swiss re sigma catastrophe database by lucia bevere
TRANSCRIPT
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Swiss Re sigma catastrophe database Lucia Bevere, Senior Catastrophe Data Analyst
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Overview of the sigma catastrophe database
• International commercial database recording both natural and man-made disasters
• Global scale
• Over 10 000 entries
• Recording started in 1970
• Event-based
• Disasters are now geocoded at national (or state/province) level for GIS purposes
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Focus on insured losses
• Annual picture of global catastrophic activity
• Trends in insured losses
Disaster losses, USD billion (at 2013 prices)
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Source: Swiss Re sigma catastrophe database
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Insured catastrophe losses - geographical distribution
2013 2012 10-y avgSource: Swiss Re sigma catastrophe database
42%
1%
5%
15%
4%
12%
11%
7%
34%
62%
84%
7%
0%
3%
3%
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Storms account for the great majority of insured losses
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Source: Swiss Re sigma catastrophe database
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Selected insured Nat Cat loss potentials compared to loss history
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Peak risks
Earthquake and windstorm ...
... in Industrialized countries ...
... with relatively high insurance density
Katrina 2005
Northridge 1994
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80
8
Lothar 1999
Storm Europe
40
Hurricane US + Caribbean incl. NFIP, FHCF
Earthquake Japan incl. JER
85
55
Earthquake California
Historic insured loss (sigma, indexed to 2013)
Modelled 200 year insured loss
Insurance loss scenarios [USD bn]
FHCF: Florida Hurricane Catastrophe Fund
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Tohoku 2011
JER: Japan Earthquake Reinsurance Scheme State-run schemes
NFIP: National Flood Insurance Program
200
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Structure
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Classification of natural catastrophes
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Category Peril Group Peril
Natural catastrophe
Earthquake
Earthquake
Tsunami
Volcano eruption
Weather-related
Storm
Flood
Hail
Cold, frost
Drought, bush fires, heat waves
Other natural catastrophes
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Examples of database entries
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Country Peril Date Number of victims Amount of damage
Event Source
Vietnam Storm 30.09.2007 95 dead 8 missing 90 injured 125 000 homeless USD 126m economic losses
Typhoon Lekima with winds up to 130 km/h, heavy rain, landslides; 9 500 houses destroyed, 115 000 ha of cropland (of which 30 000 ha of rice) flooded
Central Committee for Flood and Storm Control
US Storm 31.01.2011 36 dead USD 1 034m insured losses USD 2 000m economic losses
Groundhog Day Blizzard winter storm, heavy snowfall, freezing rain; damage to private, industrial and commercial buildings, damage to power houses, 20 000 flights cancelled
Various
Source: Swiss Re sigma catastrophe database
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Other natural catastrophe
Storm
Cold, frost
Storm
Drought, bush fire, heat wave
Other natural catastrophe
Weather related
Earthquake
Flood
Earthquake
Swiss Re current classification Swiss Re current classification IRDR-Data suggestion
Classification redesign
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Other natural catastrophe
Storm
Cold, frost
Storm
Drought, bush fire, heat wave
Other natural catastrophe
Weather related
Earthquake
Flood
Earthquake
Swiss Re current classification Swiss Re current classification IRDR-Data suggestion
Classification redesign
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Variables: minimum selection thresholds for 2014
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• Insured losses and business interruption losses:
– marine USD 19.3 m
– aviation USD 38.6 m
– other property losses USD 48.0 m
• or Total losses (economic damage) USD 96.0 m
• or Casualties
– dead or missing 20
– injured 50
– homeless 2000
Each year the monetary thresholds are adjusted for inflation
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Sources
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Swiss Re
• claims assessors
• underwriters
• National disaster authorities
• EU, UN, World Banks etc.
• Ad hoc scientific research
• etc.
Press National meteo/seismological
services
Industry
Governments, International
organisations, Science, NGOs etc.
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How reliable? Global?
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Ocean Drive, FL, 1926. Ocean Drive, FL, 2000.
Population Growth Rates (1960-2000)
All US 57%
Florida 223%
Increasing values
concentration in exposed areas
Insurance penetration
Changing hazard
climate variability
climate change
Losses are not normalised for exposure
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Assessment of social losses…
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NOAA Brunkart et al (2008)
Markwell et al (2010)
Deaths
Total
Louisiana
… straightforward?
Hurricane Katrina
1833
1577 1155 971
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Economic losses subject to high degree of uncertainty
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• Data collection is not systematic • Lack of hazard-specific observation/monitoring • Official damage reports are often missing (particularly for
small/medium events) • No central repository • Data are often collected by different authorities using different
criteria and with different users in mind – ground losses – meteorological aspects
• No harmonization at supra-national level • Lack of damage details • Reporting on losses may be mixed with post-disaster expenditures
• Missing events
• Lack of any measure of cost
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