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Old 09-11-2014, 09:52 PM   #41
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Old 09-17-2014, 01:38 PM   #42
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Originally Posted by FIRE'd@51 View Post
This approach makes a lot of sense to me. It seems to to capture the best of both approaches. There is no need to estimate a covariance matrix with all its associated problems, mainly the non-stationarity of the parameters. It retains the sequential correlations from market/inflation cycles built into Firecalc while reducing the serial correlation from data overlap. If I understand bootstrapping correctly, it makes all 30-year paths equally probable in a single drawing.

Can you tell us the difference in succes rates you get using Firecalc and the MC/bootstrap process for the same portfolio mix, e.g. a 75/25 equity/bond mix?
For those that are still interested:

I ran a comparison of FC and bootstrap for a 50/50 SP500/10 year treasury bond mix over 30 years. My FC simulator results are a little different than the online version due to different bond calculation assumptions, but they are in the same ballpark. Disclaimer: although I've done my best to make sure my simulator works correctly, there could still be some things that I have missed.

Here's the table of fail rates for various withdrawal rates using the two different simulators:
HTML Code:
3%     3.5%     4.0%     4.5%     5%    
0.0%   0.0%     0.0%     26%     40%        FC
1.8%   5.5%    13.5%     24%     38%        Bootstrap
The withdrawal rate for 5% failure is 4.1% for FC and 3.4% for bootstrap.

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Old 09-17-2014, 02:44 PM   #43
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Originally Posted by Fred123 View Post
This topic has come up before, and I'm always surprised at the aversion some people here have to MC analysis and the overconfidence they have in FC (which relies on perhaps 5 independent samples if you are looking at 30 year outcomes). Some of this seems to be based on a misunderstanding on how MC works if historical data is used to model the distribution. For example, there aren't "a s***load of parameters" used, there are two (mean and variance) for normal or log-normal distributions and maybe one or two others if a "fat-tailed" distribution is used. There are no parameters used at all if bootstrap sampling is used.

But, more importantly, what are the alternatives? As a famous statistician once said: "all models are wrong, but some are useful."
There may only mean and variance. But figuring out what the mean and variance is requires plugging in hundreds of numbers and making some SWAG (scientific wild ass guess). So for instance are the average return for US equity based on starting with 1871, 1921, or 1946 (arguments can be for all dates). Or do you decide like Dr Pfau that US equity outperformed most markets (although not places like Australia and Canada) so going forward US equities should perform more like the rest of the world. Or do you decide that the stock markets in the rest of the world have evolved to look at lot more like the US so the ROW equity return should look more like the historical returns of the US.

There was the potential for a complete collapse of the economic system in the fall of 2008. So valuing stocks was a difficult challenge..By March of 2009 the danger of complete collapse had passed and we were just dealing with a deep recession. I confidently predicted at the time the market would double in five years due to fundamentals like earnings, book values, dividends, yada yada. I'm certainly not making that prediction for the next 5 years, nor are any other value investors. Likewise Warren Buffett, in summer of 1999 warned the future stock market returns for the next 5 years were going to below average.

Why because future stock market returns are highly influenced by the returns of the past couple of years. Something that Monte Carlo simulations ignore.
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Old 09-18-2014, 03:03 AM   #44
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the ignoring of that influence of previous years is just what monte carlo simulations want to rule out. the previous combo's in that order will likely never play out that way again.

in fact we are in one of them now that never happened before and is uncharted , low rates and high stock valuations only appeared in monte carlo simulations prior never in historical data.

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