Yes, using a formula that accounts for higher moments is important (whether 3, 4, 5 or 6). But I still think that Kelly blowups usually happen not because of the wrong formula, but because of a poor appreciation of the uncertainty of returns.
I'm going to labour the point, in case there are people reading this thread without the same appreciation of the nuances that you obviously have.
The first order problem is that if are over confident about your mean versus your standard deviation (there I won't use the 's' word again); as I've already discussed. It doesn't matter how you update your estimate of optimal Kelly, you're going to need thousands of data points just to know that there is a 95% chance your optimal Kelly is somewhere between 2% and 4% (using more realistic numbers).
The second order problem is you have a negative skew / evil kurtosis, but don't realise you do, because you haven't yet had your LTCM moment. So you get a long string of small positive returns. Even the fanciest Kelly formula will be taking you to fill your boots.
Or if you prefer
There is a 95% chance that you win 1% of your bet ;
There is a 5% chance that you lose 10% of your bet
There is a 0.01% chance that you lose 100% of your bet
... comes out to around 97% (using excel solver, with rounding, for the avoidance of any ambiguity)
But if the true distribution is actually:
There is a 95% chance that you win 1% of your bet ;
There is a 4.8% chance that you lose 10% of your bet
There is a 0.2% chance that you lose 100% of your bet
... optimal comes in around 55%
But then is why we use half kelly, right? We'd just about get away with it.
But if the true distribution is actually:
There is a 95% chance that you win 1% of your bet ;
There is a 4.5% chance that you lose 10% of your bet
There is a 0.4% chance that you lose 100% of your bet
Then you should be using something more like 17%.
My question is does anyone really have a well calibrated idea of whether something has a 0.01%, 0.2% or 0.4% chance of happening? I refer you to the behavioural finance work on this, plus everything Taleb has written.
Even if the underlying distribution of returns is stable (a massively unrealistic assumption as I've already said), you'd need tens of thousands of trades to narrow down a sufficiently narrow window for that figure.
(this is a more long winded way of saying that having to estimate higher moments doesn't make your life any easier)
And in real life, as I've already said we don't have a stable distribution or enough data; nine times out of 10 we're LTCM in 1998 or the CDO guys in 2006 and the -100% currently has a realised probability of zero. So you have to guess what the odds of -100% are. If you're tied into a gaussian thought process you'd not bother assiging any probability, since even on the last set of numbers -100% is a 15 sigma event. Even if you manage to get yourself to confront the remote possibility of a total loss how do you know whether that possibility is 0.2% or 0.4%?
The only way out of this conundrum is to avoid trading anything with such evil skew (or anything that's likely to have such nasty skew in the future), or if you absolutely must do so then use an extremely conservative fraction of optimal Kelly. One Kelly formula might tell you that optimal is 90%, another might say 80%, but in certain situations you'd be insane to use more than 5%.
GAT