4

If you don't have a significant amount of losses in your portfolio to validate the model, you should be able to obtain external loss data and adjust it where necessary to better fit your organization. This is very common with operational loss models where operational losses are quite scarce.


3

I do not know the regulatory rules for this case, but methodologically you could take another similar dataset "peer data" and then check how correctly your model predicts the losses of this dataset.


1

I think it was Gnetting in 2011 who first proved that ES is not elicitable, which then threw some doubt as to whether it was backtestable. Carlo Acerbi pretty much put the matter to bed a few years ago in a number of papers, in which he explained that it does not matter for backtesting purposes whether or not ES is elicitable. Here is a link to his 2014 ...


1

Ideally, you should close the trade and book the pnl and do the analysis with respect to that. But the problem here is you are making the decision based on two trades. You can tackle this problem using the following way Determine the average duration of your trade say d days The number of days in the fold should be at least able to accommodate 10 trades. ...


1

If you do have some positive examples to estimate your model from, then, technically, you are dealing with the task of one-class classification (a.k.a anomaly detection, also directly related to density estimation). In your case the "anomalies" are high-risk customers, not present in the data. Various methods exist for anomaly detection and density ...


1

Look at Gilli & Schumann's paper. They provide a Bates' model estimates set, the way to improve such estimates calibrating those ones using an Heuristic model and, lastly, the relative codes in matlab, in order to be able to replicate the model. Unfortunately, there are not available the relative call prices estimated time series; I think that noone ...


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