Lessons Learned from International Catastrophe Pools - Financing - - PowerPoint PPT Presentation

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ICRM SYMPOSIUM 2018 Lessons Learned from International Catastrophe Pools - Financing Asias Exposure to Extreme Weather Professor Shaun Wang Nanyang Business School 7 August 2018 | Grand Copthorne Waterfront Hotel Asias Economic

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SLIDE 1

Lessons Learned from International Catastrophe Pools - Financing Asia’s Exposure to Extreme Weather

Professor Shaun Wang Nanyang Business School

7 August 2018 | Grand Copthorne Waterfront Hotel

ICRM SYMPOSIUM 2018

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SLIDE 2

Asia’s Economic Development in the next 5 - 20 years

  • With a population of 4.5 billion people, Asia is
  • n track for unprecedented economic

development

  • The rapid population growth and urbanisation

in Asia are exacerbating an already significant insurance protection gap in the region.

  • Over the past 40 years, according to Swiss Re,
  • nly 5% of economic losses from all flood

disasters in Emerging Asia were insured.

2018-Aug-07 Shaun.Wang@ntu.edu.sg 2

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SLIDE 3

ASEAN’s infrastructure investment

  • Finance Minister Mr. Heng Swee Keat, in his

April 5, 2018 keynote speech at the 8th World Bank-Singapore Infrastructure Finance Summit, pointed out that ASEAN’s infrastructure investment needs will total US$2.8 trillion between 2016 and 2030, or about US$184 billion annually, which would require significant private capital to fill in the shortage of public funding.

2018-Aug-07 Shaun.Wang@ntu.edu.sg 3

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SLIDE 4

Infrastructure Financing & Insurance Protection Gap

2018-Aug-07 Shaun.Wang@ntu.edu.sg 4

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SLIDE 5

BENEFIT OF RISK POOLING (WORLD BANK, 2017)

2018-Aug-07 Shaun.Wang@ntu.edu.sg 5

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SLIDE 6

CAT Pool: African Risk Capacity

  • In 2012, African Risk Capacity (ARC) was

established as a Specialized Agency of the African Union to help member states improve their capacities to better prepare for and respond to extreme weather events and natural disasters, therefore protecting the food security of their vulnerable populations.

2018-Aug-07 Shaun.Wang@ntu.edu.sg 6

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SLIDE 7

CAT Pool: ARC (year 1 launching)

  • Since 2014, ARC Ltd. enables participating African

governments to insure themselves against drought and respond rapidly when their citizens experience harvest failure.

  • The inaugural risk pool, which covered the

2014/2015 rainfall seasons, consisted of the 4 countries Kenya, Mauritania, Niger and Senegal.

  • Innovation: insurance pay-out will be based on

AfricaRiskView (ARV) model output

2018-Aug-07 Shaun.Wang@ntu.edu.sg 7

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SLIDE 8

Hypothetical Correlation Matrix

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Country- Peril 1 Country- Peril 2 Country- Peril 3 Country- Peril 4 Country- Peril 5 Country- Peril 6 Country- Peril 7 Country- Peril 8 Country- Peril 9 Country- Peril 10 Country- Peril 11 Country- Peril 12 Country- Peril 13 Country- Peril 14 Country- Peril 1

