blowing through the range 2014 clrs presentation · blowing through the range 2014 clrs...
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![Page 1: Blowing through the range 2014 CLRS presentation · Blowing through the range 2014 CLRS presentation September 15, 2014 Christopher Andersen, FCAS, MAAA Ron Fowler, FCAS, MAAA](https://reader036.vdocument.in/reader036/viewer/2022071015/5fce6eda4422f64b532aa539/html5/thumbnails/1.jpg)
Blowing through the range
2014 CLRS presentationSeptember 15, 2014
Christopher Andersen, FCAS, MAAARon Fowler, FCAS, MAAA
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Agenda
► Actuarial ranges and their context► Communication of the actuarial range► When things go wrong – Case studies
► Financial guarantee► Catastrophes► Asbestos
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What is in an actuarial range?
► Actuarial standards of practice No. 43► Property / Casualty Unpaid Claim Estimates
► Actuarial central estimate► An estimate that represents an expected value over the range of
reasonably possible outcomes (“range”).
► 3.3.1 …may not include all conceivable outcomes, as, for example, it would not include conceivable extreme events where the contribution of such events to an expected value is not reliably estimable.
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What disclosures are proper for an actuary to communicate?
► 3.6.7 – External conditions► Claim obligations are influenced by … potential economic
changes, regulatory actions, judicial decisions, or political or social forces. …the actuary is not required to have detailed knowledge of or consider all possible external conditions…
► 3.6.7 – Changing conditions► The actuary should consider whether there have been significant
changes in conditions …Examples include reinsurance program changes, … claims personnel [changes], …
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What disclosures are proper for an actuary to communicate? [cont.]
► 3.6.8 – Uncertainty► The actuary should consider the uncertainty … This standard does
not require or prohibit the actuary from measuring this uncertainty. … the actuary should consider the types and sources of uncertainty … [and] may include uncertainty due to model risk, parameter risk, and process risk.
► 4.2 – Additional disclosures (re: range of estimates)► In the case when the actuary specifies a range of estimates, the
actuary should disclose the basis of the range provided
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When things go wrong – Case studies
► 2008 financial crises ► Financial guarantee insurance
► Unusual catastrophes► Hurricane Katrina► Thailand flooding► New Zealand earthquake
► Asbestos
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The 2008 financial crises was more sudden than previous depressions in the financial markets
-40.0%
-30.0%
-20.0%
-10.0%
0.0%
10.0%
20.0%
30.0%DJIA – Year over year percentage change
Prepared by EY
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Industry loss ratio spiked to 200% to 300% and have still not recovered to pre-financial crisis levels
0.0%100.0%200.0%300.0%400.0%500.0%600.0%700.0%800.0%900.0%
1000.0%
Loss ratios for Industry and Top Financial Guarantee and Mortgage-Back Securities writers
P&C Industry Company 1 Company 2 Company 3 Company 4
Based on SNL data; Prepared by EY
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Unprecedented financial market performance underlined inadequate reserve amounts in financial guarantee and mortgage guarantee products
-50.0%-25.0%
0.0%25.0%50.0%75.0%
100.0%125.0%
1 year reserve development / Prior year reserves
Commercial Auto Workers CompensationMedical Professional Fidelity and suretyFinancial Guarantee and Mortgage Gaurantee
Based on SNL data; Prepared by EY
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Financial guarantee insurance
► What went wrong?► Was the cause of the reserve development due to process risk or
parameter risk?
► The development was due to a “tail event” or process risk
► How did the actuarial models uphold?► New information needed to be reflected that was not traditional to
historical analysis and development► Economic projections► TARP program
► Scenario testing is critical in developing a range of expectations
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2006 vintage curves indicated stability
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%2 8 14 20 26 32 38 44 50 56 62 68 74 80 86 92 98 104
110
116
122
128
Seve
rity
Loan Age
1998199920002001200220032004200520062007
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Post 2008 financial crisis the historical development was no longer relevant
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%2 8 14 20 26 32 38 44 50 56 62 68 74 80 86 92 98 104
110
116
122
128
Seve
rity
Loan Age
1998199920002001200220032004200520062007
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Financial guarantee insuranceCase study – Post-crisis estimates and range
► Scenario testing was employed as future state after shock was difficult to ascertain from historical data► What movement in transition probabilities is plausible?► How high can loss severities be?
