Rowan Douglas-National Tornado Summit 2017 - Rowan Douglas ... · WRN: NOAA/Oklahoma University: National Severe Storm Laboratory 10 High resolution hail distribution: 2000-2011

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National Tornado Summit 2017

From Tornado Alley to Wall Street:

Storm Science & Finance

© 2017 Willis Towers Watson. All rights reserved.

Rowan Douglas CBE

CEO Capital, Science & Policy Practice, Willis Towers Watson

Chairman, Willis Research Network

Tornadoes in Europe

2© 2017 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.

Synthesis of the Observational Datasets, Antonescu et al, July 2016

Since:

Germany 855

Ireland 1054

UK 1091

Italy 1410

France 1680

Recent Severe Hail Losses in Europe


An overshadowed peril?

2 largest German insurance industry losses from hailstorms

� July/Aug 2013: Manni, Andreas, and Franz hailstorms

� Germany and France

� Insured loss: EUR 3.5bn (USD 4bn)

Largest DE loss since Munich 1984 event

� 2014: Ela convective storm was largest European insured loss of the year

� Germany, France and Belgium

� Insured loss: EUR 1.75bn (USD 2.2bn)

� 2010 Prague, Czech Republic

� 2009: Wolfgang – Austria, Switzerland

� European Severe Weather Database (ESWD)

� https://www.eswd.eu/

� National weather service and crowd-sourced data

14 cm

The Output that Transformed a Market


Loss Exceedence Probability Curve

Exceedance Probability (EP) Curve - Occurrence

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

0 50 100 150 200 250 300 350 400

Loss Amount ($ millions)

Ex

ce

ed

an

ce

Pro

ba

bilit

y

0

50

100

150

200

250

300

350

10 20 50 100 250 500 1,000

Estimated Return Period

Lo

ss

Am

ou

nt

($m

illi

on

s)

Source AIR Worldwide, with grateful thanks to Ashish Jain

Risk and capital management: stress tests to quantify potential impact of risk exposures.

5Source: Swiss Re Annual Report 2013 with grateful thanks to Esther Baur, Swiss Re

Example: Swiss Re Annual Report

Managing Extremes: The history of experience is not an understanding current risk

Katrina2005

Northridge 1994

20

50

10

Lothar1999

Storm Europe

45

Hurricane US + Caribbean

200

FHC

F

EarthquakeJapan

85

55

Earthquake California

Historic insured loss (indexed to 2012)

Modelled 200 year insured loss

Insurance loss scenarios [USD bn]

FHCF: Florida Hurricane Catastrophe Fund

35

Tohoku2011

JER: Japan Earthquake Reinsurance SchemeState-run schemes

JER

NFIP

NFIP: National Flood Insurance Program

Source: Swiss Re with grateful thanks to Ivo Menzinger, Swiss Re

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eXTREMETM Tornado - A Maximum Foreseeable Loss Model

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What if Moore event occurs in Tulsa?

© 2017 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.

� Exhaustive set of high-severity events, centred specifically on a portfolio

� ~1 million events can be built specifically for a company’s portfolio (no coverage gaps)

� Tornado, hail, or wind swaths independently or together

� Idealised storm swaths

� Historical storm swaths for “what-if” scenarios

Hail Risk Climatology


Pan-European Coverage

From hazard to loss

� 1st hail risk assessment to cover southern, central and eastern Europe with a contiguous hazard model

� ~38,000 individual ‘overshooting top’ events selected, constrained by hail occurrence observation (ESWD)

� Stochastic simulation of 2,500 years of hail activity (~ 630,000 events)

� E.g. Austrian Client portfolio

1 in 200 year loss

Average annual loss

Historical scenarios

Wolfgang, 2009

1 in 90 year loss

Hail Risk: Australia

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Radar and OT data


Brisbane Hailstorm, 27th Nov, 2014

� 100,000 insurance claims

� 25% property, 75% motor

� Insured loss: AUD 1.1bn (USD 900m)

