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Posts feedHedging or betting?
Last week I presented at Longevity 14 in Amsterdam. A recurring topic at this conference series is index-based approaches to managing longevity risk. Indeed, this topic crops up so reliably, one could call it a hardy perennial.
Socio-economic differentials: convergence and divergence
Many western countries, including the UK, have recently experienced a slowdown in mortality improvements. This might lead to the conclusion that the age of increasing life expectancies is over. But is that the case for everyone?
How much data do you need?
There are two common scenarios when an actuary has to come up with a mortality basis for pensioners or annuitants.
Reverse Gear
Against a background of long-term mortality improvements it is understandable to expect that societal change and developments in health care will be agents of progress. Recent research from Princeton Professor of Economics Anne Case and Nobel prize-winning economist Angus Deaton jolts such complacency in the starkest way.
Haircut or hedge-trim?
Richard Willet's observation last year on the restatement of population estimates was picked up again recently by the BBC. Amongst the implications of the missing nonagenarians are some potentially interesting consequences for index-based longevity hedges.
Risk and models under Solvency II
Insurers need to have internal models for their major risks. Indeed, both the Individual Capital Assessment (ICA) regime in the UK and the pending Solvency II rules in the EU demand that insurers have good models for their risks.
Everything counts in large amounts
Models for projecting mortality are typically built using information on lives with deaths by age and gender. However, this ignores an important risk factor for longevity, namely socio-economic group. For annuity and pension reserving, therefore, it would be helpful to use such information when building stochastic projection models.
A basis point
In an earlier post I mentioned the advent of survivor forwards, or S-forwards, a derivative contract which could be used for hedging pension liabilities.
Forecasting with limited portfolio data
In a recent post on basis risk in mortality projections, I floated the idea of forecasting with limited data and even suggested that it would be possible to use the method to produce a family of consistent forecasts for different classes of business. The present post describes an example of how this idea works in practice.
Self-selection
Actuaries valuing pension liabilities need to make projections of future mortality rates. The future is inherently uncertain, so it is best to use stochastic models of mortality. Unfortunately, such models require a long enough time series, but few (if any) portfolios have such data.