1.00 0.15 0.00

  • 0.13

0.16

  • 0.13
  • 0.01
  • 0.15
  • 0.14

0.54 0.30 0.32 0.17 0.00

Country- Peril 2

0.15 1.00

  • 0.01
  • 0.13
  • 0.12
  • 0.01

0.02

  • 0.15
  • 0.01

0.32 0.29 0.46

  • 0.01

0.00

Country- Peril 3

0.00

  • 0.01

1.00 0.15 0.14 0.51 0.00

  • 0.16
  • 0.01
  • 0.15

0.01

  • 0.01

0.14

  • 0.15

Country- Peril 4

  • 0.13
  • 0.13

0.15 1.00 0.01 0.14

  • 0.26

0.01 0.16

  • 0.15
  • 0.13
  • 0.14
  • 0.01

0.02

Country- Peril 5

0.16

  • 0.12

0.14 0.01 1.00 0.15 0.02

  • 0.15

0.01

  • 0.01

0.47 0.15 0.71 0.16

Country- Peril 6

  • 0.13
  • 0.01

0.51 0.14 0.15 1.00 0.14

  • 0.01

0.15

  • 0.14
  • 0.01
  • 0.13

0.00 0.15

Country- Peril 7

  • 0.01

0.02 0.00

  • 0.26

0.02 0.14 1.00 0.15 0.14

  • 0.17
  • 0.13
  • 0.01

0.01

  • 0.13

Country- Peril 8

  • 0.15
  • 0.15
  • 0.16

0.01

  • 0.15
  • 0.01

0.15 1.00 0.34

  • 0.15
  • 0.15
  • 0.15
  • 0.16

0.34

Country- Peril 9

  • 0.14
  • 0.01
  • 0.01

0.16 0.01 0.15 0.14 0.34 1.00

  • 0.14

0.00 0.01 0.01 0.32

Country- Peril 10

0.54 0.32

  • 0.15
  • 0.15
  • 0.01
  • 0.14
  • 0.17
  • 0.15
  • 0.14

1.00 0.32 0.16 0.00

  • 0.01

Country- Peril 11

0.30 0.29 0.01

  • 0.13

0.47

  • 0.01
  • 0.13
  • 0.15

0.00 0.32 1.00 0.46 0.49 0.02

Country- Peril 12

0.32 0.46

  • 0.01
  • 0.14

0.15

  • 0.13
  • 0.01
  • 0.15

0.01 0.16 0.46 1.00 0.17

  • 0.01

Country- Peril 13

0.17

  • 0.01

0.14

  • 0.01

0.71 0.00 0.01

  • 0.16

0.01 0.00 0.49 0.17 1.00 0.15

Country- Peril 14

0.00 0.00

  • 0.15

0.02 0.16 0.15

  • 0.13

0.34 0.32

  • 0.01

0.02

  • 0.01

0.15 1.00

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CAT Pool: ARC (year 2, in and out)

  • For the 2015/2016 seasons, the three

additional countries of Gambia, Malawi and Mali joined ARC, bringing the total number of risk pool countries to seven.

  • For the third risk pool in 2016/2017, Burkina

Faso joined the pool while Kenya and Malawi left, leaving a total of six countries in the pool. The government of Kenya has citied political pressure to explain expenditures.

2018-Aug-07 Shaun.Wang@ntu.edu.sg 9

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Benchmark Risk Premium

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Layer (a, a+h] Annual Average Expected Loss Benchmark Reinsurance Premium Risk Load % Benchmark Reinsurance Premium Risk Load % 10M XS 60M 761,108 $ 1,852,994 $ 143% 1,077,333 $ 42% 10M XS 70M 427,442 $ 1,297,440 $ 204% 697,638 $ 63% 10M XS 80M 222,575 $ 895,395 $ 302% 441,623 $ 98% 10M XS 90M 107,803 $ 616,570 $ 472% 276,469 $ 156% 10M XS 100M 53,950 $ 447,624 $ 730% 183,368 $ 240% 10M XS 110M 23,289 $ 313,642 $ 1247% 115,297 $ 395% 10M XS 120M 9,438 $ 224,046 $ 2274% 73,507 $ 679% 10M XS 130M 3,201 $ 155,061 $ 4744% 44,540 $ 1291% 10M XS 140M 1,500 $ 124,731 $ 8215% 32,819 $ 2088% 10M XS 150M 1,389 $ 121,718 $ 8661% 31,731 $ 2184% 10M XS 160M 473 $ 61,024 $ 12810% 14,920 $ 3056% Combined 110M XS 60M 1,612,167 $ $ 6,110,246 $ 279% 279% 2,989,243 $ 85% 85% Hard Market; Wang Transform (lambda=0.45, def=5) Soft Market; Wang Transform (lambda=0.1, def=9)

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CAT Pool: ARC (testing year 3)

  • In 2016, ARC Ltd. paid out US$ 8.1 million to

Malawi in support of approximately 810,000 people impacted by a drought.

  • Initially, Malawi’s parametric drought insurance

policy did not trigger a pay-out, because ARC Ltd.’s AfricaRiskView (ARV) model indicated a low number of people affected by the drought.

  • However, the Government of Malawi estimated a

much higher number of people impacted by the drought.

2018-Aug-07 Shaun.Wang@ntu.edu.sg 11

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CAT Pool: ARC (testing year 3)

  • It turned out that farmers had shifted to planting

maize with a 90-day growing period, compared to the maize variety with a growing period of 120- 140 days as assumed in the customisation of Malawi’s model.

  • The rainfall pattern in 2015/16 was particularly

unfavourable to the shorter cycle maize.

  • ARC re-calibrated the AfricaRiskView model to

correct this crop assumption, resulting in a model

  • utcome of US$ 8.1 million pay-out under the

revised policy to the Government of Malawi.

