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Risk Appetite for Dummies...26/10/2012 2 Five Challenges Risks In the Box •EP Curve uncertainty...
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26/10/2012 1 THE EXPANDING ROLE OF THE ACTUARY IN CATASTROPHE LOSS ESTIMATION AND MANAGEMENT CAS Annual Meeting November 13 th 2012 Peter Taylor Five Challenges in Outcome Space “It’s not a good idea to take a forecast from someone wearing a tie.” Nassim NicholasTaleb where there be …
Transcript of Risk Appetite for Dummies...26/10/2012 2 Five Challenges Risks In the Box •EP Curve uncertainty...
26/10/2012
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THE EXPANDING ROLE OF THE ACTUARY IN CATASTROPHE LOSS ESTIMATION AND MANAGEMENT
CAS Annual Meeting
November 13th 2012
Peter Taylor
Five Challenges
in Outcome Space
“It’s not a good idea to take a forecast from someone wearing a tie.”
Nassim NicholasTaleb
where there be …
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Five Challenges
Risks In the Box
• EP Curve uncertainty
• Closed form distributions
Risks Across the Boxes
• Multiple Models
Risks Outside of the Box
• Rumsfeld
• The ORSA
“Tell me what you know. Tell me what you don’t know. Then tell me what you think. Always distinguish which is which.”
US Secretary of State, Colin Powell
http://www.thedailybeast.com/newsweek/2012/05/13/colin-powell-on-the-bush-administration-s-iraq-war-mistakes.html
Another fine mess
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2004 Hurricanes
Bonnie Aug 3 – Aug 14 2004
Charley Aug 9 – Aug 14 2004
Frances Aug 25 – Sep 10 2004
Ivan Sep 2 – Sep 24 2004
Jeanne Sep 13 – Sep 28 2004
Katrina Storm Surge Footprint (RMS recon)
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And then …
• Model changes
• Christchurch
• Thailand
• …
Meanwhile, the crowds were stirring…
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“It is hard to overstate the damage done in the recent past by people who thought they
knew more about the world than they really did.”
John Kay in “Obliquity” 2010
― Seek transparency and ease of interrogation of any model, with clear expression of the provenance of assumptions.
― Communicate the estimates with humility, communicate the uncertainty with confidence.
― Fully acknowledge the role of judgement.”
D. J. Spiegelhalter and H. Riesch in “Don’t know, can’t know: embracing deeper
uncertainties when analysing risks” Phil. Trans. R. Soc. A (2011) 369, 4730–4750
“
“Somewhere along the way, appreciation for the inherent uncertainty in risk has been
diminished or even lost.
“Instead of models helping users to become more deeply risk-aware, the opposite can occur.
“Indeed, some now feel more vulnerable to a change in model versions than to the very catastrophes that
these models were intended to mitigate.”
Hemant Shah, CEO RMS
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“Learn to say ‘I don’t know’. If used when appropriate, it will be often.”
Karen Clark, CEO, Karen Clark & Company
“Understanding the models, particularly their limitations and sensitivity to assumptions, is the
new task we face.
“Many of the banking and financial institution problems and failures of the past decade can be directly tied to model failure or overly optimistic judgements in the setting of assumptions or the
parameterization of a model.”
Tad Montross, 2010, Chairman and CEO of GenRe in “Model Mania”
Risks in the Box
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1. EP Curve Uncertainty
Uncertainty in the box
Source: AIR
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So is the EP Curve this….
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Or this?
Managing Catastrophe
Model Uncertainty- Guy Carpenter 2011 12 years …
Occupancy - Industrial L.A., 1991, 2-story, Level 0 for all subtypes; 50 simulations,with 50,000-yr walkthrough each
TU: 40%BSW_C: 20%BSW_M: 20%W_E: 10%CBF: 7%LS: 3%
1000 Yr RP
Loss Distribution
Sensitivity to data
Sensitivity to data
CODA TypeL.A., 1991, 2-story, Level 2
TU: 100%
1000 Yr RP
Loss Distribution
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Sensitivity to Assumptions
loss amplification
scenario event set selection
damageability
severity
clustering
seasonality
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Swimming Naked?
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Losses $m
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Swimming Naked?
Another Example
• 200 Yr annual VaR: $100m
• Three cases, different spreads
• What is the chance of these losses?
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Case 1
Case 2
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What’s the Capital Provision?
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EP Curve Uncertainty
So the question is …
How do you allow for the uncertainty in the EP Curve?
Ideas include …
• Use confidence ranges • Quantify as contingent capital
2. Use of Closed Form Distributions
“Given the type of distributions adopted in the context of operational risk, it is especially difficult to represent the
aggregated loss distributions by closed form curves.”
Operational Risk – Supervisory Guidelines for the Advanced Measurement Approaches, June 2011
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Fooled by … Convenience
• Choose a probability distribution
• Here’s the toolkit (thank you text books) – Normal if lots of unrelated random additive quantities
– Lognormal for lost of unrelated random multiplicative quantities
– Poisson if it’s infrequent discrete events
– Pareto if infrequent high severity
– Beta if bounded (e.g. between 0 and 1)
• Two parameter distributions easy for Excel, phew!
• So we choose … Beta
• What could possibly go wrong?
