Friday, May 18, 2018

Some Risks Can't be Modeled

My last few posts, The “Future” of Retirement Planning, The Limits of Simulation and Spending Rules and Simulation, have discussed different aspects of retirement planning, specifically, spending rules and Monte Carlo (MC) simulation.

Spending rules calculate a safe amount to spend in the current year. I highly recommend that you reapply your spending rule every year to take new information into account but if that's all you do then you have a one-year planning horizon.

If retirement were a game of combined chance and skill, like backgammon or poker (and it is, of course), then spending rules would identify our best current move. Simulation would tell us the probabilities that this move will ultimately win the game, like knowing the odds that your backgammon opponent will roll a 3 on his next turn (hint: they aren't good — I'd be willing to leave that stone uncovered).

A good poker player will know the odds of the deck and a good backgammon player will know the odds of the dice. They will become second nature. A good retirement planner will know the odds of possible retirement outcomes.

MC provides probability distributions for possible outcomes given a spending rule that would be repeated periodically over many lifetimes. In other words, it identifies financial risks of a retirement plan.

MC, however, only generates “normal” scenarios or those that would probably be drawn from a normal distribution. By design, MC creates most scenarios near the mean or “expected” outcome. The further from the mean, the less likely that a scenario will be created in an MC simulation.

The shortcoming of MC simulation is not that it will create unrealistic scenarios — quite the opposite — it won’t generate many highly unlikely outcomes. So, even after we test retirement plan risk with simulation we still don’t know much about the effects of low-probability catastrophic events.

Simulating such events, even if we could, wouldn’t be very rewarding. After the Great Recession, Nassim Taleb testified before Congress that improbable events are impossible to predict and called those who claim that they can forecast them “charlatans.” (He was referring to Value-at-Risk advocates.)

If Taleb is not to your taste, you can come to nearly the same conclusion by recognizing the huge confidence intervals inherent in our relatively small sample of historical market returns.  We simply can't be confident in predictions based on them.


Avoiding unforeseeable risks is not an option. It's hard to steer around an obstacle you don't know is there.
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After simulations, we still need a way to plan for the unknowable. Risk management generally proposes four strategies:
  1. Avoid the risk. You can avoid the risk of riding a motorcycle by not riding one.
  2. Mitigate the risk. Wear a helmet when you ride a bike.
  3. Insure the risk when insurance is available and affordable.
  4. Accept the risk when there is no realistic alternative. The risk of 30 years of consecutive market losses is a good one to accept. So is death by falling satellite.
Avoiding unforeseeable risks is clearly not an option. It's hard to steer around an obstacle you don't know is there. Mitigating these risks presents a similar challenge.

We can insure some retirement risks by buying annuities, umbrella liability and life insurance, for example, but insurers won't offer me long-term care insurance and premiums can be (or can become) unaffordable.

Regardless, at some point we must face the fact that our retirement plan can’t manage every risk by relying on good fortune in the stock market. (I say this knowing full well that many retirees in the “probabilist school” believe precisely that. I just don’t share their optimism, probably because I have spoken with 80-year old’s who lost that bet and must now get by on Social Security benefits alone.)

The best spending rules won’t eliminate these risks. After a long sequence of poor returns, they will simply reduce safe spending to a level that no longer supports the household’s standard of living. Nor will the best simulation software ferret them out and suggest fixes.

After selecting a spending rule and modeling outcomes with MC simulation we need to address low-probability catastrophic outcomes with insurance when we can. This is the theory behind floor-and-upside strategies — hedge your bet.

I consolidated a list of identified retirement risks in Retirement is Risky Business – Here's a List that should provide a starting point for your review. Low-probability catastrophic outcomes defy avoidance and mitigation but they’re worth contemplating and possibly worth insuring.

The key takeaway is that MC simulation can tell you a lot about fairly normal outcomes but very little about improbable, high-impact events, also known as "tail risk." Consequently, simulation is not the end of the retirement planning process. We have to evaluate tail risk by some process other than prediction and that means "seat of the pants."

It won't be a thorough process and according to Taleb, it can't be. That doesn't mean you shouldn't try. Having a floor of safe income, for example, can mitigate a lot of different, even unpredictable risks.

The best retirement plan will fail if Earth is hit by another dinosaur-ending asteroid but that's a risk we probably have to accept. There may be other low-probability, high-impact risks that we can mitigate, though, without being able to predict them with models.




Thanks, Mason Finance Group, for choosing The Retirement Cafe´ for your Best Retirement Blogs of 2018 list. Congrats to Ken Steiner's How Much Can I Afford to Spend in Retirement? blog, as well!


6 comments:

  1. "but insurers won't offer me long-term care insurance": why is that? I know that in Britain the insurers who offered it couldn't make a living from it and stopped offering it. But why?

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    1. Probably the same reasons. More and more US carriers are getting out of the business and insurers are VERY picky about whom they will insure.

      My agent told me that three of his last 12 applications had been accepted and he's pretty picky abouy for whom he bothers. He also told me about an otherwise healthy middle-aged woman who was rejected because she had a bad knee. Apparently bad knees are statistically linked to long term care.

      To be honest, I applied not expecting approval. The insurer received my application on a Friday and requested that I be interviewed by medical staff. When I called on Monday to set up an appointment for the interview, I was told that it was no longer necessary. I received notice of rejection later that day. Not a lot of wasted time there.

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    2. I found it interesting that I was deluged with Long Term Care Insurance offers about 10 years ago when I was 50. The prices seemed reasonable, but my big question was whether or not the insurance firms would be solvent in 2040 or 2050 when I would most likely need the care.I thought it was a business model with a high probability of having actuarial cost modeling errors given the increasing longevity simultaneous with increasing medical costs. I just didn't see how they could price it with sufficient accuracy to ensure a high survival rate of the insurers. So I never bought in as I thought there was a high probability I would pay years of premiums and then have nothing even though it was an insured event.

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    3. You run into the same problem if premiums rise to a level you can no longer afford.

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  2. Interestingly, Derek Tharp found that Monte Carlo simulations actually overstate "fat tails." https://www.kitces.com/blog/monte-carlo-analysis-risk-fat-tails-vs-safe-withdrawal-rates-rolling-historical-returns/

    A big problem with most Monte Carlo simulations is that they do not incorporate mean reversion of returns, a phenomenon now widely known.

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    1. Glad you brought that up. I have seen the Tharpe white paper and find it seriously flawed. I plan to write about it soon.

      Monte Carlo simulations don't overstate fat tails. Monte Carlo simulations of constant-withdrawal strategies might but I don't think so. Since we can't accurately measure tail risk, we can't state it, understate it, or overstate it.

      Mean reversion is probably too widely known. Economists debate whether it exists, how long the market takes to revert, and whether it is substantial even if it does exist (see here, for example). Some economists argue that it can't be measured. Research results are all over the place.

      The general public perception that mean reversion quickly and faithfully corrects market shocks is way ahead of the research.

      Either way, this is not a "Monte Carlo problem". Monte Carlo models can model mean reversion with autoregression. Tharpe's paper doesn't do that, though he does throw mean reversion out there as an explanation of his results with no argument to back it up. (There's a far simpler explanation.)

      Lastly, there is no work that I am aware of that shows the impact of mean reversion – again, if it exists – on probability of ruin. 4% Rule failure is 100% explained by sequence of returns, not by the returns, themselves.

      Other than that, as they say, I totally agree with you!

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