I have a bad habit of buying the same book twice or printing the same article three times. Wasteful. So I decided to catalog what I have on hand. Then I thought this might be of some plausible use to others so I added the list here. This excludes what I have lost or thrown away and items read before I kept what I printed. Obviously it ignores what I've read online but have not printed. This is only related to RH work (retirement, annuities, asset allocation, pensions, options math, econ, systematic trading, etc) so it excludes things like casual reading and literature.
* is an "editor's choice" selection
Sep 28, 2018
Sep 23, 2018
How I might tweak my consumption utility simulator in the future
I wanted to look, in this post, at what might be missing in my lifetime consumption utility simulator. Specifically I wanted to anticipate how I might adjust it if I were to think more carefully about the presence of things like annuities, bequest motives, spend shocks such as long term care, the need to inter-temporally shift consumption in the future, support a desire for optionality on future consumption increases and so forth. None of that is "answered" here in this post. I just wanted to think about how I might deal with it at some point. The goal is not to educate a reader, by the way, it is rather to commit to "paper" what I think I'm trying to do for myself. I find that writing something down makes me think more carefully which I can then use in the future.
The Basic Consumption Utility Function
In my attempt to wing a consumption utility simulator earlier this year, I tailored the core value function, coded in R, to be like Case A in Yaari's 1965 paper. That was the case with what he called Fisher style utility (with constraints) without insurance (annuity) markets. His Case A function -- without much explanation of terms except that Tbar is random life, V is an expected discounted utility of lifetime consumption, c is consumption, g is utility of consumption and alpha is a subjective discount -- looked like this:
My amateur value function, the one I try to simulate, looks like this below which is more or less the same thing as above, given my non-economist efforts, except it's discrete and simulated...if I understood things right...which is a big if because it's hard for me to read calculus and impossible for me to read the related differential equations. Let's just say I made a good faith effort:
This does a journeyman's job for what I am trying to do personally and works well enough for evaluating basic consumption utility. The terms and sim structure can be understood here.
The Basic Consumption Utility Function
In my attempt to wing a consumption utility simulator earlier this year, I tailored the core value function, coded in R, to be like Case A in Yaari's 1965 paper. That was the case with what he called Fisher style utility (with constraints) without insurance (annuity) markets. His Case A function -- without much explanation of terms except that Tbar is random life, V is an expected discounted utility of lifetime consumption, c is consumption, g is utility of consumption and alpha is a subjective discount -- looked like this:
 |
| Yaari case A (eq 13) |
My amateur value function, the one I try to simulate, looks like this below which is more or less the same thing as above, given my non-economist efforts, except it's discrete and simulated...if I understood things right...which is a big if because it's hard for me to read calculus and impossible for me to read the related differential equations. Let's just say I made a good faith effort:
 |
| RH EDULC value function |
Sep 16, 2018
In a parallel universe...
In a parallel universe this moment has always existed and will continue to exist into an infinite future. That idea pleases me.
Also, in case any reader is wondering, this is why I "retired" early. There was absolutely nothing related to the FIRE movement whatsoever about my choice. I had been taking full time (24 hours a day solo, 5 days a week) care of these children of mine for many years before 2008. When I (we) went through a move and a divorce in '08, given the presence of a constructively involved co-parent who also either traveled or worked intensely, I felt compelled to to maintain continuity of care for them (i.e., retire) for a while to keep young minds sane and focused. That I was 50 at the time was irrelevant (except that I now know the risk...hence, the blog). One of the most beautiful sentences I've ever heard in life was a now-adult child say to me: "I know what you did for us..."
 |
| maybe 10-11 years ago |
Also, in case any reader is wondering, this is why I "retired" early. There was absolutely nothing related to the FIRE movement whatsoever about my choice. I had been taking full time (24 hours a day solo, 5 days a week) care of these children of mine for many years before 2008. When I (we) went through a move and a divorce in '08, given the presence of a constructively involved co-parent who also either traveled or worked intensely, I felt compelled to to maintain continuity of care for them (i.e., retire) for a while to keep young minds sane and focused. That I was 50 at the time was irrelevant (except that I now know the risk...hence, the blog). One of the most beautiful sentences I've ever heard in life was a now-adult child say to me: "I know what you did for us..."
Some of my influences...for now
Being neither academic nor practitioner, this blog has never been a "teacher" blog. My goal has never really been to explain or educate as such since I know so little and I feel like I know even less as I learn what I don't know. Rather, I am a student and the purpose of the blog is to report from class on what I am imperfectly learning...didactic, perhaps, only in the reporting. But learn I do, so...