2018-Aug-07 Shaun.Wang@ntu.edu.sg 12

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Lessons Learned from the CAT Pool - Africa Risk Capacity

  • Affordability Gap
  • Parametric trigger and “basis” risk
  • Need to measure “expectation gap” to avoid

disappointment

  • Communicate about “Basis risk” in parametric

modeling

2018-Aug-07 Shaun.Wang@ntu.edu.sg 13

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SLIDE 14

Florida Hurricane Catastrophe Fund (formation)

  • With Hurricane Andrew in 1992 as a catalyst

for its establishment, the initial motivation behind the creation of the FHCF was provision

  • f catastrophe reinsurance cover.
  • Right from the beginning, the objective of the

FHCF was to keep premiums affordable across the board and to have policyholders in low- risk areas cross-subsidize those at higher risk.

2018-Aug-07 Shaun.Wang@ntu.edu.sg 14

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FHCF in 2004-5 (testing times)

  • In 2004 and 2005, Florida was hit by four and three

hurricanes, respectively. As of December 31, 2015, the FHCF had paid over US$ 9.3 billion in loss reimbursements to its participating insurers.

  • The losses associated with the 2005 hurricanes

produced pay-outs that exceeded the FHCF’s available

  • cash. To address the cash shortfall, FHCF issued US$

1.35 billion in tax-exempt post-event revenue bonds with a maturity date of 2012. This was the first time that the FHCF had to issue bonds.

2018-Aug-07 Shaun.Wang@ntu.edu.sg 15

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FHCF in 2016 (proven success)

  • In 2016, the maximum statutory single season

capacity of the FHCF was US$ 17 billion. With an accumulated cash balance of US$ 13.8 billion.

  • The FHCF has low operating expenses and a

relatively small staff of 13 full time employees.

2018-Aug-07 Shaun.Wang@ntu.edu.sg 16

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SLIDE 17

Lessons Learned from FHCF

  • Statewide political support – essential for

Florida’s economic prosperity

  • Political homogeneity (single state, rather

than multi-state pool with AL and LA)

  • Lower capital requirements than commercial

insurers

  • Resulted in significant savings for residents
  • Ability to impose post-event assessment (time

diversification) added operational flexibility

2018-Aug-07 Shaun.Wang@ntu.edu.sg 17

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Research Findings of the NTU-MAS Cyber Risk Management Project

Tripartite Collaboration

  • Monetary Authority of

Singapore

  • Cyber Security Agency
  • Nanyang Technological

University

  • SCOR； Aon； MSIG； Lloyd’s；

TransRe；

  • (Geneva Association; Verizon)

Launched in May 2016 (to be completed in 2019)

18 2018-Aug-07 Shaun.Wang@ntu.edu.sg

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SLIDE 19

Attack surface “vulnerability”

Knowledge Set

a relative concept: 1) WHO 2) What

  • actually knows
  • should know but

not know

  • Unknown unknown

2018-Aug-07 Shaun.Wang@ntu.edu.sg 19

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Security Investment: 1) “y” in knowledge, 2) “z” in risk reduction

2018-Aug-07 Shaun.Wang@ntu.edu.sg 20

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Optimal Security Investments

  • Invest amount “y” in knowledge, and amount “z” in risk

reduction measures, the data breach probability 𝑤 𝑨 𝑧 = 𝑤(𝑨0|𝑧0)

( 𝑧 𝑧0)( 𝑨 𝑨0)

  • Optimal investment is proportional:

𝒛∗ 𝒜∗ = 𝜷 𝜸

Reference: Wang, Shaun, “Knowledge Set of Attack Surface and Cybersecurity Rating for Firms in a Supply Chain” (November 3, 2017). Available at SSRN: https://ssrn.com/abstract=3064533

2018-Aug-07 Shaun.Wang@ntu.edu.sg 21

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Economics of Catastrophe Pool for Asian Countries

  • Risk Knowledge and Risk

Awareness

  • Risk Reduction

(adaptation, prevention and emergency response)

  • Risk Pooling and Transfer

2018-Aug-07 Shaun.Wang@ntu.edu.sg 22

Asset Growth Income Generation Wealth Creation Risk Reduction Risk Transfer Wealth protection

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SLIDE 23

Use CAT Pool as Catalyst for Achieving Multiple Economic Development Goals

  • 1. Adapting to Climate Change
  • 2. Assisting Infrastructure Development
  • 3. Reducing Poverty
  • The Role of International Aid (G7, Development banks)

➢To create the right incentives on the recipients ➢To embed “financial aid” with “catastrophe pooling experience”

  • To work with finance ministries and the global

insurance industry

2018-Aug-07 Shaun.Wang@ntu.edu.sg 23

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The Speaker Acknowledges Collaborators and Supporters of Catastrophe Risk Pool Research

Contact: Shaun.Wang@ntu.eud.sg