Catastrophe modelling : deriving the 1-in-200 year mortality shock for a South African insurer’s capital requirements under solvency assessment management
| dc.contributor.author | Plantinga, A. A. | en_ZA |
| dc.contributor.author | Corubolo, D. J. | en_ZA |
| dc.contributor.author | Clover, R. J. | en_ZA |
| dc.date.accessioned | 2018-03-05T07:18:57Z | |
| dc.date.available | 2018-03-05T07:18:57Z | |
| dc.date.issued | 2015 | |
| dc.description | CITATION: Plantinga, A. A., Corubolo, D. J. & Clover, R. J. 2015. Catastrophe modelling : deriving the 1-in-200 year mortality shock for a South African insurer’s capital requirements under Solvency Assessment. South African Actuarial Journal, 15:51–92, doi:10.4314/saaj.v15i1.3. | |
| dc.description.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. | en_ZA |
| dc.description.uri | http://www.actuarialsociety.org.za/assa-news/sa-actuarial-journal-and-articles/#1502709871086-3ef34a01-4b0e | |
| dc.description.version | Publisher's version | |
| dc.format.extent | 63 pages | |
| dc.identifier.citation | Plantinga, A. A., Corubolo, D. J. & Clover, R. J. 2015. Catastrophe modelling : deriving the 1-in-200 year mortality shock for a South African insurer’s capital requirements under Solvency Assessment. South African Actuarial Journal, 15:51–92, doi:10.4314/saaj.v15i1.3 | |
| dc.identifier.issn | 1680-2179 (print) | |
| dc.identifier.other | doi:10.4314/saaj.v15i1.3 | |
| dc.identifier.uri | http://hdl.handle.net/10019.1/103211 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Actuarial Society of South Africa | |
| dc.rights.holder | Actuarial Society of South Africa | |
| dc.subject | Catastrophic risk -- South Africa | en_ZA |
| dc.subject | Emergency management -- South Africa | en_ZA |
| dc.subject | Disaster insurance -- South Africa | en_ZA |
| dc.subject | Insurance companies -- Risk assessment -- South Africa | en_ZA |
| dc.title | Catastrophe modelling : deriving the 1-in-200 year mortality shock for a South African insurer’s capital requirements under solvency assessment management | en_ZA |
| dc.type | Article | en_ZA |