Note: as in the previous essays, this is a draft as I hone some of this content. Also, since I view these essays as consolidating and integrating what I've learned about ret-fin so far, I will continue to add to and update this provisional latticework over time in response to new findings or errors.
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This essay is a continuation of:
Five Retirement Processes - Introduction
Process 1 – Return Generation
Process 2 - Stochastic Consumption Processes
Process 3 - Portfolio Longevity
Summary here.
"The primary determinant of retirement cost is longevity." Dirk Cotton
Process 4 - Human Mortality and Conditional Survival Probability
The first thing you need to know about this post is that I will start sandbagging myself on the title topic before the first sentence is even completely read. Actually sandbagging is not the right word since it implies "deep knowledge + deceit" used to gain some advantage. Let's say, rather that I will formally disclaim any professional facility with the topic of human mortality and conditional survival probability because I am not an actuary or a demographer. I am a retiree and an amateur-wannabe-retiree-quant. Hundreds of years, whole industries, zillions of careers, oceans of digital ink, and no small number of degrees have been dedicated to the study of longevity and actuarial science. I am clearly not one of those people and I am not working in that domain. (email me for any errors or omissions; I will correct and attribute if it makes sense)
Yes, we can safely assume that I have no role in pricing insurance products or researching population dynamics. Nor do I hedge corporate books of life-risk, either against people living too long (annuities) or people dying too soon (life insurance). But then again, "they" (the people that do that kind of thing) are not often seen rough-thumb-nailing boundaries for portfolio longevity in retirement or evaluating discounted utility of lifetime consumption across several strategies. Nor are they often seen trying to scale any one individual's estimate of lifetime ruin risk as it might apply to what might happen to that person's children if that risk were to unfold (apologies if you do this kind of thing. I'm throwing you under the bus to make a point).
So, while longevity science in it's extreme analytic professional form does not interest me much, longevity considerations, in a very general and personal sense, are of interest to me...great interest. This is because I have a lot of self interest in the long term impact of my retirement choices. One of the weirder things in blogging is to quote oneself but I will here because it is relevant to this point. In the last process-post (Process 3 - Portfolio Longevity), I said this:
...while [portfolio longevity] and human mortality are truly independent processes and it is useful to make that separation intellectually, in the end [portfolio longevity] is constrained in our imagination by the limit of our self interest which is itself imposed by how long we are allowed to live and spend. --Me.Using my own words in that way sets me up to say that this post is not intended to, didactically or otherwise, instruct anyone on anything, particularly actuarialness, which is not a word. My only goal here is to report on some few tidbits related to the broader subject of human mortality that I have found useful over the last three or four years in the retirement quant stuff I try to do. These tidbits have been quite useful to the analysis of the retirement processes that are of interest to me and on which I have written in my last several essays. They can be summarized like this:
1. It makes sense to nudge oneself off of the false certitude of point estimates for longevity and into thinking in probabilities and distributions. This is still true even if one, in the end and like me, goes back to using a fixed reference point like "30 years."
2. When thinking in distributions, it makes sense to be aware that there are different populations that can shape the probabilities of interest.
3. Life expectancy, even if we know the population and distribution, is a drifting target over time due to changes in culture and science.
4. Life expectancy, even if we happen to know the population, the distribution, and the drift, is a still a moving target because survival probabilities are conditional on the age to which one has survived.
5. There are some simple math hacks that can be used by the quantitatively inclined to model longevity if the need arises.
6. Generally speaking, it looks like the maximum expectation for human longevity does not change much...but the distribution of probability before that time does.
7. Planning for either an average longevity or a maximum longevity is probably not the wisest move when contemplating planning horizons.
