Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200

Abstract: This paper investigates catastrophe risk for South African life insurers by considering the additional
deaths that could arise from a 1-in-200 year mortality shock. Existing South African academic research
on catastrophic risk has mostly focused on property losses and the resulting impact on property
insurance companies. Life catastrophe risks have not been extensively modelled in a South African
context. Local research would be beneficial in terms of quantifying these catastrophic risks for South
African life insurers, and would assist firms when assessing their own catastrophe mortality solvency
requirements under the new Solvency Assessment and Management (SAM) regime by providing a
summary of data relating to various past catastrophes.

In this paper we model a wide range of catastrophes to assess such mortality risk faced by life
insurance companies in South Africa. An extensive exercise was undertaken to obtain data for a wide
range of catastrophes and these data were used to derive severity and frequency distributions for
each type of catastrophe. Data relating to global events were used to supplement South African data
where local data were sparse. Data sources included official government statistics, industry reports
and historical news reports. Since, by nature, catastrophic events are rare, little data are available for
certain types of catastrophe. This means there is a large degree of uncertainty underlying some of the
estimates. Simulation techniques were used to derive estimated distributions for the potential number
of deaths for particular catastrophic events. The calculated overall shock for the national population
was 2.6 deaths per thousand, which was lower than the SAM Pillar 1 shock of 3.2 deaths per thousand
for the same population.
It has been found that a worldwide pandemic is by far the main risk in terms of number of deaths in
a catastrophe and, given that this is the most significant component of catastrophe risk, prior research
on this risk in an South African context is summarised and revisited.

Keywords: Life catastrophe; mortality shock; Solvency II; Solvency Assessment and Management (SAM);
pandemic; stochastic model; 1-in-200