SK10 vs 25D RRN

You should write a book, if any time you stop working in your current gig. Not many text or trading books have as much insight

This is just the tip of the iceberg. Most people aren't sharing their real knowledge in this industry. Everything is siloed.
 
So I ran the SK10 calculations for WTI Crude Oil - SK10 = [Vol(ATM) – Vol(ATM – 10%))/sqrt(DTE). I have a real-time data grid that shows me the Normalized 25D RR [IV(25delta) - IV(75delta)] / IV(50delta) live for every automated curve fitting publication. Although I think the SK10 helps characterize the downside skew across all expirations of the terms structure, it only tells half the story. Because the SK10 measure only includes the downside strike (which may be appropriate for stocks or equity indexes), if there is a flattening or steepening slope in the calls (like we see in commodities or FX) then one measure may conflict with the other, which it does in this case.

According to the SK10 calculation, My front month expiry Mar19 has the most negative SK10 figure (-.206) vs the further out months, implying that its put skew is the steepest vs the ATM, which it is. However, my Normalized 25d RR reading of (-7.37) shows that Mar19 has the flattest overall curve when comparing the OTM 25d puts vs the OTM 25d calls. This is due to the OTM calls (upside strikes) trading much higher vs the ATMs in Mar19 relative to the other further out expirations.

It's clear that the Normalized 25d RR indicates a more accurate picture of the overall slope and shape of vol curve across all months under one product. The SK10 measure only gives me relevant information for half of the vol curve. Is there an SK10 formula that includes upside strikes? What would be the mathematical formula for the correct moneyness for an upside strike (OTM call) for a log-normally distributed underlying. What would be the SK10 equivalent for an upside strike? It would have to be some strike more than (>10%) 10% OTM, or less than 90% moneyness.

How does Skew calculation calculated from historical returns compare with the Skew calculated from options. In my opinion all these market based probabilities are not much better than simple historical calculations.

In your other thread I posted equity skew from Minny FED. This one is the same for crude.

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How does Skew calculation calculated from historical returns compare with the Skew calculated from options. In my opinion all these market based probabilities are not much better than simple historical calculations.

In your other thread I posted equity skew from Minny FED. This one is the same for crude.

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Not sure if observed skew behavior is a good predictor of future volatility (standard deviation on your chart). From what I've seen, they usually move in tandem with each other, with demand for downside or upside (changes in the skew) often lagging the violent moves in the underlying. For example, the OTM put vols don't explode in anticipation of a market gap decline. The panic to purchase puts will happen only during or after a market meltdown. And the OTM calls will only catch a bid (IVs increase more relative to same delta put IVs) when a sustained rally has been confirmed by the market.

Also, when futures are range bound and volatility is creeping in, the skew will behave independently of volatility
 
How does Skew calculation calculated from historical returns compare with the Skew calculated from options. In my opinion all these market based probabilities are not much better than simple historical calculations.

In your other thread I posted equity skew from Minny FED. This one is the same for crude.

View attachment 197309

Just curious, how would you calculate Skew from the historical returns of the underlying? That doesn't sound possible.
 
It is just a third moment, Variance is a second moment and Kurtosis is a fourth moment

Got it. You're just taking the normal distribution of historical returns of an asset and determining a Skew from observed data. Well, as you know and its been proven that implied (expected) volatility is a much better predictor of future volatility than historical. Although implied vol typically trades at a premium across all products and is often mis-priced, it's much better to look at a forward looking instrument rather than something that happened in the past to determine the expected future behavior of an underlying.

The Skew Index is a "noisy" indicator, and so far seems to be a lousy predictor of a "fat tail" event. I don't think you can gather accurate probabilities of a Black Swan event by just measuring the steepness of far out winged puts (5 delta <), which I believe the CBOE is doing. Sub 10 readings of the VIX are probably a better predictor of a future "tail event."

The Oil Skew Index (if it had one).I think would in general be a lagging indicator, as it prices in a "fat tail" event when it's actually occurring. There's opportunity in commodities to ride the panic wave before it peaks, and then "fade" it during market capitulation. Easier said than done. It's a game of musical chairs.
 
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I suspect that he intends to make money, but I could be mistaken.

Thanks genius. I must be mistaken, tracking/plotting 25d risk reversals is a surefire way to make money. Everyone can go home how!!
 
Thanks genius. I must be mistaken, tracking/plotting 25d risk reversals is a surefire way to make money. Everyone can go home how!!
Well, the general idea is exactly that - 1. get information, 2. process it, 3. profit. The rest is details which nobody, myself included, wants to share. I wonder why?

How does Skew calculation calculated from historical returns compare with the Skew calculated from options. In my opinion all these market based probabilities are not much better than simple historical calculations.
There are several other ways of looking at historical skew realization, some better than other and looking at different metrics. Here is one, albeit a bit labour intensive for linear skew in equities. First, build a model for directional dependence of realized volatility using following regression:
Code:
log( RV[t, T| T] / IV[t, T|t, k=ATMF] ) ~ A * log( S[T]/S[t] )
Now, for any given point in time, you can compare the slope A from the historical model to the "local vol" model B which would be something like
Code:
log( 0.5 * IV[t, T| k=K, t] / IV[t, T| k=ATM, t] + 0.5 ) = B * log(K/A)

You can think of more ideas along the same lines, I am sure.
 
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