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

ICRM SYMPOSIUM 2018 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

Asia’s Economic Development in the next 5 - 20 years • With a population of 4.5 billion people, Asia is on 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, only 5% of economic losses from all flood disasters in Emerging Asia were insured. 2018-Aug-07 Shaun.Wang@ntu.edu.sg 2

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

Infrastructure Financing & Insurance Protection Gap 2018-Aug-07 Shaun.Wang@ntu.edu.sg 4

BENEFIT OF RISK POOLING (WORLD BANK, 2017) 2018-Aug-07 Shaun.Wang@ntu.edu.sg 5

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

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

Hypothetical Correlation Matrix Country- Country- Country- Country- Country- Country- Country- Country- Country- Country- Country- Country- Country- Country- Peril 1 Peril 2 Peril 3 Peril 4 Peril 5 Peril 6 Peril 7 Peril 8 Peril 9 Peril 10 Peril 11 Peril 12 Peril 13 Peril 14 Country- 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 Peril 1 Country- 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 Peril 2 Country- 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 Peril 3 Country- -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 Peril 4 Country- 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 Peril 5 Country- -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 Peril 6 Country- -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 Peril 7 Country- -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 Peril 8 Country- -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 Peril 9 Country- 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 Peril 10 Country- 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 Peril 11 Country- 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 Peril 12 Country- 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 Peril 13 Country- 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 Peril 14 2018-Aug-07 Shaun.Wang@ntu.edu.sg 8

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

Benchmark Risk Premium Hard Market; Wang Soft Market; Wang Transform (lambda=0.45, Transform (lambda=0.1, def=5) def=9) Annual Benchmark Benchmark Risk Load Risk Load Layer (a, a+h] Average Reinsurance Reinsurance % % Expected Loss Premium Premium 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% 2018-Aug-07 Shaun.Wang@ntu.edu.sg 10

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

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 outcome 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

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

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 of 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

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

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

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

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

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

Security Investment: 1) “y” in knowledge, 2) “z” in risk reduction 2018-Aug-07 Shaun.Wang@ntu.edu.sg 20