1. 1. Conceptualizing Tail Risk: A Reinsurers Perspective Gabor Jaimes - GAJA Consulting Aresty Instiute of Executive Education The Wharton School of the University of Pennsylvania RMA/Wharton Advanced Risk Management Program 2015 Philadelphia, 15 July 2015
2. 2. Outline The reinsurance business model Assessing and managing tail risk Group discussion
3. 3. The Reinsurance Industry 300+ reinsurers worldwide Domiciles concentrated in London, Zurich, Bermuda, Germany, Singapore Munich Re, Swiss Re, Hannover Re, SCOR, Lloyds of London, Partner Re, General Re/BH, XL- Catlin Re, China Re, Korean Re, GIC Re, Reinsurance Group of America, Transatlantic Re, Everest Re, Renaissance Re, Platinum Re, White Mountain Re, Aspen Re, Peak Re, Top Layer Re, Samsung Re,
4. 4. The reinsurance business model Absorb tail risk from insurance companies Assess tail risk adequately Manage exposures thoroughly Identify new/emerging risks early Diversify across regions, products, risk types Minimize none-core business related risks
5. 5. The reinsurance business model Hurricane Katrina (2005)
6. 6. Japan Earthquake (2011) Superstorm Sandy (2012) 9-11 (2001) The reinsurance business model
7. 7. Christchurch Earthquakes (2010-2011) Thailand Flood (2011) Deep Water Horizon (2010) Indian Ocean Earthquake (2004) Northridge Earthquake (1994) Hurricane Andrew (1992) Hurricane Ike (2008) Alberta Floods (2013) The reinsurance business model
8. 8. Event Year Insured Loss Victims Hurricane Katrina (US) 2005 79 bn 1,836 Tohoku Earthquake (JP) 2011 37 bn 18,520 Hurricane Sandy (US) 2012 36 bn 237 Hurricane Andrew (US) 1992 27 bn 43 Terror Attacks / 9-11 (US) 2001 25 bn 2,982 Northridge Earthquake (US) 1994 22 bn 61 Hurricane Ike (US) 2008 22 bn 136 Christchurch Earthquake (NZ) 2011 17 bn 181 Hurricane Ivan (US) 2004 16 bn 119 Thailand Floods (TH) 2011 16 bn 815 Hurricane Wilma (US) 2005 15 bn 35 Hurricane Rita (US) 2005 12 bn 34 Largest Insured Cat Events Source: Swiss Re Sigma 2015
9. 9. Event Year Insured Loss Victims Cyclone Bangladesh 1970 nil 300,000 Beijing Earthquake 1976 nil 255,000 Haiti Earthquake 2010 0.1 bn 225,600 Indonesia Earthquake 2004 2.5 bn 220,000 Cyclone Nargis (Myanmar) 2008 nil 138,300 Cyclone Gorky (Bangladesh) 1991 nil 138,000 Sichuan Earthquake (China) 2008 0.4 bn 87,500 Pakistan Earthquake 2005 nil 74,300 Peru Earthquake 1970 nil 66,000 Russia Heat Wave 2010 nil 55,600 Iran Earthquake 1990 0.2 bn 40,000 Central Europe Heat Wave 2003 1.6 bn 35,000 Deadliest Cat Events Source: Swiss Re Sigma 2015
10. 10. Outline The reinsurance business model Assessing and managing tail risk Group discussion
11. 11. 1 From historical events to a probabilistic event set Hurricane Katrinas historical track and the probabilistic events as generated in the model Source Swiss Res Cat Perils (Dr Marc Wueest)
12. 12. 1 Northern Atlantic JunNov (~11 storms/year)Eastern Pacific May Nov (~16 storms/year) Northwestern Pacific All year-round (~27 storms/year) North Indian Ocean Apr-May & Oct-Nov (~5 storms/year) South-West Indian Ocean Nov-Apr (~21 storms/year) Australia South-West Pacific Nov-Apr (~9 storms/year) From historical events to a probabilistic event set globally
13. 13. Murphys Law Photo: Beverly Hills Cop => Murphys Law
14. 14. Near Misses
15. 15. Emerging Risks
16. 16. 1 High Prolonged power blackout Run-away inflation and surging bond yields Big data Endocrine disrupting chemicals Unforeseen consequences of electromagnetic fields Unforeseen consequences of nanotechnology Medium Cyber attacks Supply chain vulnerability Underestimated nat cat exposure Changing communication patterns Toxic substances and workplace safety Changing lifestyle Emerging infectious diseases Unresolved sovereign debt crisis Underinvestment in critical infrastructure Legal actions drive changing claims patterns Personal damage compensation in Europe Regulatory fragmentation and extra- territoriality concerns Contingent reputational risks Drug resistance The future of medicine Global talent crunch New forms of mobility Low Social unrest Do-it-yourself galore A risky harvest The robots among us 1-3 years 4-10 years > 10 years Potential impact Time frame Insights are grouped by insurance business area based on expected impact: Property Casualty Life & Health Financial Markets Claims Operations Emerging Risks Source: Swiss Re
17. 17. Integrated view of the risk to allow for proper modeling of (tail) dependences
18. 18. Assessing (and managing) tail risk 1. Analyze & understand historical events 2. Generate plausible future events 3. Consider Murphys Law 4. Consider near misses 5. Anticipate emerging risks 6. Anticipate tail dependences 7. Use a risk measure that considers the tail (Exp. Shortfall) 8. Hedging: retrocession, cat bonds, cat swaps 9. Fully integrated risk aggregation & capital allocation 10. Risk-adjusted Return on Capital by transaction
19. 19. Using a risk measure that gives sufficient weight to tail events Source: Swiss Re Annual Report 2014
20. 20. Integrated view of the risk to allow for proper modeling of (tail) dependences
21. 21. Consistent use of a risk measure across all modeled risk categories Source: Swiss Re Annual Report 2014 also for hedging
22. 22. Assessing and managing tail risk 1. Analyze & understand historical events 2. Generate plausible future events from the historical set 3. Consider Murphys Law 4. Consider near misses 5. Anticipate emerging risks 6. Anticipate tail dependences 7. Consistent use of a risk measure that considers the tail 8. Fully integrated risk aggregation & capital allocation 9. Risk-adjusted return on capital by transaction 10. Hedging: retrocession, cat bonds, cat swaps
23. 23. Outline The reinsurance business model Assessing and managing tail risk Group discussion
24. 24. Thank You!! Gabor Jaimes - GAJA Consulting gabor@gaja-consulting.com Aresty Instiute of Executive Education The Wharton School of the University of Pennsylvania RMA/Wharton Advanced Risk Management Program 2015