Johnny B. Goode said:

**Shouldn't this be in the Options forum?**

Thanks again.

I think I understand what you are doing. A few more questions, if you will allow me.

1. How big of a sample of data do you use in making your historical surface projection?

2. Are you only looking for spreads in vol of 2 or more standard deviations from the mean before you trade? Have you done back-testing on what strategy is optimal for this? (Is it profitable with 1.5 std devs, etc?)

3. Do you scale position size with deviations from the mean spread too?

Thanks again. I really like the idea of your strategy. I am considering programming a system like this and one based on a modified options pricing model (maybe something like Jump Diffusion) to find relative mispricings within and across highly-correlated markets (ie. sp 100, 200 , 500). These appeal to me in particular because I think that they could be done virtually without any human intervention.

Hi, no problem,

1) When looking at the historical surface data I go back 3 years. We have seen a lot in that time so I consider it to be a fair sample.

2) I have done back testing and run various (linear) optimisation models but have concluded that such curve fitting is dangerous. 2 stdevs does not throw up that many opportunities (by definition) but is very low risk. I might run it on 1.5 if I had a portion of money with a different risk profile as this would (on the backtest) still be very profitable.

3) No, it is always constant.

I admire your quest for a lack of human intervention and am 100% systematic in my approach as well.

As for Jump Diffusion, don't waste your time. It is a nice academic idea (and was ahead of its time in 1976) but it lacks practical application. While assumptions of geometric Brownian motion are obviously flawed the requirements to assume a number of annual jumps and the % of vol explained by those jumps is too abstract.

It is comparable to creditmetrics where people assume a given number of default events. Unfortunately, jumps, like default events are often big, totally stochastic and tend to come several at a time.

I would stick to Black Scholes or, if you have the computational power, a binomial model.

If you do some research on trading discrepancies between highly correlated markets I would be very interested to read it. Correlation is the new volatility :idea: There is a lot of work to be done on it. I am working on a currency option correlation model right now. Trading the correlation between 2 pairs. The beauty is that there is an upper bound (1) and the implied forward correlation often assumes that high values will persist. The attached chart shows otherwise!