Weather Cycles: What’s in it for Insurers? · temperatures in the equatorial Pacific Ocean: 1....

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.

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?

-25

-20

-15

-10

-5

0

5

10

15

20

25

1901

1903

1905

1907

1909

1911

1913

1915

1917

1919

1921

1923

1925

1927

1929

1931

1933

1935

1937

1939

1941

1943

1945

1947

1949

1951

1953

1955

1957

1959

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

0

100

200

300

400

500

600

700

SOI I

ndex

Financial Year

Tota

l R

ainf

all (

mm

)

Total Rainfall (October to March) SOI Index Series1

The Link Between ENSO and Rainfall

The Link Between ENSO and Rainfall

0

100

200

300

400

500

600

El Nino Neutral La Nina Strong La Nina

Ave

rage

Rai

nfal

l Oct

ober

to M

arch

(mm

)

Average Rainfall (October to March) Average all years



and Rainfall


and Insurance Costs


Costs



Predictions?

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%

400%

600%

800%

1000%

1200%

Nil 0-1mm 1-10mm 10-20mm 20-30mm 30mm+ No Rain Rain

CPP

Rela

tivi

ty to

No

Rain

Daily Rainfall Intensity

0%

100%

200%

300%

400%

500%

600%

700%

800%

Nil 0-1mm 1-10mm 10-20mm 20-30mm 30mm+ No Rain Rain

CPP

Rela

tivi

ty to

No

Rain


90%

95%

100%

105%

110%

115%

120%

125%

Nil 0-1mm 1-10mm 10-20mm 20mm+ No Rain Rain

CPP

Rela

tivi

ty to

No

Rain




and Rainfall


and Insurance Costs


Costs



Predictions?

Influence of ENSO on Storms - Brisbane

0

1

2

3

4

5

6

-30 -20 -10 0 10 20 30

Stor

m S

core

SOI

StrongLa Nina

Weak La Nina

1

2

3

4

5

El Nino Neutral La Nina Strong LaNina

Brisbane Flood History

0

1

2

3

4

5

6

7

8

9

10

1877 1886 1895 1904 1913 1922 1931 1940 1949 1958 1967 1976 1985 1994 2003 2011

Floo

d Le

vel (

met

res)

Year ending 30 June

Peak SOI > 8 SOI > 14

River Peak vs SOI

Weather Cycle vs Annual Industry Costs (ICA Data)

0

5

10

15

20

25

30

0%

50%

100%

150%

200%

250%

El Nino Neutral La Nina Strong La Nina

Num

ber

of Y

ears

in W

eath

er P

hase

Ave

rage

Ann

ual C

ost R

elat

ive

to A

vera

ge

Relativity Average Number of Years in Weather Phase



and Rainfall


and Insurance Costs


Costs



Predictions?

• 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

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)

A Particular Dynamic Model: Lamont-Doherty (LDE05)

Source: “Predictability of El Nino over the past 148 years” (Cane 2004)

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

-3

-2

-1

0

1

2

3

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?



and Rainfall


and Insurance Costs


Costs



Predictions?

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?

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

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 ($)

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

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

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%

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

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%

Conclusions

• ENSO does affect weather costs

• It is predictable to some extent

• Insurers need to be clever to achieve a significant impact on profit

Any Other Potential Uses for ENSO Predictions?

• Budgeting

• Monitoring

• Normalising historical results

Weather Cycles: What’s in it for Insurers? · temperatures in the equatorial Pacific Ocean: 1....

Documents

Transcript of Weather Cycles: What’s in it for Insurers? · temperatures in the equatorial Pacific Ocean: 1....