weather cycles: what’s in it for insurers? · temperatures in the equatorial pacific ocean: 1....
TRANSCRIPT
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Weather Cycles: What’s in it for Insurers?
Prepared by Tim Andrews, Sean West and Kamal Pun
This presentation has been prepared for the Actuaries Institute 2012 General Insurance Seminar.
The Institute Council wishes it to be understood that opinions put forward herein are not necessarily those of the Institute and the Council is not responsible for those opinions.
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Presentation Outline
The Link Between ENSO
and Rainfall
The Link Between Rainfall
and Insurance Costs
The Link Between ENSO and Insurance
Costs
Is ENSO Predictable?
If Yes, What Can We Do With the
Predictions?
![Page 3: Weather Cycles: What’s in it for Insurers? · temperatures in the equatorial Pacific Ocean: 1. Dynamic Models • Physical equations representing ocean and atmospheric behaviour](https://reader033.vdocuments.mx/reader033/viewer/2022060309/5f0a59577e708231d42b3560/html5/thumbnails/3.jpg)
Presentation Outline
The Link Between ENSO
and Rainfall
The Link Between Rainfall
and Insurance Costs
The Link Between ENSO and Insurance
Costs
Is ENSO Predictable?
If Yes, What Can We Do With the
Predictions?
![Page 4: Weather Cycles: What’s in it for Insurers? · temperatures in the equatorial Pacific Ocean: 1. Dynamic Models • Physical equations representing ocean and atmospheric behaviour](https://reader033.vdocuments.mx/reader033/viewer/2022060309/5f0a59577e708231d42b3560/html5/thumbnails/4.jpg)
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SOI I
ndex
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all (
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Total Rainfall (October to March) SOI Index Series1
The Link Between ENSO and Rainfall
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The Link Between ENSO and Rainfall
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El Nino Neutral La Nina Strong La Nina
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Average Rainfall (October to March) Average all years
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Presentation Outline
The Link Between ENSO
and Rainfall
The Link Between Rainfall
and Insurance Costs
The Link Between ENSO and Insurance
Costs
Is ENSO Predictable?
If Yes, What Can We Do With the
Predictions?
![Page 7: Weather Cycles: What’s in it for Insurers? · temperatures in the equatorial Pacific Ocean: 1. Dynamic Models • Physical equations representing ocean and atmospheric behaviour](https://reader033.vdocuments.mx/reader033/viewer/2022060309/5f0a59577e708231d42b3560/html5/thumbnails/7.jpg)
The Link Between Rainfall and Insurance Claims
Home - Brisbane
GIOSydney & Brisbane Regions (2010-11)
Home & Motor Cost Per Policy Relative to No Rain
Home - Sydney Motor - Sydney
0%
200%
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1200%
Nil 0-1mm 1-10mm 10-20mm 20-30mm 30mm+ No Rain Rain
CPP
Rela
tivi
ty to
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Rain
Daily Rainfall Intensity
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Nil 0-1mm 1-10mm 10-20mm 20-30mm 30mm+ No Rain Rain
CPP
Rela
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Rain
Daily Rainfall Intensity
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Nil 0-1mm 1-10mm 10-20mm 20mm+ No Rain Rain
CPP
Rela
tivi
ty to
No
Rain
Daily Rainfall Intensity
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Presentation Outline
The Link Between ENSO
and Rainfall
The Link Between Rainfall
and Insurance Costs
The Link Between ENSO and Insurance
Costs
Is ENSO Predictable?
If Yes, What Can We Do With the
Predictions?
![Page 9: Weather Cycles: What’s in it for Insurers? · temperatures in the equatorial Pacific Ocean: 1. Dynamic Models • Physical equations representing ocean and atmospheric behaviour](https://reader033.vdocuments.mx/reader033/viewer/2022060309/5f0a59577e708231d42b3560/html5/thumbnails/9.jpg)
Influence of ENSO on Storms - Brisbane
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El Nino Neutral La Nina Strong LaNina
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Brisbane Flood History
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Floo
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River Peak vs SOI
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Weather Cycle vs Annual Industry Costs (ICA Data)
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El Nino Neutral La Nina Strong La Nina
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ears
in W
eath
er P
hase
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rage
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ual C
ost R
elat
ive
to A
vera
ge
Relativity Average Number of Years in Weather Phase
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Presentation Outline
The Link Between ENSO
and Rainfall
The Link Between Rainfall
and Insurance Costs
The Link Between ENSO and Insurance
Costs
Is ENSO Predictable?
If Yes, What Can We Do With the
Predictions?
