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Me and my team has recently bought access to market data for sovereign bonds from Latin America countries. We are interested in finding a suitable outlier detection method to detect eventual errors in our data.

We have been googling multivariate outlier detection and found Robust Mahalanobis Distances as a possible tool. Has anyone in here had experience with bond data outlier detection using RMD?

Please discuss/make suggestions how we could/should tackle this problem

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  • $\begingroup$ are you doing historical data cleanup or online outlier rejection? The former is supposed to be easier since causality doesn't matter. I bet RMD wouldn't be to too different from many other methods. The online algo would be harder and I'm not sure RMD would give you any edge either. $\endgroup$ – Will Gu Apr 19 '18 at 19:21

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