Event Set Construction

� Event footprint defined by 3D radar

� NASA overshooting top (OT) datasets filtered to

match radar

� National coverage and longer time-series event

sets built from OT data

3D Doppler

Radar

Event footprint

with OT signature

WRN: NOAA/Oklahoma University: National Severe Storm Laboratory

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High resolution hail distribution: 2000-2011


� Severe hail days(>1”; 25.4mm) ,based on reanalysis of historical WSR-88D radar data

Source: Smith & Ortega, 2016

Deterministic Scenarios for Risk Communication

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Validation points on the loss exceedance curve


� Intuitive nature of deterministic scenarios helps communicate with senior managers, board of directors and other stakeholders

� Deterministic methods used for stress testing and developing risk management solutions

� Deterministic scenarios can be paired with stochastic models

� Useful in developing alternative views of risk

� Considering different sizes of events and “what if” analyses

� E.g. Tornado May 20th, 2013: Moore, OK

� Peak winds ~210 mph

� 24 fatalities

� USD 1.8bn insured loss

Previous event in 1999

36 fatalities

El Niño Southern Oscillation (ENSO)

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Opportunities for forecasting frequency tornado/hail shifts


Source: NOAA

� Naturally occurring involving ocean temperatures in the Pacific

� La Niña increases hurricane activity in the Atlantic due to reduced wind shear

� El Niño modifies the jet stream positioning over the continental U.S.,

which alters severe weather ingredients

� ENSO is believed to influence the large-scale environmental features that

produce severe thunderstorms

� Winter ENSO indices can be used to predict spring severe weather frequency

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Impact of ENSO on Severe Storm Activity

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Less tornado/hail in central US during El Niño


Source: Allen J. T., Tippett, M. K. & Sobel A. H. Nature Geoscience, March 2015

El Nino

La Nina

� Composite mean anomalies of spring (March, April, May) hail and tornadoes conditioned on spring ENSO state

WRN: Columbia University Tornado and Hail Forecasts

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Jan 2017: Comparison with NOAA Severe Weather Reports


Observed

Forecast

Tornado

Tornado

Hail

Hail

FEMA: Disaster Deductibles

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Promoting preparedness, mitigation and resilience?


12th Jan 2017: FEMA 2nd notice on “disaster deductible” proposal

� Recipients expend predetermined amount of own funds before receiving federal funding

Why?

� Federal spending on disasters should be reduced

� Promote risk-informed decision-making

� Reduce the costs of future events – more effective use of taxpayer resources

� Transparency of deductible requirement facilitates planning and budgeting

� Applies to Public Assistance (PA) funds only (not individual assistance)

� Repair, or replacement of public facilities or infrastructure

� Currently, federal share of PA funds “shall not be less than 75%” (Stafford Act, 1988)

Legislative changes required

� Credits available to reduce deductible requirement with demonstration of:

� Adopting and enforcing building codes

� Funding mitigation projects

� Investing in disaster relief, insurance, and emergency management programs

FEMA Proposed Disaster Deductibles

State

Year 1 Starting

Deductible (USD

mn)

‘‘Final’’ Adjusted

Deductible (USD

mn)

Potential

Deductible

Reduction

Oklahoma 5.3 3.3 37.1%

California 52.5 7.6 85.5%

Washington 9.5 8.9 6.0%

Disaster Deductibles: OK Not OK?

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Federal Register: Vol. 82, No. 8, Jan 12th 2017 / Proposed Rules


OK: has highest number (3) of annual average major disaster declarations

� USD 5.3m – 3.3m starting deductible (~state average potential mitigation discount of 39%)

� CA Highest credit potential: 2nd highest population (37.2m), 1.5 declarations p.a.

� WA Lowest credit potential: ~state average population (6.7m), 1.2 declarations p.a.

