The Manipulation Potential of Libor and Euribor

Eisl, Alexander; Jankowitsch, Rainer; Subrahmanyam, Marti G.

doi:10.1111/eufm.12126

Cited by 41 publications

(33 citation statements)

References 24 publications

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“…In their study, Eisl et al (2017) examined the manipulation potential of interbank benchmark rates in such a way that they set the lowest LIBOR fixing rate of the current day equal to the highest observed fixing rate. With this scenario the authors attempted to simulate how much a single bank could move the value of the benchmark rate if it intended to manipulate it.…”

Section: Definition Of Manipulation Potentialmentioning

confidence: 99%

“…The value of the manipulation potential was derived as the difference between the value of the original LIBOR and that of the modified LIBOR. In the context of this study the model constructed by Eisl et al (2017) was adapted and applied under a number of different scenarios.…”

Section: Definition Of Manipulation Potentialmentioning

confidence: 99%

“…In their study, Eisl et al (2017) tested the efficiency of the trimmed mean. They sought to measure the extent to which underreporting (where the bank's submitted fixing is below the mean, i.e.…”

Section: Trimmed Mean (Trimming Procedure)mentioning

confidence: 99%

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Integrity of Financial Benchmarks

Béres¹

2019

Financial and Economic Review

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Section: Definition Of Manipulation Potentialmentioning

confidence: 99%

Section: Definition Of Manipulation Potentialmentioning

confidence: 99%

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Integrity of Financial Benchmarks

Béres¹

2019

Financial and Economic Review

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“…14 The European Commission has not disclosed anything to date about its ongoing cartel investigations into the foreign exchange market. 15 As a result, little or no information about the supposed inner workings of the benchmark rates cartels is public.…”

mentioning

confidence: 99%

“…Similar documents of DoJ, CFTC and FCA cases exist for JPMorgan Chase, RBS, UBS, HSCB and Barclays. 15 European Commission, "Statement on the euro interest rate derivatives case," 20 May 2014; "Banks prepare to settle with Brussels over forex cartel probe," Financial Times, 19 November 2017. 16 United States v. Alex.…”

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confidence: 99%

Collusive Benchmark Rates Fixing

2018

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe …xing of the Libor and Euribor benchmark rates has proven vulnerable to manipulation. Individual rate-setters may have incentives to fraudulently distort their submissions. For the contributing banks to collectively agree on the direction in which to rig the rate, however, their interests need to be su¢ ciently aligned. In this paper we develop cartel theory to show how an interbank lending rates cartel can be sustained by preemptive portfolio changes. Exchange of information facilitates front running that allows members to reduce con ‡icts in their trading books. Designated banks then engage in eligible transactions rigging to justify their submissions. As the cartel is not able to always …nd stable cooperative submissions against occasional extreme exposure values, there is episodic recourse to non-cooperative quoting. Periods of heightened volatility in the rates may be indicative of cartelization. Recent reforms to broaden the class of transactions eligible for submission may reduce the level of manipulation, but can lead to more frequent collusive quoting.JEL-codes: E43, G14, G21, K21, L41

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Yield curve data choice and potential moral hazard: An empirical exercise on pricing callable bonds

2020

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There are alternative providers of zero-coupon yield curve datasets. This essential input for most financial purposes can also be estimated from crosssectional market price information. Although all these datasets are representations of the same reality, each dataset provides estimations from different baskets of assets and different fitting techniques. As the properties of the yield curves and their time series dynamics are different, the results of pricing interest rate derivatives may be sensitive to the choice among these datasets. We compare our own spot rates estimation from GovPX bond data and three popular available interest rate datasets. From an empirical exercise on pricing callable bonds, we observe relevant differences in economic terms when volatilities from different inputs are used. Therefore, financial institutions may have an incentive for choosing unduly one or another yield curve dataset in their self-interest, raising a moral hazard problem.

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The Manipulation Potential of Libor and Euribor

Cited by 41 publications

References 24 publications

Integrity of Financial Benchmarks

Integrity of Financial Benchmarks

Collusive Benchmark Rates Fixing

Yield curve data choice and potential moral hazard: An empirical exercise on pricing callable bonds

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