tag:blogger.com,1999:blog-5621914599310831423.post8375245859409229653..comments2020-05-22T04:51:32.246-07:00Comments on The Retirement Café: The Limits of SimulationDirk Cottonhttp://www.blogger.com/profile/05616143752082768155noreply@blogger.comBlogger9125tag:blogger.com,1999:blog-5621914599310831423.post-70145767124873321172018-04-29T16:59:47.357-07:002018-04-29T16:59:47.357-07:00Paul, I'm going to respond to your post from t...Paul, I'm going to respond to your post from the bottom up.<br /><br />First, no need to apologize for length; its a great comment.<br /><br />Second, there <i>should</i> be a limit on how much comfort you draw from a model. They can provide a lot of information for making planning decisions but they can't tell your future.<br /><br />The problem with modeling shocks is two-fold. We have no distribution to model. Also, shocks are tail risk. Nassim Taleb tells us that it is impossible to predict low-probability events. In Congressional testimony he called those who try "charlatans." <br /><br />You can imagine severe shocks but then you can always imagine worse. It's better to address tail risk by thinking about bad scenarios and how you might handle them. Perhaps you could handle a $100K medical expense shock but what would you do with a $1M shock? At some point you will need to accept that risk or insure it (if insurance is available and affordable). Modeling won't help much here. Expense shocks are more difficult to predict than the stock market (that's why we call them "shocks.")<br /><br />A few comments on your first paragraph. Correlation of asset returns is helpful under normal conditions but, as the saying goes, all correlations go to 1.0 in a market crisis. (Tail risk, again.)<br /><br />No one knows for sure "to what extent the market exhibits inter-year correlation." There is only weak evidence for mean-reversion and even if it exists, how long it will take to revert is unknowable. I recently read a paper showing that, regardless, it isn't a winning factor for a market strategy.<br /><br />We can model all of these things but I'm not convinced any of them would change the outcomes enough to make you change your retirement plan. Modeling provides information about what happens <i>most</i> of the time. It doesn't really "do" tail risk. You need to plan that in other ways.<br /><br />Thanks for writing!<br /><br />Dirk Cottonhttps://www.blogger.com/profile/05616143752082768155noreply@blogger.comtag:blogger.com,1999:blog-5621914599310831423.post-35386984531502307912018-04-29T02:05:07.785-07:002018-04-29T02:05:07.785-07:00Thank you for the article. As you have noted befor...Thank you for the article. As you have noted before, correlation factors are hard to model but I wonder if their ommission isn't at least as important as a too simplified probability function, especially for the more pessimistic outcomes. I dont know to what extent the market exhibits inter year correlation, but if it does this presumably is a consequence of an underlying factor such as participants belief that they have just experienced a bubble and asset prices remain too high, in their opinion, or inflation, for which a year of high inflation is likely to be preceded and followed by years of elevated inflation too. The correlation therefore arising from the correlation in the underlying factors.<br /><br />I wondered about injecting a pre-programmed ' shock' into my personal spreadsheet model (the model is now rejected thanks in part to your observations :), such as a 30% fall followed by a multi-year slow recovery. The same could be done with a Monte Carlo model. However, for a relatively short timescale, eg 35 years, adding a (say) 5 year event would likely dominate the results which would just introduce the additional uncertainties of how to model, and time, the enforced shock.<br /><br />This isn't just academic since I think this introduces a bias into my thinking especially with respect to protection against inflation. I am chewing at the bit to invest in a direct holding of IL gilts UK resident) and have recently converted a DB pension into a DC fund as the former only offered uplift to a maximum of 5% inflation.<br /><br />So for me, this is a limit on how much guidance or comfort I can draw from a model.<br /><br />Sorry for the long post.<br /><br />Paul<br /><br /><br /><br />Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-5621914599310831423.post-49291425449534593902018-04-24T16:45:23.310-07:002018-04-24T16:45:23.310-07:00You are generally correct but your plan will be un...You are generally correct but your plan will be unique to your household circumstances. It would be extremely difficult for me to give you a brief response. Instead, why don't you email me at jdcplanning@gmail.com and let's set up a time to talk and get you started on the right foot.Dirk Cottonhttps://www.blogger.com/profile/05616143752082768155noreply@blogger.comtag:blogger.com,1999:blog-5621914599310831423.post-64686732988881534282018-04-24T16:36:18.939-07:002018-04-24T16:36:18.939-07:00This is all very humbling and I know you've ad...This is all very humbling and I know you've addressed this in multiple past posts but I could use a refresher on your recommendation for the best course of action when designing a retirement portfolio. I believe you are an advocate of safety first which means to have a floor, I think for expenses that are deemed 'necessary'. And then invest any remaining assets in the market, stocks and bonds? Annuities? I think Wade recommends annuities over bonds? I do find these posts to be thought provoking but where I always go is: what should I do? Thanks for your thoughts and please spell out any recommendations. I'd be interested in Larry's approach too. Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-5621914599310831423.post-22067991625410952502018-04-24T09:47:38.995-07:002018-04-24T09:47:38.995-07:00Thanks, Larry. Not sure what the quibble is, I thi...Thanks, Larry. Not sure what the quibble is, I think I'm in complete agreement with you here. As I said in the post, MC simulation is better at making relative assessments than absolute. Relative assessments are valid decision-making arguments. <br /><br />My issue with the frequency-based probability measure is that successful scenarios were exposed to significantly different levels of risk. If I win $5 from a very risky bet and you win $5 from a safe bet, we both won $5 but your path was far preferable. The frequency measure considers our experiences identical (we both won $5).Dirk Cottonhttps://www.blogger.com/profile/05616143752082768155noreply@blogger.comtag:blogger.com,1999:blog-5621914599310831423.post-75626521766571301312018-04-24T09:37:25.384-07:002018-04-24T09:37:25.384-07:00I agree wholeheartedly Dirk. Counting the frequenc...I agree wholeheartedly Dirk. Counting the frequency of failed outcomes, i.e., failure rate of iterations, doesn't characterize risk very well. However, it does lead to more informed decision making where 15% failures relative to 10% failures would hint that the 15% scenario is riskier relative to the 10% scenario. So there is information to be had, but it is NOT to be interpreted as to THAT is what is going to happen (to your point).