Posts Tagged ‘cap mgmt’



August 11th, 2016

Emerging Risk Exposure and Aggregation

Posted at 1:00 AM ET

Here we review GC Capital Ideas posts on the challenge for (re)insurers to identify their exposure to emerging risks and deal with the potential risk aggregation.

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August 10th, 2016

Changing View on Applying (Re)insurance to Risk Decisions

Posted at 1:00 AM ET

Here we review GC Capital Ideas posts on how the view of applying (re)insurance to risk decision making is evolving.

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August 3rd, 2016

Utilizing Data and Models to Understand Emerging Risks

Posted at 1:00 AM ET

Here we review GC Capital Ideas posts on how the accumulation of data and utilization of models will help (re)insurers understand the implications of emerging risks.

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July 25th, 2016

Benchmarks for Enterprise Risk Management Disclosures

Posted at 1:00 AM ET

Here we present GC Capital Ideas’ stories on analyses of enterprise risk management disclosures. A 2014 study updated the analysis done in 2009, one of our most popular stories. The full briefings are attached.

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July 21st, 2016

Value in Enterprise Risk Management Practices

Posted at 1:00 AM ET

Here we review GC Capital Ideas posts on the benefits of enterprise risk management practices in supporting (re)insurance capital and regulatory decision making.

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July 7th, 2016

Changes to Best’s Capital Adequacy Ratio (BCAR)

Posted at 1:00 AM ET

Here we review recent GC Capital Ideas posts on developing changes to Best’s Capital Adequacy Ratio (BCAR) and the potential impact of those changes on (re)insurers.

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June 13th, 2016

Reserving and Capital Setting: Conclusion

Posted at 1:00 AM ET

The obvious response to the issues emerging risks provide is to make sure reserves and capital position are more than robust enough for any eventuality - however remote - and then release them when the risks fail to materialize. But, there are many arguments against this as a practical strategy:

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June 9th, 2016

Reserving and Capital Setting: The Crystalization of Emerging Risks, Part II

Posted at 1:00 AM ET

The chart below attempts to illustrate the solvency calculation issue. Suppose the best estimate is 20 and the assessment from modeling is that the 1-in-200-year ultimate loss is 100. If all else stays the same and with the simplifying assumption that the yield curve stays flat, one can say that the sum of the 1-year solvency capital requirements (SCRs) approximated the difference between 100 and 20 (i.e. 80). Yet, because of the discounting, when in time the change in own funds is recognized, is important. The black line represents a linear recognition pattern so the 1-year SCRs are all equal with increments of 10. The blue line represents a Binary Fast recognition so the first year SCR is 80 and the remaining years’ SCR are zero. This means that the deterioration is recognized quickly. The red line again shows binary recognition but with a slow pattern as the movement is only occurring toward the end of the liabilities’ life. The two curves in light blue and light red represent less severe versions of the binary forms.

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June 8th, 2016

Reserving and Capital Setting: The Crystalization of Emerging Risks, Part I

Posted at 1:00 AM ET

As discussed in the Executive Summary of this report, the term “crystalization of risk” refers to the timescale over which we realize that the risk is manifesting itself and how this view changes until ultimate understanding of quantum is reached and all liabilities are discharged. The “Reserving Risks” section in last year’s report, Ahead of the Curve: Understanding Emerging Risks looked at how information emerges in the presence of reserving cycles. The profit or loss in any particular financial year is made up of not only the profit or loss from the same accident year but also any recognized changes in the reserves on prior years.

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June 7th, 2016

Reserving and Capital Setting: Sizing the Problem, Part III: Quantifying Emerging Risks; Expert Judgement

Posted at 1:00 AM ET

Data quality and availability should also be examined in depth. Because the risks are new, the data may not be captured correctly to power the model, which will lead to further uncertainty and may even preclude the use of a model altogether.

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