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Posts feedLongevity capital requirements on the edge
in Kleinow & Richards (2016, Table 5) we noted a seeming conundrum: the best-fitting ARIMA model for the time index in a Lee-Carter model also produced much higher value-at-risk (VaR) capital requirements for longevity trend risk. How could this be?
All about the base(line)
When we first developed a technique for putting longevity trend risk into a 1-in-200 framework consistent with Solvency II, we sought to accommodate model risk by supporting a wide range of stochastic projection models.
What — and when — is a 1:200 event?
The concept of a "one in two hundred" (1:200) event over a one-year time horizon is well established as a reserving standard for insurance in several territories: the ICA in the United Kingdom, the SST in Switzerland and the forthcoming Solvency II standard for the entire European Union.
Benchmarking VaR for longevity trend risk
I recently wrote about an objective approach to setting the value-at-risk capital for longevity trend risk. This approach is documented in Richards, Currie & Ritchie (2012), which was recently presented to a meeting of actuaries in Edinburgh.
VaR-iation by age
During the public discussions of our paper on value-at-risk for longevity trend risk, one commentator asked for a fuller presentation of VaR capital requirements by age. In the paper, as with our introductory overview, we used age 70 as a representative average age of an annuity portfolio.
VaR for longevity trend risk
Last month Stephen, Iain and Gavin presented their paper on putting longevity trend risk into a one-year, value-at-risk (VaR) framework. The presentations were made to audiences of actuaries in Edinburgh and London, and the video of the London debate is now available online.
Canonical correlation
At our seminar earlier this year I looked at the validity of assumptions underpinning some stochastic projection models for mortality. I looked at the assumption of parameter independence in forecasting, and examined whether this assumption was borne out by the data. It transpires that the assumption of independence is a workable assumption for some models, but not for others. This has important consequences in a Solvency II context — an internal model must be shown to have assumptions grounded in fact.