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LIBOR troubles: anomalous movements detection based on Maximum Entropy

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  • Aurelio F. Bariviera
  • M. T. Martin
  • A. Plastino
  • V. Vampa

Abstract

According to the definition of the London Interbank Offered Rate (LIBOR), contributing banks should give fair estimates of their own borrowing costs in the interbank market. Between 2007 and 2009, several banks made inappropriate submissions of LIBOR, sometimes motivated by profit-seeking from their trading positions. In 2012, several newspapers' articles began to cast doubt on LIBOR integrity, leading surveillance authorities to conduct investigations on banks' behavior. Such procedures resulted in severe fines imposed to involved banks, who recognized their financial inappropriate conduct. In this paper, we uncover such unfair behavior by using a forecasting method based on the Maximum Entropy principle. Our results are robust against changes in parameter settings and could be of great help for market surveillance.

Suggested Citation

  • Aurelio F. Bariviera & M. T. Martin & A. Plastino & V. Vampa, 2015. "LIBOR troubles: anomalous movements detection based on Maximum Entropy," Papers 1508.04512, arXiv.org.
    • Handle: RePEc:arx:papers:1508.04512
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    References listed on IDEAS

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    1. John C. Williams & John B. Taylor, 2009. "A Black Swan in the Money Market," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 58-83, January.
    2. Rosa Abrantes-Metz & Sofia Villas-Boas & George Judge, 2011. "Tracking the Libor rate," Applied Economics Letters, Taylor & Francis Journals, vol. 18(10), pages 893-899.
    3. Aurelio F. Bariviera & M. Belen Guercio & Lisana B. Martinez & Osvaldo A. Rosso, 2015. "A permutation Information Theory tour through different interest rate maturities: the Libor case," Papers 1509.00217, arXiv.org.
    4. John B. Taylor & John C. Williams, 2009. "A black swan in the money market," Proceedings, Federal Reserve Bank of San Francisco, issue jan.
    5. Aurelio Fernandez Bariviera & M. Bel'en Guercio & Lisana B. Martinez, 2015. "Data manipulation detection via permutation information theory quantifiers," Papers 1501.04123, arXiv.org.
    6. Martín, M.T. & Plastino, A. & Vampa, V. & Judge, G., 2014. "A parametric, information-theory model for predictions in time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 63-69.
    7. Aurelio Fernandez Bariviera & María Belén Guercio & Lisana B. Martinez & Osvaldo A. Rosso, 2015. "The (in)visible hand in the Libor market: an information theory approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(8), pages 1-9, August.
    8. Rosa Abrantes-Metz & Daniel Sokol, 2012. "Lessons from LIBOR for Detection and Deterrence of Cartel Wrongdoing," Antitrust Chronicle, Competition Policy International, vol. 11.
    9. Stenfors, Alexis, 2014. "LIBOR deception and central bank forward (mis-)guidance: Evidence from Norway during 2007–2011," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 32(C), pages 452-472.
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    2. Bariviera, Aurelio F. & Guercio, M. Belén & Martinez, Lisana B. & Rosso, Osvaldo A., 2016. "Libor at crossroads: Stochastic switching detection using information theory quantifiers," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 172-182.
    3. Fa-Bin Shi & Xiao-Qian Sun & Jin-Hua Gao & Li Xu & Hua-Wei Shen & Xue-Qi Cheng, 2019. "Anomaly detection in Bitcoin market via price return analysis," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-11, June.
    4. Lee, Kang-Bok & Han, Sumin & Jeong, Yeasung, 2020. "COVID-19, flattening the curve, and Benford’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).

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