![Page 13: Weather Cycles: What’s in it for Insurers? · temperatures in the equatorial Pacific Ocean: 1. Dynamic Models • Physical equations representing ocean and atmospheric behaviour](https://reader033.vdocuments.mx/reader033/viewer/2022060309/5f0a59577e708231d42b3560/html5/thumbnails/13.jpg)
• Two types of models available to predict sea-surface temperatures in the equatorial Pacific Ocean:
1. Dynamic Models • Physical equations representing ocean and atmospheric
behaviour used to determine future conditions • Computationally intensive
2. Statistical Models: • Rely on large volumes (30-50 years) of past observations
to predict the future • Simpler and cheaper to implement
ENSO Predictions: Types of Models
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Red = Actual
Blue = Forecast (6 months prior)
A Particular Dynamic Model: Lamont-Doherty (LDE05)
Source: “Predictability of El Nino over the past 148 years” (Cane 2004)
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A Particular Dynamic Model: Lamont-Doherty (LDE05)
Source: “Predictability of El Nino over the past 148 years” (Cane 2004)
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Dynamic Model Predictions • “Ensemble” models tend to be the best performers • Could be improved further by excluding certain weaker
models from the ensemble • 86% correlation at 6 months lead time
Source: Springer-Verlag 2008
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Dec-07 Apr-08 Aug-08 Dec-08 Apr-09 Aug-09 Dec-09 Apr-10 Aug-10 Dec-10 Apr-11 Aug-11 Dec-11 Apr-12 Aug-12 Dec-12 Apr-13
IRI CS - 6 months ALL IRI CS - 6 months DYN POAMA - 6 months ESSIC Intermed Coupled Model
Japan Met Agency - 6 months Japan Frontier Coupled Actual NINO3.4
…How Accurately were the 2011/12 La Nina Events Predicted?
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Presentation Outline
The Link Between ENSO
and Rainfall
The Link Between Rainfall
and Insurance Costs
The Link Between ENSO and Insurance
Costs
Is ENSO Predictable?
If Yes, What Can We Do With the
Predictions?
![Page 19: Weather Cycles: What’s in it for Insurers? · temperatures in the equatorial Pacific Ocean: 1. Dynamic Models • Physical equations representing ocean and atmospheric behaviour](https://reader033.vdocuments.mx/reader033/viewer/2022060309/5f0a59577e708231d42b3560/html5/thumbnails/19.jpg)
What can we do with the Predictions?
• Assume we can perfectly predict the stage of the weather cycle months in advance…
• What is the benefit to insurers?
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A Simple Model
• Considered range of pricing strategies
• Only new business premiums adjusted in light of forecast; retention book untouched
• Measured the potential saving in weather loss ratio under each option
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A Simple Model: Key Assumptions Number of Years Simulated: 20Number of Simulations: 1,000Initial Number of Policies: 1,000
Base Premium: 400Base Weather CPP (October-March): 200Base Weather CPP (April-September) 120
Type of Weather CPP PremiumPeriod Oct-Mar Apr-Sep Relativity OutlookStrong La Nina 10% 0% 200% Very BadLa Nina 15% 0% 130% BadNeutral 50% 100% 100% AverageEl Nino 25% 0% 90% Good
Probability
Premium Retention New BusinessOutlook Rate (p.a.) Growth (p.a.) Renewal New BusinessVery Bad 85% 10% 400 500Bad 85% 15% 400 450Average 85% 25% 400 400Good 85% 30% 400 350
Premium Rate ($)
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Cycle Prediction: Strategies Considered
1. Do nothing – base strategy
2. Reactive – change rates based on previous period
3. 6 month forecast – change rates based on the stage of the
cycle 6 months in advance
4. 6 month forecast with 2 year outlook – if there is a Strong
La Nina within the next 2 years, change the rates now, otherwise
maintain 6 month strategy
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0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032
Num
ber o
f Pol
icie
s
Do Nothing
Reactive
6 months
6 months, 2 year outlook
A Sample Run
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032
Strong La Nina La Nina Neutral El Nino Series5
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Strategy Avg pol growth (p.a.) Average LRDo Nothing 10.25% 42.67%Reactive 9.27% 42.88%6 months 9.27% 42.67%6 months, 2 yr outlook 7.05% 42.48%
Results Across all Simulations:
“Best” strategy Loss Ratio
Improvement = 0.19%
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Premium Retention New BusinessOutlook Rate (p.a.) Growth (p.a.) Renewal New BusinessVery Bad 82% 10% 500 500Bad 84% 15% 450 450Average 85% 25% 400 400Good 87% 30% 350 350
Premium Rate ($)
A Simple Model: Alternate Assumptions
Number of Years Simulated: 20Number of Simulations: 1,000Initial Number of Policies: 1,000
Base Premium: 400Base Weather CPP (October-March): 200Base Weather CPP (April-September) 120
Type of Weather CPP PremiumPeriod Oct-Mar Apr-Sep Relativity OutlookStrong La Nina 10% 0% 200% Very BadLa Nina 15% 0% 130% BadNeutral 50% 100% 100% AverageEl Nino 25% 0% 90% Good
Probability
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Strategy Avg pol growth (p.a.) Average LRDo Nothing 10.25% 42.63%Reactive 9.31% 42.66%6 months 9.31% 42.41%6 months, 2 yr outlook 6.63% 40.89%
Results Across all Simulations:
“Best” strategy Loss Ratio
Improvement = 1.74%
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Conclusions
• ENSO does affect weather costs
• It is predictable to some extent
• Insurers need to be clever to achieve a significant impact on profit
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Any Other Potential Uses for ENSO Predictions?
• Budgeting
• Monitoring
• Normalising historical results