The following is an adaptation of an email I sent to someone in response to a question on "which retirement researcher/practitioner do you think influenced your thinking most?" In real life, the question and answer are more process than snapshot but I took at shot at answering the question as a snapshot. I've made some edits and additions to the email in this post as well as some likely and accidental omissions. This is not in rank order, just an impressionistic melange:
The following is an adaptation of an email I sent to someone in response to a question on "which retirement researcher/practitioner do you think influenced your thinking most?" In real life, the question and answer are more process than snapshot but I took at shot at answering the question as a snapshot. I've made some edits and additions to the email in this post as well as some likely and accidental omissions. This is not in rank order, just an impressionistic melange:
Sep 13, 2018
Perfect Withdrawal Rates with normal and fat-tailed return distributions
Point of the Post
In this post I am taking a non-rigorous, non-exhaustive peek at what happens to a perfect withdrawal rate (PWR) distribution when generated with either a normal or a fat-tailed return distribution where I have coerced the first and second moments to match. The goal is mostly just to do it as a placeholder for something I want to try later...but also to see what happens visually to the PWR distribution as well as to get a first pass look at the scale of the effect on the 5th or 10th percentile PWR for quasi-reasonable but fake assumptions.
In this post I am taking a non-rigorous, non-exhaustive peek at what happens to a perfect withdrawal rate (PWR) distribution when generated with either a normal or a fat-tailed return distribution where I have coerced the first and second moments to match. The goal is mostly just to do it as a placeholder for something I want to try later...but also to see what happens visually to the PWR distribution as well as to get a first pass look at the scale of the effect on the 5th or 10th percentile PWR for quasi-reasonable but fake assumptions.
Sep 12, 2018
Picerno (and RH) on High Yield
James Picerno has a blog post out on Is It Time To Start Taking Profits In Junk Bonds? and I think his conclusions are correct, conclusions which can be summed up by his final comment:
...the good times may roll on for the high-yield market. But for anyone who respects probabilities, the odds don’t look especially favorable. After all, the problem with holding assets that are priced for perfection is that we live (and invest) in an imperfect world.On the other hand the discussion of high yield reminded me of two things:
Sep 10, 2018
Revisiting Perfect Withdrawal Rates But with Variable Duration
Perfect Withdrawal rates
Over the last 3 or 4 years a number of different sources[1] have discussed a way of viewing retirement consumption analysis by way of "perfect" or "maximum" withdrawal rates (PWR). Quoting Suarez 2015: PWRs are the "constant amount that will draw the account down to the desired final balance if the investment account provides...any...particular sequence of annual returns." i.e., it is the rate in any of some number of parallel simulated universes that draws an account down to zero (or a bequest level) at the end of the duration of the analysis. If one were to simulate 10000 parallel universes, one would thus have a distribution of 10000 constant "perfect" withdrawal rates for each parallel world. Constant is a debatable proposition but for first pass examination of retirement processes (not last mile real planning) it is ok enough.
The distribution, then, can be examined by retirees and planners for making a joint "allocation and spending" choice. It is, in effect, no more and no less than inside-out monte carlo simulation except that the fail rate is set to zero over the interval of interest and spend rates are allowed to vary to make it work. Looking at the left side of the distribution (the lower spend rates, at or below which were the rates that worked in really bad situations) is interesting. Looking at the right is not or rather is an elegant problem to have. The cumulative percentile (say 5% or 10%) of spend rates that worked in the worst of situations is roughly equivalent to a fail rate analysis except that it is, in my opinion, a wee bit more transparent and conversation-worthy.
Over the last 3 or 4 years a number of different sources[1] have discussed a way of viewing retirement consumption analysis by way of "perfect" or "maximum" withdrawal rates (PWR). Quoting Suarez 2015: PWRs are the "constant amount that will draw the account down to the desired final balance if the investment account provides...any...particular sequence of annual returns." i.e., it is the rate in any of some number of parallel simulated universes that draws an account down to zero (or a bequest level) at the end of the duration of the analysis. If one were to simulate 10000 parallel universes, one would thus have a distribution of 10000 constant "perfect" withdrawal rates for each parallel world. Constant is a debatable proposition but for first pass examination of retirement processes (not last mile real planning) it is ok enough.
The distribution, then, can be examined by retirees and planners for making a joint "allocation and spending" choice. It is, in effect, no more and no less than inside-out monte carlo simulation except that the fail rate is set to zero over the interval of interest and spend rates are allowed to vary to make it work. Looking at the left side of the distribution (the lower spend rates, at or below which were the rates that worked in really bad situations) is interesting. Looking at the right is not or rather is an elegant problem to have. The cumulative percentile (say 5% or 10%) of spend rates that worked in the worst of situations is roughly equivalent to a fail rate analysis except that it is, in my opinion, a wee bit more transparent and conversation-worthy.
