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Big data and central banks

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  • Bholat, David

    (Bank of England)

Abstract

This article recaps an event held at the Bank of England on 2-3 July 2014. The article covers three main points. First, it situates the event within the context of the Bank’s new approach to data as set out in the Bank’s Strategic Plan. Second, it summarises and reflects on major themes from the event. And third, the article links central banks’ emerging interest in big data approaches with their broader uptake by other economic agents.

Suggested Citation

  • Bholat, David, 2015. "Big data and central banks," Bank of England Quarterly Bulletin, Bank of England, vol. 55(1), pages 86-93.
    • Handle: RePEc:boe:qbullt:0168
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    File URL: https://www.bankofengland.co.uk/-/media/boe/files/quarterly-bulletin/2015/big-data-and-central-banks.pdf?la=en&hash=91BA29740CFE8B0334F7E874EEB57D3053DFC1C0
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    References listed on IDEAS

    as
    1. Benos, Evangelos & Wetherilt, Anne & Zikes, Filip, 2013. "Financial Stability Paper No 25: The structure and dynamics of the UK CDS market," Bank of England Financial Stability Papers 25, Bank of England.
    2. Bell, Venetia & Co, Lai Wah & Stone, Sophie & Wallis, gavin`, 2014. "Nowcasting UK GDP growth," Bank of England Quarterly Bulletin, Bank of England, vol. 54(1), pages 58-68.
    3. David M Bholat, 2013. "The future of central bank data," Journal of Banking Regulation, Palgrave Macmillan, vol. 14(3-4), pages 185-194, July.
    4. Flood, Mark D. & Lemieux, Victoria L. & Varga, Margaret & William Wong, B.L., 2016. "The application of visual analytics to financial stability monitoring," Journal of Financial Stability, Elsevier, vol. 27(C), pages 180-197.
    5. Davey, Nick & Gray, Daniel, 2014. "How has the Liquidity Saving Mechanism reduced banks’ intraday liquidity costs in CHAPS?," Bank of England Quarterly Bulletin, Bank of England, vol. 54(2), pages 180-189.
    6. Benos, Evangelos & Sagade, Satchit, 2012. "High-frequency trading behaviour and its impact on market quality: evidence from the UK equity market," Bank of England working papers 469, Bank of England.
    7. Merrouche, Ouarda & Schanz, Jochen, 2010. "Banks' intraday liquidity management during operational outages: Theory and evidence from the UK payment system," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 314-323, February.
    8. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    9. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    10. Finan, Kevin & Lasaosa, Ana & Sunderland, Jamie, 2013. "Tiering in CHAPS," Bank of England Quarterly Bulletin, Bank of England, vol. 53(4), pages 371-378.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    2. Atz, Ulrich & Bholat, David, 2016. "Peer-to-peer lending and financial innovation in the United Kingdom - Ulrich Atz and David Bholat," Bank of England working papers 598, Bank of England.
    3. Bholat, David, 2016. "Modelling metadata in central banks," Statistics Paper Series 13, European Central Bank.
    4. Antoaneta Serguieva & David Bholat, 2017. "Multichannel contagion vs stabilisation in multiple interconnected financial markets," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    5. Paloviita, Maritta & Haavio, Markus & Jalasjoki, Pirkka & Kilponen, Juha & Vänni, Ilona, 2020. "Reading between the lines : Using text analysis to estimate the loss function of the ECB," Research Discussion Papers 12/2020, Bank of Finland.
    6. Antoaneta Serguieva, 2017. "Multichannel Contagion vs Stabilisation in Multiple Interconnected Financial Markets," Papers 1701.06975, arXiv.org, revised Apr 2017.
    7. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
    8. Flood, M. D. & Jagadish, H. V. & Raschid, L., 2016. "Big data challenges and opportunities in financial stability monitoring," Financial Stability Review, Banque de France, issue 20, pages 129-142, April.
    9. Stefan Angrick & Naoyuki Yoshino, 2020. "From Window Guidance to Interbank Rates: Tracing the Transition of Monetary Policy in Japan and China," International Journal of Central Banking, International Journal of Central Banking, vol. 16(3), pages 279-316, June.
    10. Okiriza Wibisono & Hidayah Dhini Ari & Anggraini Widjanarti & Alvin Andhika Zulen & Bruno Tissot, 2019. "The use of big data analytics and artificial intelligence in central banking – An overview," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    11. repec:zbw:bofrdp:2020_012 is not listed on IDEAS
    12. Pongsak Luangaram & Warapong Wongwachara, 2017. "More Than Words: A Textual Analysis of Monetary Policy Communication," PIER Discussion Papers 54, Puey Ungphakorn Institute for Economic Research.
    13. repec:zbw:bofitp:2018_004 is not listed on IDEAS
    14. Giuseppe Bruno & Hiren Jani & Rafael Schmidt & Bruno Tissot, 2020. "Computing platforms for big data analytics and artificial intelligence," IFC Reports 11, Bank for International Settlements.
    15. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    16. Romain Plassard, 2020. "Making a Breach: The Incorporation of Agent-Based Models into the Bank of England's Toolkit," GREDEG Working Papers 2020-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    17. Stefan Angrick & Naoyuki Yoshino, 2020. "From Window Guidance to Interbank Rates: Tracing the Transition of Monetary Policy in Japan and China," International Journal of Central Banking, International Journal of Central Banking, vol. 16(3), pages 279-316, June.

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