Disaster Declarations by State

Rank State

Population

(2010)

Annual

Average Major

Disaster

Declarations

1 Oklahoma 3,751,351 3.0

2 New York 19,378,102 2.5

3 Missouri 5,988,927 2.4

4 Iowa 3,046,355 2.3

5 Kansas 2,853,118 2.3

6 Nebraska 1,826,341 2.3

7 New Hampshire 1,316,470 2.2

8 South Dakota 814,180 2.2

9 Maine 1,328,361 2.0

10 North Dakota 672,591 2.0

… … … …

FEMA National Flood Insurance Program (NFIP)

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Reinsurance purchase


Jan 2017: FEMA obtains $1.042bn of reinsurance for NFIP

� Bolster claims-paying ability and reduce reliance on US Treasury borrowings

� 25 reinsurers on programme (e.g. Swiss Re, Munich Re, Liberty Mutual…)

� Carriers responsible for 26% of losses between USD 4-8bn incurred from a single event

� Occurrence probability of 17.2% chance of taking place during the year (5.8 years)

� Market tested in Sept 2016 with USD1m placement with 3 reinsurers

� US flood models:

� AIR Touchstone: 2015 (v3.1) – released in Oct 2014

� Corelogic RQE: 2016 (v16.1) – released in Sept 2015

� Models enable confidence in underwriting and flood risk management

Natural Disasters Impacting Sovereign Credit Ratings


Standard & Poor’s

Flood Tropical cyclone Earthquake

� Analysis based on sample of 48 countries

� Insurance mitigates the risk of potential downgrades

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Cities / Sub-state Regions


A scale for maximum climate risk impact and financial instability

Source: Lloyd’s / University of Cambridge Centre for Risk Studies, 2015With grateful thanks to Trevor Maynard, Lloyd’s

UN World Conference on Disaster Risk Reduction (WCDRR)


Sendai, Japan

March 2015

� Business, professional associations and private sector financial institutions, including financial regulators and accounting bodies… To integrate disaster risk management, including business continuity, into business models and practices via disaster risk-informed

investments... And engage in the development of normative frameworks and technical standards that incorporate disaster risk management UN Sendai Framework for Disaster Risk Reduction 2015-2030, paragraph 36(c)

Resilience to Extreme Weather


Royal Society Report, November 2014

The re/insurance sector has made considerable progress in

evaluating the risks posed by extreme weather.

These risks now need to be better

accounted for in the wider financial system, in order to inform

valuations and investment decisions and to incentivise

organisations to reduce their exposure. This could be done

through a requirement for public and private sector organisations to

report their financial exposure to extreme weather at a minimum of

1 in 100 (1%) per year risk levels.

Human Rights, Disaster Resilience & Financial Regulation


Emerging Duties of Care against Natural Hazards

As the UN Human Rights Council asserts:

“Natural hazards are not disasters in and of themselves. Whether or not they

become disasters depends on the exposure of a community, and its vulnerability and resilience, all factors that

can be addressed by human (including State) action. A failure (by governments and other actors) to take reasonable

preventative action to reduce exposure and vulnerability and to enhance resilience, as well as to provide mitigation,

is therefore a human rights issue” HRC, 2014a

Financial Stability Board (FSB)


Task Force on Climate-related Financial Disclosures (TCFD)

December 2016

� TCFD Appointed by Mark Carney

Chair of FSB

Governor Bank of England

Fmr Gov. Canadian Central Bank

� TCFD chaired by Michael Bloomberg� Members inc. Asset Managers, Corporates, Banks, Re/ins

� Company disclosures to promote more informed investment related to climate risks

� Physical Risk (including natural hazards / disasters

� Transition Risk (energy policy, new technologies etc.)

� Liability Risks

� Identify concentrations of climate-related risks

Willis Towers Watson Disclaimers

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source in the preparation of this analysis. Willis Towers Watson shall have no liability in connection with any results, including, without limitation, those arising from based upon or in connection with errors, omissions, inaccuracies, or inadequacies associated with the data or arising from, based upon or in connection with any methodologies used or applied by Willis Towers Watson in producing this analysis or

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There are many uncertainties inherent in this analysis including, but not limited to, issues such as limitations in the available data, reliance on client data and outside data sources, the underlying volatility of loss

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Rowan Douglas-National Tornado Summit 2017 - Rowan Douglas ... · WRN: NOAA/Oklahoma University: National Severe Storm Laboratory 10 High resolution hail distribution: 2000-2011