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#Kdk At εεχμτ 2010 12 03 V10
- 1. 1o Εκνικό Συνζδριο Επιςτθμονικισ Εταιρείασ
Χρθματοοικονομικισ Μθχανικισ και Τραπεηικισ (Ε.Ε.Χ.Μ.Τ.)
operational risk:
management Vs measurement
a practitioner’s view
03/12/2010
kdkarydias
Group OpRisk Officer
Eurobank EFG
- 2. introduction
operational risk is as old as the banking industry
itself
institutions had been reactive and responsive to
OpRisk as it arose rather than managing it in a
proactive manner
the confluence of the collapse of Barings and
the derivatives blow-ups in the mid-1990s was
one among several factors that led to Basel II
banks must compute an explicit capital charge for
operational risk
©2010 kdkarydias 2
- 3. the human factor
OpRisk is defined as the risk of loss resulting from
inadequate or failed:
internal processes designed, drafted, performed by
people
people are people
systems designed, implemented, operated by people
or from external events due to people or act of God
OpRisk is heavily related to people and to
human behaviour
©2010 kdkarydias 3
- 4. OpRisk characteristics
mainly endogenous
Unwanted by-product of the business activity
Not willingly incurred
Positively related to the complexity of the operations
permeates the entire enterprise, involving virtually
every employee, every business process and every
system
highly idiosyncratic
Tend to be less correlated to each other and to other risk
types
Less directly linked to business cycles
in principle (partially) controllable ex ante
©2010 kdkarydias 4
- 5. OpRisk characteristics cont’d
a trade off between risk and cost of avoidance
more qualitative rather than quantitative
potential OpRisk losses can be practically
unbounded
Observed losses are not related to bank size
Losses are not capped
Often significant time lags between cause and effect
Usually recognised “after the fact”
Loss severity distributions are fat-tailed
is sizeable compared to other risk types
©2010 kdkarydias 5
- 6. why measure OpRisk?
“What can’t be measured, can’t be managed…”
Joe Sabatini, Head of OpRisk, JP MorganChase & Co
to enable an effective risk mgt process
to measure progress
to quantify exposure in a forward looking manner
as a minimum you need “measures” with some
directional and relative reliability
is risk going up or down?
is risk higher here or there?
©2010 kdkarydias 6
- 7. objective of measuring OpRisk
provide an accurate view of the OpRisk profile of
the business over the next 12 months
what are the expected losses from OpRisk
what is the worst case loss from OpRisk
support the analysis of OpRisk
what are the top OpRisks
what is the worst case loss under stress conditions
how will changes to business strategy or control
environment affect the potential losses
how does the potential hit compare with other banks
©2010 kdkarydias 7
- 8. OpRisk capital (Basel II)
as per AMA, banks should put an OpRisk capital
aside in line with the 99.9% or even higher
confidence level over a one-year holding period
Basel II requires that a bank directly or indirectly
uses information from all four elements of
operational loss data, namely:
internal loss data
external loss data
scenario/workshop data
business environment & internal controls data
©2010 kdkarydias 8
- 9. measurement
quantification can be a powerful tool for
enhancing transparency, as long as it is credible.
financial industry managers and regulators have an
increasing interest in quantifying OpRisk
numerous conferences convened on quantifying
OpRisk and involving the top specialists - little
substantive has emerged
critiques, however, have been raised about the
limitations and less desirable consequences of
blind quantification
©2010 kdkarydias 9
- 10. critiques
“As far as the laws of mathematics refer to reality,
they are not certain; and as far as they are certain,
they do not refer to reality”
Albert Einstein, German Physicist, 1922
“But there are also unknown unknowns; there are
things we don't know we don't know”
Donald Rumsfeld, American Politician, 2002
“… a message to banks that have placed too much
emphasis on modeling operational risk to the
detriment of operational risk management”
House of Lords, Banking Supervision & Regulation Report, 2009
©2010 kdkarydias 10
- 11. modelling for OpRisk
many banks have focussed on meeting the capital
modelling requirements of Basel II and have lost
sight of the need to manage the business
most of the models currently in use to
quantify OpRisk are based on historical
loss data
considering the constant change of
technology and organizational
structures these data prove not
sufficient to quantify the OpRisk of
today
©2010 kdkarydias 11
- 12. using historical loss data
sparse existing data
employment of third-party loss data is impractical and partly
impossible
past loss data may no longer reflect current state and structure
of risks
risk prevention strategy “interferes” with loss history
due to non-transparent risk drivers, VaR (Value at Risk) levels are
of little informational value to management (too aggregated,
too abstract)
current structure / non-materialised risks can only be integrated
by direct estimation of a new loss distribution, which is complex
and difficult to reason
present knowledge about risk correlation cannot be formalised
©2010 kdkarydias 12
- 13. limitations of models
a model is always a strong reduction/ approximation of a more
complex reality
models are as good as the underlying assumptions: "garbage in–
garbage out effect"
not all risks are relevant and/or quantifiable: also here, use
20/80 approach
new external parameters and continuous restructurings can
make models questionable, as there is no reliable base material
comparisons of absolute model figures with those of third
parties are questionable: The prime internal value added of a
good model – including the stress test – is its trend over time
theoretical rigidity may not prevail over practical relevance and
credibility
models are always only part of an overall risk management
approach and must include common sense
©2010 kdkarydias 13
- 14. goal of OpRisk management ...
... is to reduce the frequency and severity of large, rare events
Minor Events
Generally not bank threatening
Experience makes easy to
High understand problems, measure Not relevant
frequency issues, take action [already out of business]
“cost of doing business”
Generates efficiency savings than
reduce material risks
Major Events
Can put banks out of business or
Low
Does not really matter harm reputation
frequency
Difficult to understand & prioritise
in advance
Small losses Large losses
©2010 kdkarydias 14
- 15. OpRisk management
OpRisk has, until now, baffled experts due
its lack of meaningful mathematical models
complex data requirements
the broad range of areas in which it occurs
most serious OpRisk losses can not be judged as
mere accidents
the only way to gain control over operational risk
is to improve the quality of control over the
possible sources of huge operational losses
©2010 kdkarydias 15
- 16. epilogue
“We need to overcome the tendency to believe
that a single number can summarise a
distribution of possibilities ... We need to be
creative in managing the unexpected”
John Trundle
“We believe that quantification should not be
exaggerated. Equally important is the need for
creativity in operational risk management”
Stefan Look
The PRiM Risk Newsletter, issue No 23, 10/2010
©2010 kdkarydias 16
- 17. The views expressed in this presentation have
not been subjected to peer review, and should be
interpreted accordingly
We thank our colleagues and partners for the
many fruitful interactions that have contributed
to this work
The views expressed in this presentation do not
necessarily reflect the views of Eurobank EFG
©2010 kdkarydias 17