<br /><br />The current practice of simulation interpretation is flawed as I explained in this post <br /><br />https://blog.betterfinancialeducation.com/sustainable-retirement/part-ii/ <br /><br />The solution that is sought is NOT what you see on the right side of a simulation graph, but using Monte Carlo to actually derive an answer (the solution) based on asking the simulation to solve for the answer to the question. In retirement the question is "How much can I spend if I have $x?" or the corollary question, "How much do I need to save in order to spend $Y?" In order to answer these questions, one needs a common denominator so any simulation set can be properly compared to another simulation set. Using the same failure rate as a comparison helps compare two differing portfolio allocation characteristics for example. Rising failure rate of a plan when markets misbehave and portfolio values decline as a result is also a good way to predetermine ahead of time what reduced portfolio value should trigger what kind of decision (a decision that would actually affect outcome, not a market timing decision).<br /><br />A small quibble Dirk, but decision making information can be taken from simulations (not that you said otherwise). Looking forward to seeing where your series of posts for this line of thought go!Anonymoushttps://www.blogger.com/profile/17516328219004197356noreply@blogger.comtag:blogger.com,1999:blog-5621914599310831423.post-45338062687758457782018-04-24T09:08:29.477-07:002018-04-24T09:08:29.477-07:00William, your bottom line is, in fact the bottom l...William, your bottom line is, in fact <i>the</i> bottom line.<br /><br />The argument you cite, however, has a large and obvious flaw — the presumption that a mere 200 years of so of market returns data holds the best and worst outcomes that the market will ever produce.<br /><br />You're predicting a 100% success rate under the assumption that nothing unprecedented happens in the future when, in fact, we see unprecedented outcomes regularly. The assertion that a 100% success rate is possible is a frightening thing, in itself.<br /><br />That same data could be used just as convincingly to argue that since 6.5% of Monte Carlo simulations have never been experienced in history that history likely hasn't shown us its worst, yet.<br /><br />Importantly, you're treading down an unconvincing logic path when you refer to "Monte Carlo simulations." Monte Carlo simulations are based on models and Monte Carlo simulations based on other models will provide different results than you share. You're treating simulations as if all Monte Carlo simulations provide the same results. They don't.<br /><br />Since we can't know whether future results will be bound by our limited historical data, we can't know if the models are optimistic, pessimistic, or spot on, based on historical returns, so I see little value to arguing the point. Regardless, I wouldn't characterize 6.5% of returns, even if correct, as "far more extreme." <br /><br />My judgment that MC models (not Monte Carlo simulation, itself) tend to be overly optimistic has nothing to do with the simulation, but that counting the frequency of failed outcomes is a poor way to characterize the risk.<br /><br />Thanks for writing!Dirk Cottonhttps://www.blogger.com/profile/05616143752082768155noreply@blogger.comtag:blogger.com,1999:blog-5621914599310831423.post-16688298453150748062018-04-24T08:22:24.312-07:002018-04-24T08:22:24.312-07:00At Michael Kitces blog, Derek Tharp demonstrated t...At Michael Kitces blog, Derek Tharp demonstrated that Monte Carlo analyses actually tend to be too pessimistic with regard to 'worst case' scenarios, primarily because most of them do not incorporate mean reversion, which is a well known phenomenon in financial markets.<br /><br />"Monte Carlo projections of a long-term retirement plan using typical return and standard deviation assumptions are actually far more extreme than real-world historical market scenarios have ever been!<br /><br />For instance, when comparing a Monte Carlo analysis of 10,000 scenarios based on historical 60/40 annual return parameters to historical returns, it turns out that 6.5% of Monte Carlo scenarios are actually worse than even the worst case historical scenario has ever been! Or viewed another way, a 93.5% probability of success in Monte Carlo is actually akin to a 100% success rate using actual historical scenarios!"<br />https://www.kitces.com/blog/monte-carlo-analysis-risk-fat-tails-vs-safe-withdrawal-rates-rolling-historical-returns/<br /><br />There is nothing that can be done to eliminate all financial risk. This is a hard pill for many to swallow, but it's the truth.Williamhttps://www.blogger.com/profile/00343019917339951141noreply@blogger.comtag:blogger.com,1999:blog-5621914599310831423.post-51976433409525497082018-04-23T19:02:20.574-07:002018-04-23T19:02:20.574-07:00Michael Kitces has said paraphrasing, "I'...Michael Kitces has said paraphrasing, "I'd rather be approximately right rather than precisely wrong." Your list summarizes this well.<br /><br />Dimensional has long argued that models aren't meant to predict (which is impossible as we all know, but we then go on to try anyway). Their recent white paper "Mind Over Model, March 2018" says "Checking the weather? Guess what—you’re using a model. While models can be useful for gaining insights that can help us make good decisions, they are inherently incomplete simplifications of reality. ... In the end, there is a difference between blindly following a model and using it judiciously to guide your decisions."<br /><br />They are meant to be decision tools to refine past decisions into future choices ... rinse and repeat.<br /><br />Really enjoying your current train of thought Dirk!<br /><br />Anonymoushttps://www.blogger.com/profile/17516328219004197356noreply@blogger.com