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LIBOR meets machine learning: A Lasso regression approach to detecting data irregularities

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  • Pontines, Victor
  • Rummel, Ole

Abstract

We use the Lasso linear regression technique to detect level shifts and additive outliers in both the daily 3 and 6 month US dollar LIBOR rates, and compare the results to an identical application of this technique to non-LIBOR, US short-term funding benchmarks. We find that the two LIBORs have the largest incidence of outliers, especially, additive outliers. For two non-LIBOR benchmarks, the 6 month Treasury bill and the Federal funds effective rate, no outliers were detected, which reinforces our results. Furthermore, our identified outlier episodes for both LIBOR rates fall inside the period that the manipulation of LIBOR occurred.

Suggested Citation

  • Pontines, Victor & Rummel, Ole, 2023. "LIBOR meets machine learning: A Lasso regression approach to detecting data irregularities," Finance Research Letters, Elsevier, vol. 55(PA).
    • Handle: RePEc:eee:finlet:v:55:y:2023:i:pa:s1544612323002246
    DOI: 10.1016/j.frl.2023.103852
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    References listed on IDEAS

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    1. Özer Depren & Mustafa Tevfik Kartal & Serpil Kılıç Depren, 2021. "Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
    2. Priyank Gandhi & Benjamin Golez & Jens Carsten Jackwerth & Alberto Plazzi, 2019. "Financial Market Misconduct and Public Enforcement: The Case of Libor Manipulation," Management Science, INFORMS, vol. 65(11), pages 5268-5289, November.
    3. Abrantes-Metz, Rosa M. & Kraten, Michael & Metz, Albert D. & Seow, Gim S., 2012. "Libor manipulation?," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 136-150.
    4. 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.
    5. Monticini, Andrea & Thornton, Daniel L., 2013. "The effect of underreporting on LIBOR rates," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 345-348.
    6. Darrell Duffie & Jeremy C. Stein, 2015. "Reforming LIBOR and Other Financial Market Benchmarks," Journal of Economic Perspectives, American Economic Association, vol. 29(2), pages 191-212, Spring.
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    More about this item

    Keywords

    LIBOR; Outlier detection; Anomaly detection; Lasso regression;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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