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The twelve $10 moves are marked in red. These all occurred within 2 month periods. I've also counted 10 2-month periods where the price traded within a $10 range.

Notice how the maximum formed by the tip of your red triangles are roughly occuring every 9-10 months. That would be, I believe, the top basis element.
Hey, I have no idea, man. I just run the numbers. All that technical stuff is supposed to be your bailiwick. SAT seems to know something about that, too. Stochastics, I think you call it.

I've got one more I'll throw in here at the end of the line. But after that I'm done.

Your graph shows exactly how the frog gets boiled and never notices.
I don't know much about boiling frogs. I'm not a vivisectionist. Nor a very good cook.

But I will tell you this. If you were to substitute Americans for those frogs you speak of. And if you substituted gasoline prices for the boiling water. You'd probably be right.

yep...that was the analogy I was going for.
That's a weird set of curves - I'd expect to see a yearly, or even an overall bi-yearly, to time with US elections, cycle. Instead, 9 months? The only 9-month cycle I can think of is human gestation and that has nothing to do with gas, humans in the 1st world don't even plan for the seasons these days.
Here's how I would calculate it. Historically, oil markets have had a volatility of about 30%. In the past few years this has gone up somewhat to about 35%. Source:

http://www.econbrowser.com/archives/2005/07/100_a_barrel_wh.html

This is a one standard deviation measure of how much prices are likely to change in a year. For shorter periods, we reduce it proportionately. For two months, 1/6 of a year, this is about 6%.

Now, that's one standard deviation. In general there is a 68% chance of staying within 1 SD, or plus or minus 6% price change. 68% of the time, prices will change by less than 6% in a 2 month period.

Two standard deviations would be twice as much, and correspond to 95%. 95% of the time, price will vary by no more than plus  or minus 12%.

Three standard deviations would be plus or minus 18%, and corresponds to 99%. 99% of the time, prices will change by no more than 18% in a two month period.

In this cases, prices fell from about 78 to 61. This is a drop of 22%, or more than a three standard deviation change, in fact almost four standard deviations. It should happen something like one in ten thousand times. Either oil price volatility has increased markedly (and I don't know that we see much evidence for that prior to the recent change) or else this was an extremely rare occurance.

Well, I suppose that's no surprise, we already knew it was rare, prices haven't fallen this much this fast in many years. This just shows how rare it is.

Now, one caveat - these statistics are based on a normal distribution. However financial prices are slightly "leptokurtic", a fancy word that just means "fat tailed". (If you like to live dangerously, try telling your girlfriend she's leptokurtic and say it's latin for beautiful.) What it means is that extreme moves are more likely than would be the case for a classical normal curve. Market psychology is more likely to lead to extremes than the kinds of physical processes that are often described by normal curves.

The bottom line is that this 22% drop in two months is in fact rare, but maybe not quite as rare as the calculations above would suggest. Four SD drops do happen in the market but they are certainly noteworthy.

And let me point out that Khebab's prediction graph shows us climbing back up to around 85 in November. An increase from 61 to 85 in two months would be 6.5 standard deviations! That would be even that much more remarkable than the drop we've already seen.

Speaking of fat tails, should we maybe take a moment and recognize that a guy just blew away $6 billion on a bet with probably a similar prediction curve such as this for natural gas futures.
Yep. His big mistake was sticking to his guns and adding to his positions even as the tsunami came closer. One other note: I don't think anyone has commented on the "pull" the crash in NG prices has had on crude (the two are somewhat correlated in price).I would not be surprised if the crude/NG ratio is at an all-time high.  
That is not correct. The standard deviation varies with the square root of the trading period. So if 12 months gives you 30% then 2 months is about (30x 30=900/6=150 SQRT = 12,25%)
Then I have to add, that 78 Dollars a Barrel certainly isn´t the mean.
Thanks for the correction. I got confused because volatility when defined as the square of standard deviation is in fact proportional to time, and sometimes traders use the term 'volatility' when they are talking about the SD. Looks like Khebab did the calculation carefully below and got a more reasonable answer.
It is such a pleasure to be part of a civilized debate. I learn a lot here at the oil drum.
Thanks for the link Halfin! the Black-Scholes model is truly a beautiful model!

If I look at the lognormal distribution properties:

the variable mu is a linear dependent function of time in my case (log(Price)= 0.2615*(t-2002)+3.1). We have the following relationship for the mean and the variance of lognormal distributed variable X:

so the standard deviation is proportional to the mean. The coefficient in my case would be sqrt(exp(0.091^2)-1)= 0.0912= 9.1% wich is not far from the 6% you are using. So volatility would be given by the following function:

0.0912 * exp(0.2615*(t-2002)+3.1 + 0.091^2/2)

using this formula for mid-September 2006, we get a volatility between $6.89 and $7.03. The 95% confidence interval would be [$61.2, $89.2]. The recent drop of $22 is 22/75.2= 22.6% which is 22.6/9.3= 2.43 SD. The move toward $85 would be 3.44 SD with my value.

So you are still saying highly unusual moves. (?)
I stick to my square root: this is the random walk hypothesis: Rather than cruising at a constant speed such that the position deviates proportionally with time, a random walk is erratic with steps in both directions: positive and negative. Since steps have random +/- signs, their square is always positive, and thus the sum of squares of the steps is increasing in proportion to time.
Wow, tex created maths posted in a blog. Lovely :-)