Surely …
Jean-Sébastien Lagacé, Catastrophe Modeling, March 28th 2008 – Université Laval
Hurricane Ike
• Maximum wind speed of 145 mph • Landfall on 13th September 2008 near Galveston
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Hurricane Ike
Per-building damage assessment: •Undamaged Green •Minor Yellow (Light)
Brown (Moderate)
•Major Orange (Serious) Red (Total Collapse)
Total Undamaged Minor Major La Porte 10,150 28.9% 66.4% 4.7% Harris County, Hurricane Ike Residential Damage Assessment Dec 2008
Christchurch February 2011
What’s really going on here? Hint …
Tom Larsen, Severe Convective Storms in the US, RAA Conference 2012
“Loss estimation (net of deductibles, limits) is very sensitive to how damage is
modeled” Tom Larsen
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Damage is discrete not averaged
Fallacy of comparing mean location level losses with actual losses observed from a hurricane
Vineet Jain, Anatomy of a Damage Function: Dispelling the Myths, AIR 2010
“Modeled mean losses for locations are generally
smaller than location-level losses in realizations. The difference is that modeled
mean losses are positive for every location, while many locations in the realizations
will have zero losses” Vineet Jain
Mean SD 20 Yr VaR
48 29 102
Mean SD 20 Yr VaR
26 26 77
100,000 xs 25,000
---------- Ground-up Loss ----------
---------- Loss to Layer ----------
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Mean SD 20 Yr VaR
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Mean SD 20 Yr VaR
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---------- Loss to Layer ----------
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Uncorrelated Empirical Loss to Layer Distribution
Fooled by … Convenience
Note Beta understates severity
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EP Curve ELT Approximation
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Closed Form Distributions
So the question is … Can you justify use of
a particular closed-form Distribution?
Ideas include …
• Don’t use them unless you have to • Test goodness of fit against full
stochastic run
Risks Across the Boxes
3. Multiple Models
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Model Uncertainty
SOURCE: Guy Carpenter: Managing Catastrophe Model Uncertainty (Dec 2011)
Model Comparison – Different Models
C
B
A
<- l
aye
r -
>
Multiple Models
Prior Posterior
Model A
Model B
Model …
Evidence
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Model A
Model B
Model …
Subjective Frequentist
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On the Quantitative Definition of Risk
Kaplan and Garrick, Risk Analysis Vol 1 No 1 1981
Klibanoof, Marinacci, Mukerji, Econometrica, Vol. 73, No. 6 (November, 2005), 1849–1892
Multiple Models
So the question is …
How do you allow for Model risk?
Ideas include …
• Bayesian framework for model fusion
Risks Outside of the Boxes
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5. Rumsfeld
“… as we know, there are known knowns; there are things we know we know. We also
know there are known unknowns; that is to say we know there are some things we do not
know. But there are also unknown unknowns -- the ones we don't know we don't know.”
US Defence Secretary, Donald Rumsfeld
The Rumsfeld Quadrants
Known Unknowns
Unknown Unknowns
Known Knowns
Unknown Knowns
Known Unknowns
Unknown Unknowns
Known Knowns
Unknown Knowns
Risks in the Box
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Another interpretation
Recognised Unknowns
Unrecognised Unknowns
Recognised Knowns
Unrecognised Knowns
Recognised Unknowns
Unrecognised Unknowns
Recognised Knowns
Unrecognised Knowns
Rumsfeldian Allowances
• Model inadequacies (known unknowns) – Such as Northridge
and Christchurch faults
– Such as BI
• Model exclusions (unknown knowns) – Such as tsunami, or not modelled policy
conditions
– Such as contingent BI
• Black swans (unknown unknowns) – Based on our track record of getting it wrong
Known Unknowns
Unknown Unknowns
Known Knowns
Unknown Knowns
Known Unknowns
Unknown Unknowns
Known Knowns
Unknown Knowns
The Fog of Uncertainty
The question is …
How can we know what we don’t know about risk?
Ideas include …
• Known unknowns & unknown knowns • Accept we may not know and say so
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“ … we do not feel it is generally appropriate to respond to limitations in formal analysis by
increasing the complexity of the modelling. Instead, we feel a need to have an external perspective on
the adequacy and robustness of the whole modelling endeavour, rather than relying on within-
model calculations of uncertainties, which are inevitably contingent on yet more assumptions that
may turn out to be misguided. ”
D. J. Spiegelhalter and H. Riesch in “Don’t know, can’t know: embracing deeper uncertainties when analysing risks” Phil. Trans. R. Soc. A (2011) 369, 4730–4750
5. The ORSA
Solvency II
SII SCR
ORSA
S II Internal Model
Business Plan
Hygiene
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Objectives • What are we trying to
achieve? • How do we measure it?
Risks • What are the risks related to
these objectives? • What mitigations are in place?
Risk Appetite • Objectives versus Risk over what timescale? • Test - Scenarios? • Test - How robust?
The ORSA “the heart of Solvency II”
High-return deep-pocket underwriting vs Steady marginal underwriting vs Opportunistic cycle-based underwriting
Risk Appetite • Relate risk to business objective - state how much risk
we are prepared to take to achieve our objectives over what period of time
• Give examples, implications, and ask for challenge • Explain level of confidence in each scenario (the chance of
the scenario happening and our confidence in this estimate)
Opportunity
• NE Wind
• New Orleans Hurricane
Karen Clark & Company “RiskInsight Open”
Characteristic Events
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The ORSA
The question is …
How to define the business’s view of risk?
Ideas include …
• Risk appetite not just risk tolerance • Challenge outside the box
Summary
Risks In the Box
• EP Curve uncertainty
• Closed form distributions
Risks Across the Boxes
• Multiple Models
Risks Outside of the Boxes
• Rumsfeld
• ORSA
Contingent capital
Test goodness of fit
Assess unknowns
Think outside the box
A process for Model Risk
“Tell me what you know. Tell me what you don’t know. Then tell me what you think. Always distinguish which is which.”
US Secretary of State, Colin Powell
http://www.thedailybeast.com/newsweek/2012/05/13/colin-powell-on-the-bush-administration-s-iraq-war-mistakes.html
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