Indexing In Monte Carlo

Now that we know the basics of indexing, we can dive into a much more interesting topic: Does it work?  We’re going to use a hypothetical contract (it’s actually a real contract from which I have borrowed heavily, but we won’t name names) where there is a minimum interest rate of 2% per year and a maximum of 12%.  I’m going to attack this from two different approaches, one will be a model based on Monte Carlo methods, and the second will be a historical analysis of 140 years of annual growth in the S&P 500 index.

Monte Carlo

The Monte Carlo model used 10 scenarios of a 30 years period of randomized returns based on the tendencies of S&P data we’re most commonly aware (a mean of 10% with a standard deviation of 15%).  Normally I’d run several more observations but the labor intensive process of adjusting the hypothetical interest rates made me decide this was enough.

Going back to our index parameters, anytime the index was below 2%, we simply used 2% as the credited interest rate and if it were anything over 12% the rate was simply 12%.  The averages were then calculated for each 30 years period..

I then used this data to construct savings scenarios using $100,000 and calculating CAGR over the same 30 years.   The average CAGR was 7.9% with a standard deviation of 0.78%.  Meaning, when we introduce indexing, it creates a very tightly disbursed data set around the mean.  We end up with a 100% probability of a 30 year CAGR of at least 6%, 90% probability of at least 7% and 80% probability of at least 8%.  Compare that to the results we got from the fully invested S&P Monte Carlo from a little while ago.

Historical Example

Since the Monte Carlo was a little lite on examples, I decided to go back and use a 140 year historical example of the S&P 500 and perform a historical analysis given the indexing parameters.  After 140 years we get a CAGR of 7.71%, not too shabby.  On top of that, if we drill in a little deeper to the per year return, we discover there’s a 58% chance every year of having a better than 10% interest rate return according to the historical data.  Keep in mind this is a 140 year historical, which doesn’t rely on just the 80’s and 90’s to boost it’s rate of return.

And what would have happened during the lost decade?  The first decade of the new millennium that has caused a lot of frustration for equity investors, a period where the major indexed posted no gain from a CAGR stand point.  My hypothetical posts a 6.41% return.  Remember this point we made months ago?

Life Insurance or Annuities?

Indexing can be found in both life insurance and annuity products.  Life insurance will offer higher caps like the example found here, in most cases.  Annuities require a lot more hedging an typically have lower caps, but not always.  So, if your sitting on a portion of qualified money for which you think this might be appropriate, you can get into something and keep the move tax free (in fact, I’d recommend staying away from anyone trying to sell you on a so-called 7702 plan as there’s no such thing).

So indexing has a strong potential to give you strong reliable returns.  Again the point made last time is no less important.  Just like any product there is good and bad, so prudence and a competent agent/broker are important.


2 Responses to “Indexing In Monte Carlo”

  1. [...] not timid in screaming from the roof tops that they both did this very thing.  We also know that indexing has done very well over the past decade, and that’s primarily made possible by the use of financial derivatives [...]

  2. Budrow says:

    This information was very beneficial, thanks.

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