As a newbie, I don't think we have enough info for making any trading decision based on the statistical distribution. How are options priced? It never prices on hv. If it is priced based on statistical volatility, then obviously the atm is underpriced. However most of the time, IV is higher than HV, and so it is not easy to draw any trading conclusion from the graph. I know the answer is naive, and wish you can provide a better answer for me.
Coach: I actually get Mike Parnos' newsletter as part of a package deal. I have seen his discussion on the guts strangle before but it was quite some time ago and I didn't have time to really read it. Since he has chosen to openly publish this strategy, I'll see if I can find any additional info on the strategy. Besides it doesn't sound like it is proprietary. Mike generally does not adjust his iron condors or spreads and that is probably why there is no discussion on this. He usually just gets the funds out as he puts it.
This kind of comment is totally un-needed. You obviously have some insight. Why not elaborate on the statistical matter at hand rather than trying to kill the discussion?
I don't think your answer is naive, I think that you are clearly working hard to become proficient in a complex area. I hope that the same can be said of me. I bring up this difference between the assumptions underlying the B-S formula (i.e. logrithmic price distribution without jumps) versus reality (leptokurtotic with jumps) to to try to stimulate discussion around whether there might be better strikes to sell and buy at. There is a school of thought on this thread that the sum of probabilities times pay-offs is equal at all strikes. This is the fundamental underpinning of the BS equation, and is the argument against FOTM credit spreads (also the gamma risk). Based on the difference between logarithmic distribution and the leptokurtotic reality, it would APPEAR that buying ATM or FOTM and selling OTM would have an advantage. I'm not saying this is true, just that the model underlying many calculations (the B-S model) is only a model. Ugly
Hi all, do any of you fine people have any experience or insight about artificial intelligence trading systems. I have received numerous calls attempting to recruit me into one such system being offered by "my trading futures.com" aka mind engines. Sounds intriguing but I cannot find any reviews either positive or negative about this. Any serious feedback would be much appreciated. Ty in advance. Lloyd
Just thinking out load... It's not quite that simple. Buying ATM can be very costly when the underlying finishes near that ATM price more often than anticipated. To compensate for that lost time premium, one could sell twice as many OTM options. Then one completes the butterfly by buying the undervalued FOTM. To gain even more edge, one buys an extra FOTM. Thus, your data suggests that a good position might be: Buy one ATM straddle and sell 2 credit spreads - long the FOTM and short the OTM (1 or 2 sigma OTM) in both puts and calls. I'd hate to attempt this, as the intiial debit would be too high to suit me. I believe the best piece of information one can draw from the data you present is that selling FOTM is a losing proposition. Mark
If you were able to develop such a system and it proved itself capable of beating the market, would you offer to 'recruit' others, or would you keep the secret to yourself and make a few billion using it? Mark
Mark , if I understand correctly , haircut is also perfect for long gamma scalping. For an owner of the long combo , any buying ( or selling) of stock is "margins free" because prop co ( like Mav's) recognizing stock as a part of delta neutral position .
Exactly, it would seem to imply you could buy butterflies on either side of the current underlying with the body at 1sigma, and expect.......I want to say positive expectancy, but I'm afraid........an advantage. Similarly, selling at 1 sigma and buying at >2 sigma to create a credit spread might take advantage of this. Again, I don't think this is actually possible. I agree that the most obvious thing to take home is that unusual events - the ones that make CSs lose money occur more commonly than logarithmic distribution implies - thus the skew.