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The alarm around non-modellable risk factors seems to have
faded in the long-awaited final version of the FRTB Market Risk
Framework published by the BCBS on January 14. But do the numbers
support it?
As a reminder, the original FRTB framework required risk factors
to be observable 24 times a year and at least once a month to be
eligible for expected shortfall under IMA. The industry (and our
analyses) quickly demonstrated that the binding constraint was the
gap requirement (once a month) rather than the count of 24 - which
had some firms wondering whether they would have to reschedule
their traders' holiday calendars. There was even talk of abandoning
seasonal markets such as credit. In a previous post we called this
unintended consequence the "FRTB Scrooge of Christmas"
effect and the industry lobbied for a relaxed gap criterion of
three observations every 90 days.
On the face of it, Basel's response is pretty accommodating by
offering the following optionality: either the risk factor is
observable 24 times a year and at least four times in 90 days or it
can pass by achieving 100 observations in a year. At IHS Markit,
not only are we fortunate enough to have tens of millions of
cross-asset Real Price Observation (RPOs) across Interest-Rates
derivatives, Credit Derivatives, Bonds, Equities and FX (and that's
before any additional pooling from the banks), but we also have the
analytics infrastructure to run such studies on the fly. This
article will thus attempt to quantify the regulatory option and
explore the fruits of industry advocacy.
Our data shows that the second leg of the criteria (100
observations in a year) has virtually no impact in terms of
reducing the number of NMRFs for alternative 2 (using the
regulatory buckets). So instead we will focus on the change in the
original gap criterion: one per month became four in 90 days. As
previously, this affects risk factors passing the count but failing
the gap (the ambers of our RAG coloured heatmaps!).
As rates dominates capital for most firms, we thought we'd start
there: unfortunately, only 13 out of 379 (i.e. 3.5%) Interest Rate
fixing tenor risk factors now pass the RFET where they previously
didn't (Amber). Examples include the long end of CZK.3m and PLN.3m
curves and a few middle buckets of HKD.1m, SGD.6m.
However, the improvement is quite significant on markets
suffering from structural or behavioural seasonality such as cash
bonds or CDS. Below is the result of a study on the aggregate CDS
single names and cash bond universes respectively.
Out of 3.2k traded CDS issuer buckets, the 592 previously
modellable ones are joined by another 264 amber risk factor buckets
to achieve 26% overall modellability.
The next question is what level of granularity is required to
pass PLA: is issuer-level sufficient? Predictably, the lower the
granularity, the lower the modellability pass rates: when combining
issuer, currency and doc clause, overall modellability falls to 19%
under the final rules (vs 13% on the previous rules so still an
improvement!). Alternatively, banks have the option to capitalise
just the basis between non-modellable issuer curves and modellable
country/sector/rating curves - which we will explore in a later
article.
This is all well and good in terms of improving the previous
version of the FRTB framework but does it live up to industry lobby
expectations? Originally the industry proposed a gap criterion of
three observations in 90 days. Could one more observation per
quarter have made a material difference?
For our 3.2k traded CDS issuer buckets, of which the modellable
sub-set is shown below, the answer is a 3% modellability decrease
between three and four in 90 days. The graph below demonstrates how
this varies with the granularity of the CDS curves.
In the much larger cash bonds universe of over 100,000
issue-maturity risk factors, the previous rules yielded roughly 14%
modellable risk factors whereas the new gap criterion achieves a
whopping 25% MRF. This jump of almost 80% can be seen by the
increase in the height of the green bars in the graph below.
Clearly the materiality from a capital perspective of this will
be very desk and portfolio specific. It will also depend on the
risk factor configuration in the planned IMA model. For banks to
make the right decisions, not least on whether to go for IMA for a
given desk, it is crucial to capture the benefits of proxies which
can constitute a significant portion of capital via the SES NMRF
charge. Our experience suggests that a surprising number of firms
still lack sufficiently realistic assessments of the full IMA
round-trip and risk which can lead to sub-optimal and even
irreversible decisions around development or model approval.
Posted 06 February 2019 by Paul Jones, Global Head – FRTB Solutions, IHS Markit
IHS Markit provides industry-leading data, software and technology platforms and managed services to tackle some of the most difficult challenges in financial markets. We help our customers better understand complicated markets, reduce risk, operate more efficiently and comply with financial regulation.
