FRTB, machine learning and swap transparency
The week in Risk.net, March 31–April 6, 2017
FRTB reboot planned over P&L storm
MACHINE LEARNING gives quants market insight
SWAP TRANSPARENCY causes unexpected problems
COMMENTARY: FRTB and model risk
The clock is ticking towards the planned 2019 implementation of Basel’s Fundamental review of the trading book (FRTB) and major problems still remain unsolved. Chief among them is the continuing dispute over the use of internal models.
It’s an argument that has already been raging for years in other areas of risk management – notably operational risk, where a long-running effort under Basel II rules to prod major banks towards using the model-based advanced measurement approach proved largely unsuccessful. Op risk regulators are now heading towards rejecting the use of internal models for regulatory capital calculation altogether, in favour of a simpler (but also contentious) standardised approach. Now the same question is proving equally insoluble in the case of market risk: in the FRTB’s profit and loss attribution test banks are facing a painful choice between an internal model approach that poses huge implementation problems, and a standardised approach that promises massive increases in market risk capital requirements.
Here and in other areas, it’s often model risk management at the smallest banks that faces the biggest test. The largest banks will receive more attention, due to their systemic importance and scale, but they tend to have the resources to comply; the smaller banks will find themselves stretched by comparison.
And it is not a problem that will go away; the arguments on each side are familiar to everyone. Internal models are more sensitive than blunt-instrument standardised approaches, and designed by people with a deeper understanding of each institution. But the results are also less easy for outsiders to use in comparisons, and will always carry the suspicion that the models have been rigged by their insider designers, who may well have strong motives to do so. External model oversight is at best an imperfect and highly costly remedy.
Ultimately, of course, it’s an issue of trust; the financial sector’s regulators simply can’t rely on the institutions they supervise to be honest with themselves about their own internal risks. Hence the costly oversight/blunt instrument dilemma they are left with – which is, in the end, partly of the banks’ own making.
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