a comprehensive overview of macroprudential measures taken in the EU in 2017 see ESRB (2018a), “A Review of Macroprudential Policy in the EU in 2017”, April.
  2. [2]For a detailed overview of the use of the buffer see Table 3 on page 29 in ESRB (2018a), op. cit.
  3. [3]The instruments are set out in the EU Capital Requirements Directive IV and the Capital Requirements Regulation. Member States can assign macroprudential instruments that are not covered by the scope of EU legislation. This includes instruments, such as loan-to-value and loan-to income limits that apply to borrowers and that in some jurisdictions therefore also apply to mortgage loans originated outside the banking sector.
  4. [4]The EU shadow banking measure includes all assets of the financial sector except those of banks, insurance corporations, pension funds, and central counterparties (CCPs). Within the EU shadow banking system, investment funds account for about one third and so-called other financial institutions (OFIs), including securitisation vehicles, account for the remainder.
  5. [5]More information on the EU shadow banking sector is available in ESRB (2018b), “EU Shadow Banking Monitor”, September.
  6. [6]European Commission (2015), “Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions – Action Plan on Building a Capital Markets Union”, September.
  7. [7]ESRB (2016), “ESRB response to the European Commission’s Consultation Document on the Review of the EU Macro-prudential Policy Framework”, October.
  8. [8]Recommendation of the European Systemic Risk Board of 7 December 2017 on liquidity and leverage risks in investment funds (ESRB/2017/6).
  9. [9]See ESRB (2016), “Recommendation ESRB/2016/14 on closing real estate data gaps”, Frankfurt am Main, October 2016.
  10. [10]See ESRB (2018b), op. cit.
  11. [11]See Barrett, D., Godfrey, B., and Golden, B., (2016), “New data collection of SPVs in Ireland: initial findings and measuring shadow banking”, Central Bank of Ireland Quarterly Bulletin, October
  12. [12]For a discussion of the use of big data by central banks see , for example, “Policy analysis with big data”, speech by Benoît Cœuré, Member of the Executive Board of the ECB, at the conference on “Economic and Financial Regulation in the Era of Big Data”, organised by the Banque de France, Paris, 24 November 2017
  13. [13]See Abad J., et al. (2016), “Shedding light on dark markets: First insights from the new EU-wide OTC derivatives dataset”, ESRB Occasional Paper Series No 11
  14. [14]See Aldasoro, I. and Ehlers, T. (2018), “The credit default swap market: what a difference a decade makes”, BIS Quarterly Review, June 2018
  15. [15]D’Errico, M., Roukny, T., (2017), “Compressing over-the-counter markets”, ESRB Working Paper 44.
  16. [16]The “EMIR Bridge Programme for Data Science” is described in more detail at

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