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Statistical evidence about LIBOR manipulation: A "Sherlock Holmes" investigation

Author

Listed:
  • Julien Fouquau

    (NEOMA - Neoma Business School)

  • Philippe K. Spieser

    (LABEX Refi - ESCP Europe - Ecole Supérieure de Commerce de Paris)

Abstract

This paper contributes to the crucial problem of LIBOR malfunctioning due to its manipulation by banks, a phenomenon described clearly in the FSA Inquiry Report published in September 2012. After applying classical tests of non-stationarity to a series of participating banks' LIBOR quotes, we detected some significant breaks in the data that correspond to significant economic events, namely Lehman Brothers' bankruptcy or central banks' decisions. Our conclusion is that a possible manipulation, confirmed ex post in the FSA report, can be deduced throughout the crisis period as a result of the behavior of the coefficients in a linear three-regime Threshold Regression model. Finally, we applied an original procedure to detect those banks most likely to build cartels – or at least homogeneous groups – during the important period of turmoil under review, namely August 2007 through November 2012. This method, called hierarchical clustering analysis, helped us to precise the conclusions of the abovementioned FSA inquiry.

Suggested Citation

  • Julien Fouquau & Philippe K. Spieser, 2015. "Statistical evidence about LIBOR manipulation: A "Sherlock Holmes" investigation," Post-Print hal-01160060, HAL.
  • Handle: RePEc:hal:journl:hal-01160060
    DOI: 10.1016/j.jbankfin.2014.03.039
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    Cited by:

    1. Dániel Horváth & Eszter Makay, 2015. "Analysis methodology of interbank reference rates - International trends and the results of the first Hungarian annual statistical analysis for 2014," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 14(2), pages 62-88.
    2. Eross, Andrea & Urquhart, Andrew & Wolfe, Simon, 2016. "Liquidity risk contagion in the interbank market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 45(C), pages 142-155.
    3. Frino, Alex & Ibikunle, Gbenga & Mollica, Vito & Steffen, Tom, 2018. "The impact of commodity benchmarks on derivatives markets: The case of the dated Brent assessment and Brent futures," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 27-43.
    4. Nuria Boot & Timo Klein & Maarten Pieter Schinkel, 2017. "Collusive Benchmark Rates Fixing," Tinbergen Institute Discussion Papers 17-122/VII, Tinbergen Institute, revised 17 Apr 2019.
    5. Florian El Mouaaouy, 2018. "Financial crime ‘hot spots’ – empirical evidence from the foreign exchange market," The European Journal of Finance, Taylor & Francis Journals, vol. 24(7-8), pages 565-583, May.
    6. Hagen Rafeld & Sebastian G. Fritz-Morgenthal & Peter N. Posch, 2020. "Whale Watching on the Trading Floor: Unravelling Collusive Rogue Trading in Banks," Journal of Business Ethics, Springer, vol. 165(4), pages 633-657, September.
    7. Depeyrot, Jean-Noel & Duval, Marion, 2018. "‪Global Dairy Trade, plateforme électronique néo-zélandaise de commercialisation. Quelles opportunités pour les marchés mondiaux de produits laitiers ?," Économie rurale, French Society of Rural Economics (SFER Société Française d'Economie Rurale), vol. 364(April-Jun).
    8. Li, Ming & Sun, Hang & Zong, Jichuan, 2021. "Intertemporal imitation behavior of interbank offered rate submissions," Journal of Banking & Finance, Elsevier, vol. 132(C).
    9. Nuria Boot & Timo Klein & Maarten Pieter Schinkel, 2017. "Collusive Benchmark Rates Fixing," Discussion Papers of DIW Berlin 1715, DIW Berlin, German Institute for Economic Research.
    10. Bahoo, Salman, 2020. "Corruption in banks: A bibliometric review and agenda," Finance Research Letters, Elsevier, vol. 35(C).
    11. Contessi, Silvio & De Pace, Pierangelo & Guidolin, Massimo, 2020. "Mildly explosive dynamics in U.S. fixed income markets," European Journal of Operational Research, Elsevier, vol. 287(2), pages 712-724.
    12. Florian Neitzert & Matthias Petras, 2022. "Corporate social responsibility and bank risk," Journal of Business Economics, Springer, vol. 92(3), pages 397-428, April.
    13. Alexander Eisl & Rainer Jankowitsch & Marti G. Subrahmanyam, 2017. "The Manipulation Potential of Libor and Euribor," European Financial Management, European Financial Management Association, vol. 23(4), pages 604-647, September.
    14. Carpenter, Seth B. & Demiralp, Selva & Senyuz, Zeynep, 2016. "Volatility in the federal funds market and money market spreads during the financial crisis," Journal of Financial Stability, Elsevier, vol. 25(C), pages 225-233.
    15. Alan De Genaro & Marco Avellaneda, 2018. "Pricing Interest Rate Derivatives Under Monetary Changes," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 1-28, September.
    16. Rodríguez-López, Araceli & Fernández-Abascal, Hermenegildo & Maté-García, Jorge-Julio & Rodríguez-Fernández, José-Miguel & Rojo-García, José-Luis & Sanz-Gómez, José-Antonio, 2021. "Evaluating Euribor Manipulation: Effects on Mortgage Borrowers," Finance Research Letters, Elsevier, vol. 40(C).
    17. Cui, Jin & In, Francis & Maharaj, Elizabeth Ann, 2016. "What drives the Libor–OIS spread? Evidence from five major currency Libor–OIS spreads," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 358-375.
    18. Jiyoung Lee & Jung Jae Kim & Jinook Jeong, 2022. "An Empirical Assessment of Collusion in the Negotiable Certificates of Deposit Market in Korea: A Discriminant Analysis," Asian Economic Journal, East Asian Economic Association, vol. 36(2), pages 203-223, June.

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    Keywords

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    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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