Here we review GC Capital Ideas posts on the challenges (re)insurers face managing and modeling casualty catastrophe risks.
Posts Tagged ‘cap mgmt’
Recently, we have seen a change in the way reinsurance is viewed in some companies and groups: The chief financial officer increasingly recognizes reinsurance as an instrument to achieve risk and capital management, rather than using capital measures like equity and sub-debt issuances.
The Own Risk and Solvency Assessment (ORSA) requirements are the key element of the Pillar 2 qualitative risk management requirements. The purpose of an “own risk assessment” by each company is to prove the appropriateness of the standard formula or internal model results if the company has applied for a certified internal model. While the Pillar 1 solvency capital requirement is calculated on a one-year basis to show that a company has enough capital to avoid insolvency through the end of the year in a 1-in-200 year event, the focus in Pillar 2 ORSA is the forward-looking assessment of solvency capital adequacy. Companies need to provide a projection of the risk and capital position for the entire planning period (at least three years), which has to be consistent with the business case balance sheet and profit and loss projection. The aim of ORSA is to demonstrate that there is an adequate level of capital available to support the business plan for a longer period. Based on this planning projection of the risk and capital position, (re)insurers need to define meaningful stress tests and scenarios to show they would be adequately capitalized in adverse scenarios as well. If a company would face solvency issues in certain stress scenarios, it needs to show it has countermeasures in place in order to reach the strategic targets of the corporate and risk strategy again.
With the world rapidly changing and evolving, what was the case 10 years ago is not the case today and will not be 10 years from now. As discussed in detail in this report, A Clearer View of Emerging Risks, new technologies can impact people in their everyday lives through the products we use, how long we live, how much we spend to keep ourselves healthy and where our information is stored. All of these carry inherent risks that are new to the world and that may not be a part of the historical dataset upon which (re)insurers rely for pricing and/or establishing proper risk controls.
One purpose of enterprise risk management (ERM) is to help (re)insurers determine how much capital is needed to support the risks they assume (subject to risk tolerance). Instead of segmenting portfolios and handling each peril on a standalone basis, a robust ERM methodology would use a holistic approach to risk and capital management where threats are identified and monitored, all action plans are developed and risks are measured.
Guy Carpenter Announces MetaRisk® 8.1, the Latest Version of its Premier Economic Capital Modeling Tool Suite
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:
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.
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.
Once the risks have been identified and ranked, the next step is how to quantify the likely impact on the financial results of the firm. The first and most obvious question is what available quantification techniques are available for each risk on the list. This will depend on the availability of relevant data and commercially produced models.