Demographic Risk and its impact on asset liability management in …sfb649.wiwi.hu-berlin.de/fedc/events/Motzen07/... · 2007. 6. 18. · Tagung des SFB 649 ÖKONOMISCHES RISIKO-11-

Tagung des SFB 649 ÖKONOMISCHES RISIKO - 1 -

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Demographic Risk and its impact on asset liability management in life insurance companies

Prof. Dr. Helmut Gründl


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Contents

Motivation

Relevant previous work

Research objectives

Planning horizon and team

First step

Next step


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Motivation:Population Dynamics and Government Pensions

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male under 20 male betw een male over 60

female under 20 female betw een female over 60

Germany

2007


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Germany

2017


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Germany

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Germany

2037


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Germany

2047


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Motivation: Private Life Annuities as the Solution?

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Premiums and Risk Management depend on future life expectancies

Problem: In the past, mortality developments have differed dramatically from forecasts

Demographic Risk

Uncertainty about future risk


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Motivation: Adjustment of Life Expectancy

Aggregate mortality rate decreased faster than expected

Resulted in adjustment of life tables DAV 1994 R in 2004

DAV 1994 R DAV 2004 R DAV 1994 R DAV 2004 RFor 2004 21 years 24 years 25 years 27 yearsFor 2040 24 years 30 years 28 years 34 years

Male FemaleEstimation of Life Expectancy at Sixty-five


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A path-breaking technological or medical innovation

People live unexpectedly longer

Problem for financial stability of annuity providers

Financial disaster for retirees

Systemic risk for capital markets

Serious threat to the financial stability of society

Demographic Risk


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Relevant Previous Work

Shareholder value management and asset liability management in insurance companies

Gründl / Schmeiser (1999, 2004)

Gründl / Winter (2005)

Gründl / Post / Schulze (2006)

Schulze / Post (2006)

Valuation of financial instruments and insurance products

Gründl (1993, 1994, 1995a, 1995b)

Gründl / Schmeiser (2002a)

Gründl / Nietert / Schmeiser (2004)

Capital allocation in insurance companies

Gründl / Schmeiser (2001, 2002b, 2007)


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Relevant Previous Work

Gründl / Post / Schulze (2006)

“To Hedge or Not to Hedge: Managing Demographic Risk in Life Insurance Companies”

in: The Journal of Risk and Insurance, 73, 19-41

Objective function: maximize shareholder-value

Determination of optimal risk management mix

Natural hedge between term life insurance and annuity policies

Outlook: Investigating further risk management tools like securitization of mortality risk, reinsurance, underwriting policy


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Research Objectives

1. Determination of optimal risk management mix

Improvement of mortality risk modeling:Determination of influencing factors and underlying stochastic process using German empirical data

Extension of risk management mix discussed in Gründl/Post/Schulze (2006)

- Insurance Linked Securities

- Development of new instruments for hedging demographic risk

Determination of optimal risk management mix to maximize shareholder value

Value of information w.r.t. mortality risk modeling


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Research Objectives

2. Valuation of risk management instruments

Inclusion of the willingness-to-pay of policyholders(Using the expected results from the project “Reaction of policyholders to the risk situation of insurance companies”, joint project with Christian Schade and AnjaZimmer)

Valuation of embedded options in annuity policies

Valuation of guarantees in term life insurance and annuity policies


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Research Objectives

3. Implications for insurance supervisory system

Assessment of the quality of the present solvency supervisory system in the context of demographic risk

Improvement of solvency regulation based on the results derived from objective 1 research

Implication for pension system reforms

Maybe part of the „Cluster of Excellence“ „Security and Risk“


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Planning Horizon and Team

Project duration: 36 months

24 months funded by DFG

Research team

Research assistant: Dipl.-Kffr. Tian Zhou-Richter

Student assistants: Ms. Gökce Kirca

Ms. Yiran Zhang


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First Step:

A Multi-Factor Model for Forecasting Mortality Rate Development


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Research Objectives of Step 1

Creating a multi-factor model for mortality rate development

Identification of relevant factors based on German empirical data

Forecasting future morality rates based on present public information

Implication for risk management

New asset management strategies

Development of new ways of securitization


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Time Series of Mortality Rate

Example: Mortality rate of 65-years old women (Data source: http://www.mortality.org/)

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0,021000

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Valu

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Macroeconomic Factors

GDP (Data source: German Federal Statistical Office)

Growth rate of GDP in %

– 2,0

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14,0


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Macroeconomic Factors

Unemployment rate(Data source: German Federal Statistical Office)


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Physical Health Factors

Expenses for medical care per capita

Data available: 1970 – 2005

Change of the calculation method in 1999

Recalculation according to the new method back to 1991

Nutrition

Body Mass Index (= Mass (kg) / height2 (m2))

Obesity (currently looking for data)


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Physical Health Factors

Tobacco consumption: only taxed sales volume available

Alcohol consumption: starting in 1950(Data source: German Federal Statistical Office)


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Social FactorsLiving space per capita (Data source: German Federal Statistical Office)

Data available from 1965 to 2002

Annual data of 20 out of 38 years ( missing values)


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Social Factors

Education

% of population with at least an university degree (currently looking for data)

Income per capita (currently looking for data)


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Climate factors

Number of very hot and cold days per year

Number of days with temperature > 30°C or <0°C

No official average temperature of whole Germany available

Usage of average temperatureof 13 weather stations in FEDC

(currently processing)

Database: DWD in FEDC

Or directly: number of extreme temperatures cases of death


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Data available through FEDCWeather data

Financial markets performance

Macroeconomic data starting in 1991

? Data before 1991

? Price performance of Insurance Linked Securities

We provide- Older macro- and socioeconomic data collected by

German Federal Statistical Office

- Data quality enhancement

Database


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Expected Results

Elicitation of relevant factors in mortality rate changes

Development of forecasting models for mortality rate based on public information

Implications for new ways of securitization utilizing the identified relevant factors

Implications for investment strategies of annuity providers


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Next Step

Determination of an insurer’s risk management toolbox based on the results of step 1

Optimal risk management mix taking into account policyholders’ reactions

Valuation of risk management instruments for hedging mortality risk

Documents

Demographic Risk and its impact on asset liability management in …sfb649.wiwi.hu-berlin.de/fedc/events/Motzen07/... · 2007. 6. 18. · Tagung des SFB 649 ÖKONOMISCHES RISIKO-11-