Validation tools

Christian Meyer and Peter Quell

This chapter will present a number of practical validation tools. Of course, the applicability and usefulness of specific tools is restricted, and a validation tool applied in the wrong place may do more harm than good. Moreover, it should never be claimed that a toolbox is complete – a validation exercise will always require original thinking and a sceptical mind.

Model users are usually less interested in the inner workings of a QRM, but concentrate instead on results, and their credibility and usefulness. It is therefore intuitive to consider tools for validation of model results first, and then proceed to other tools. The following validation tools will be explored in this chapter.

    • The arsenal of statistical methods might be put to use, with backtesting being the most prominent tool in this respect. However, statistical methods require sufficient data quality and are not always applicable.

    • Benchmarking means construction of alternative models and the comparison of results. Such comparison can provide valuable insight. However, construction of a benchmark model can be very complex (that is, as complex as the construction of the original model).

    • A

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