Sep 9, 2018
Optimal Spend Rates and Casual, Borrowed Backward Induction
The Point of the Post
I was contemplating, the other day, a project that would use backward induction and stochastic dynamic programming in order to visualize approximate optimal spend rates at different ages. I wanted to try something like this because the form of analysis is powerful and efficient, the method is economically rigorous, consumption is a big deal in a multi-period retirement decumulation setting, and I had done it once before, on asset allocation at that time, in a post from January 2017, though that now seems like a million years ago. I paused a long while on this idea for several reasons:
1. This, surprisingly to me, is becoming less and less trivial but my aging, decaying eyes are starting to limit what I can and want to do in terms of chasing down the financial economics of retirement. My ability to sit and stare at a screen of code for hours or days or weeks -- which is what it would take for this idea -- is much more constrained than it was even 5 years ago. These are the type of wages paid for entering late middle age.
2. I could not figure out how to make the proper focus on spend rates. The lowest spend rate will always have the highest probability of success which will therefore always make self-denial the dominant strategy for success, something that is both common sense and ridiculous at the same time, and, not unrelatedly,
3. If using utility to evaluate spend rates or lifetime consumption -- so that they do not tend towards self denial -- makes more sense, then I have no idea how to chain things backward. In my prior attempt it was relatively trivial (not really, technically, but conceptually) to chain probabilities backward. Chaining utility makes no sense.
I was contemplating, the other day, a project that would use backward induction and stochastic dynamic programming in order to visualize approximate optimal spend rates at different ages. I wanted to try something like this because the form of analysis is powerful and efficient, the method is economically rigorous, consumption is a big deal in a multi-period retirement decumulation setting, and I had done it once before, on asset allocation at that time, in a post from January 2017, though that now seems like a million years ago. I paused a long while on this idea for several reasons:
1. This, surprisingly to me, is becoming less and less trivial but my aging, decaying eyes are starting to limit what I can and want to do in terms of chasing down the financial economics of retirement. My ability to sit and stare at a screen of code for hours or days or weeks -- which is what it would take for this idea -- is much more constrained than it was even 5 years ago. These are the type of wages paid for entering late middle age.
2. I could not figure out how to make the proper focus on spend rates. The lowest spend rate will always have the highest probability of success which will therefore always make self-denial the dominant strategy for success, something that is both common sense and ridiculous at the same time, and, not unrelatedly,
3. If using utility to evaluate spend rates or lifetime consumption -- so that they do not tend towards self denial -- makes more sense, then I have no idea how to chain things backward. In my prior attempt it was relatively trivial (not really, technically, but conceptually) to chain probabilities backward. Chaining utility makes no sense.
Sep 2, 2018
Optimized spend rates by age and allocation in a lifecycle-decumulation utility model
The Point of the Post
This post is a continuation of several past posts where I have been using (trying to shake out) a lifecycle utility model (decumulation focus only) [1] that includes a wealth depletion framework -- i.e., where there is a time interval in the late lifecycle when non-pensionized wealth runs out and consumption snaps to available income -- to evaluate "optimal spend rates." The optimization is done by evaluating a value function (expected discounted utility of lifetime consumption) that is calculated, based on a simulation process, across different spend rates, range of ages, different risk allocations and different coefficients of risk aversion. The goal is to see what happens to optimized spend rates at different ages and how it might be influenced by asset allocation. Framed as a question it might look like this:
This post is a continuation of several past posts where I have been using (trying to shake out) a lifecycle utility model (decumulation focus only) [1] that includes a wealth depletion framework -- i.e., where there is a time interval in the late lifecycle when non-pensionized wealth runs out and consumption snaps to available income -- to evaluate "optimal spend rates." The optimization is done by evaluating a value function (expected discounted utility of lifetime consumption) that is calculated, based on a simulation process, across different spend rates, range of ages, different risk allocations and different coefficients of risk aversion. The goal is to see what happens to optimized spend rates at different ages and how it might be influenced by asset allocation. Framed as a question it might look like this:
What kind of optimized consumption does $1M in purchasing power (age 60 baseline) buy at different ages, and for different allocations to risk, in a lifecycle/decumulation utilility model that uses a wealth depletion time framework?
As in past posts the assumptions and parameters are illustrative rather than realistic and are for self learning rather than suggesting or recommending strategies and plans of action. Also, since this uses a utility framework, you'd have to buy into that, which I'm not sure I do even though I am here doing it.
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