Scenarios Transition probabilities Loss Severities Difference to Median
A No change Decrease by 5% -36%
B Used 1 month look back No change -28%
C Used 2004 probabilities Increased by 5% -4%
D Lag vintage 1 year Increased by 5% +4%
E Used 2005 probabilities for all older vintages Increased by 7.5% +57%
F Used 2007 probabilities for all vintages No change +118%
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Developing a range using non-traditional information and scenario testing
► Scenario testing► Search for relevant information from non-traditional sources
► Economic data and trends► Understand reform and its impact
► Discuss with client reasonability of assumptions and apply professional skepticism to avoid biases► Develop a maximum probable loss scenario► Consider industry perspective on phenomenon
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When things go wrong – Case studies
► 2008 financial crises ► Financial guarantee insurance
► Unusual catastrophes► Hurricane Katrina► Thailand flooding► New Zealand earthquake
► Asbestos
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Recent catastrophes have proven to have stipulations that make predicting ultimate losses more uncertain
► Hurricane Katrina► Wind versus water disputes► Extended recovery and rebuilding period
► Thailand flooding► Claim investigation was severely delayed to due standing water
and the inability to investigate claim sites► Largest claims were business interruption and have a time-
element
► New Zealand earthquake
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The Thailand Flooding claims have been harder to identify and investigate due to the lingering water in warehouses
Prepared by EY; based on RAA data. 1 period is equal to 3 months
0.0%10.0%20.0%30.0%40.0%50.0%60.0%70.0%80.0%90.0%
100.0%
1 3 5 7 9 11 13 15 17 19
% C
ase
incu
rred
to u
ltim
ate
Periods
Thailand FloodsKatrinaRitaWilmaNZ2 EQJapan EQWTC
► Significantly lower after 6 periods► Time-element coverage is causing
uncertainty
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Historical earthquake % of case incurred to ultimate development
Prepared by EY; based on RAA data. 1 period is equal to 3 months
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
120.0%
1 2 3 4 5 6 7 8 9 1011121314151617181920
% C
ase
incu
rred
to u
ltim
ate
Periods
NZ EQ2ChileJapanLoma PrietaNorthridgeNZ EQ1NZ EQ3
► The case incurred amounts continue to rise through 9 report periods
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New Zealand earthquake has experience high “event creep” than other recent natural catastrophes
► Event creep (New Zealand)► New Zealand Earthquakes: Christchurch, NZ
► NZ I: October 2010► NZ II: February 2011► Many smaller aftershocks
► New Zealand Earthquake Commission pays up to 100,000 NZD property, 20,000 NZD contents
► Suncorp and IAG have majority of the market share for additional insurance
► Reinsurers then provide excess cover (they get the effects of the creep)
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Reason for event creep for New Zealand earthquake
► Scope of event (largest natural CAT year in NZ)► Apportionment: losses were apportioned between the two
events, often with complicated models► Renewals: the two earthquakes are treated as two events
and renewals separate the contracts so there are new limits
► Liquefaction (some neighborhoods abandoned), this was not factored into many loss models
► Claim settlement time (government insurer handling claims, slower than private insurance to process)
► Cordoned off areas (Red Zone) causes BI losses
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Liquefaction
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When things go wrong – Case studies
► 2008 financial crises ► Financial guarantee insurance► Mortgage-back security insurance
► Unusual catastrophes► Hurricane Katrina► Thailand flooding► New Zealand earthquake
► Asbestos
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A history of predictions and re-predictions
► The poor track record of estimating asbestos
0100,000200,000300,000400,000500,000600,000700,000800,000900,000
1,000,000
1988 1993 1997 2005 2013
Manville Trust – Asbestos Claims filing
Filed claims Expected Ultimate filed claims
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AM Best’s asbestos and environmental studies have a similar shape
0
10
20
30
40
50
60
70
80
90
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
P/C Industry Net Asbestos Losses
Incurred Losses AM Best Ultimate
Based on annual statement data and AM Best
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So what’s in an asbestos range estimate?
► Historical development may have been considered an (un)conceivable extreme scenario or event
► Ranges are based on the best available data at that time► Survival ratio analyses► Exposure analyses► Market share and industry estimates
► Wide range of estimates may result – what is the right range?