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

Victor Pontines and Ole Rummel

Finance Research Letters, 2023, vol. 55, issue PA

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.

Keywords: LIBOR; Outlier detection; Anomaly detection; Lasso regression (search for similar items in EconPapers)
JEL-codes: C49 E43 G12 G15 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:55:y:2023:i:pa:s1544612323002246

DOI: 10.1016/j.frl.2023.103852

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