Modelling, Measuring and Managing of Extreme Risks

Allgemeines:Vorlesung -> mündliche oder schriftliche Prüfung (80 Prozent)Seminar -> ohne Prüfung, aber Vortrag (30 Prozent) und Ausarbeitung (50 Prozent)

• Bachelor-Studierende einen Vortrag und arbeiten ihn schriftlich aus, • Master- und Diplom-Studierende, erweiterte Seminararbeit.

Anwesenheitspflicht Mitarbeit (alle) (20 Prozent): Selbständiger Versuch der Berechnung der Beispiele.

Vortragender: Dr. Mag. Stefan Hochrainer-Stigler IIASA-International Institute for Applied Systems Analysis Laxenburg, Austria Website for ppt: http://user.iiasa.ac.at/~hochrain/KIT2013/

Zeiteinteilung:

Dienstag: 20.05:Teil I: 10:30-13:30: 3h -> 4 EHTeil I: 14.30-16:45: 2h.15 ->3 EHTeil II: 17.00-18.00: 1h->2 EH

Mittwoch: 21.05:Teil III: 8:00-9:30: 1h.30 ->2 EH 09:30-12:30: 3 -> 4 EHTeil VI: 13:30-15:00: 1.30 -> 2 EHTeil IV: 15:00-18:00: 3h -> 4 EH

Donnerstag: 22.05:Teil IV: Präsentationen: 8:30-14:45: 6h.15 -> 7 EH

Insgesamt: 28 EH

Überblick

Teil I: • 4 Stunden: Einführung, Motivation, Risiko, Nutzenfunktion, Risikoaversion, Prämien (Beispiele rechnen)• 1.5 Stunde : Arrow Lind Theorem, Ausnahmen, Diskussion, Katastrophen, Naturkatastrophen

Teil II: Risikoinstrumente, Naturkatastrophen, Extreme, Maßzahlen• 2.5 Stunde: Risiko öffentlicher Sektor etc. Einführung • 2.5 Stunde: Risikomanagement Methoden (Beispiel rechnen)• 1 Stunde: Versicherungslösungen für Katastrophen

Teil III:• 2.5 Stunde: Extremwertstatistik I + II• 2 Stunde: Katastrophenmodelle, Simulationsmethoden • 1 Stunde: Fiskalische Risikomatrix

Teil IV:• Spezialthemen• Anwendungsbeispiele • Aktuelle Anwendungs- und Forschungsgebiete• Abschliessende Diskussion

Überblick

MotivationExample Natural Disasters

• Only a few global databases of past natural disaster events exist, most important ones are.- EmDat: The International Disaster Database CRED,

Catholic University of Louvain, Brussels (Belgium) , http://www.emdat.be, publish reports annually

- Munich Re: Special issue: Topics (published annually)

- Swiss Re: Special issue: Sigma (published annually)

Munich Re: Topics, Swiss Re: Sigma

www.munichre.com www.swissre.com

• Different definitions of disasters:

Motivation

Munich Re

Em-Dat

SwissRe

Adjustment for inflation

• Swiss Re example based on Floods in UK: 29 October-10 November 2000

EMDAT starts from 1900

* EM-DAT 2005

Munich Re Figures: 1980-2010

Munich Re 2011

Munich Re Figures

Munich Re 2011

Munich Re Figures

Munich Re 2011

Munich Re Figures

Munich Re 2011

Munich Re Figures

Munich Re 2005

Swiss Re: Insured Losses

Swiss Re 2011

0

50

100

150

200

250

High income Middle income Low income

Per capita income country groups

Fata

litie

s/ev

ent

Average losses per income group

*

**

* NatCatService 2005** NatCatService 2005

0

2

4

6

8

10

12

14

High income Middle income Low income

Loss

es a

s %

of G

DP

Per capita income country groups

* NatCatService 2005** NatCatService 2005

Average losses per income group

Methodology for comparison

Hochrainer, 2006

HondurasImpact of disasters on GDP growth in

Honduras

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

1996 1997 1998 1999 2000 2001 2002 2003 2004

Growth without Mitch or drought Growth with Mitch and drought

1.Actual GDP growth in Honduras with events vs. projected growth without eventsSource: Zapata, 2008

Honduras

1.GDP trajectoriesSource: WDI, 2007; own calculations

GDP in Honduras

5,000

5,500

6,000

6,500

7,000

7,500

8,000

1996

1997

1998

1999

2000

2001

2002

2003

2004

Mill

ion

cons

tant

200

0 U

SD

Projected w/o event-ECLACProjected w/o event-IIASAObserved

Direct effect due to wealth loss

Indirect development loss

Observed GDP in Honduras with events vs. projected growth without events. Source: Zapata, 2008; World Bank, 2007; own calculations

Currently Paradigm shift

Government assistance (taxes)Kinship arrangementsDonor assistance

Insurance and reinsurance, microinsuranceCatastrophe bond, weather derivativesContingent credit, reserve fund

Turkey: Insurance Pool (2000)India: Weather derivatives (04)Mexico: Cat bond (06)India, Colombia, Mexico etc: FundsColombia: Contingent credit (05)Caribbean: Regional insurance pool (2006)Pacific: Regional insurance pool (in the making)Global: GFDRR, GIRIF (2008)

Traditional approach to risk financing

ProactiveReactive

All with donor support

Source: Bettencourt et al., 2006

Planning and mainstreaming disaster risks into developmental planningPlanning disaster risk into

development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning disaster risk into development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning disaster risk into development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning disaster risk into development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning disaster risk into development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning disaster risk into development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning disaster risk into development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning disaster risk into development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning disaster risk into development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning disaster risk into development

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006

Planning and mainstreaming disaster risks into developmental planning

Source: Bettencourt et al., 2006