Interpreting Inverted Implied Volatility Surfaces

# What interpretation can I derive from an inverted volatility surface?


# What interpretation can I derive from an inverted volatility surface?












While pulling some reports from Bloomberg today I came across the volatility surface for NYSE: ONDK, set for an earnings call next Monday. From what I've seen before (not much, 1 month on the job and counting) volatility should trend upwards with longer terms, and present in the form of a "smile" when you plot volatility against moneyness. The surface for ONDK had volatility dropping down with term, and presented more of a volatility frown than smile.

What exactly does it mean when volatility's highest at close-to-the-money options? And what could drive decreasing volatility with term?

## Answer by FinanceGuyThatCantCode (score 2, accepted)

https://quant.stackexchange.com/a/34081

I just checked this one. I saw the picture you are referring to and I think the frown is not real. Much of it looks like noise created by wide bid ask spreads and extremely high implied vols. The data here is very poor and the options are spread very far apart. The underlying is $4.60 and the options are struck a dollar apart. That would be like SPX having strikes every 500 points more or less. So the smiles only have 0-2 useful data points in the front months and then BBG's algorithms created a smile off of those points. I know very few people that are detail oriented enough to make good smiles with such sparse data. I am probably the only person I ever met anal enough to try to do this right though I am sure there are others I have not met.

I like to fit the various smiles in a very specific order. I assign a metric to each expiration that measures how much "information" there is in the smile. For that metric, I consider the range of useful strikes in delta space, the number of strikes available to me in that range, and how tight the bid ask spreads are roughly on average. Then I first fit the smile that has the most "information" according to my metric. Then given the last smile that I fit in the surface, I inductively fit the next smile while trying to maintain forward volatilities between this smile and already fit smiles that are sensible and without arbitrage. By enforcing no arbitrage, we actually get smoother term structures cross sections as we vary the strike by moneyness - that to me is very valuable even though I don't price too many exotics. The smoothness of these term structure cross sections makes the term structure of my parameters fitting each smile much smoother. Smoothness of parameters make the parameters meaningful and useful for other things.

My point - doing this kind of detailed work would give you the smiles you are more familiar with.

Another potential issue - and this is a big one - I see the short interest in BBG says that 12.52% of the float is currently short. This means that the cost of borrow is likely to be high and BBG is probably calculating vols under the assumption that borrow cost is not unusual. A very expensive borrow cost will change the forwards - think of the borrow cost as a continuous dividend yield. This can distort vols a lot, but I cannot confirm how expensive this borrow is.

As far as term structure vols go, usually term structures of vols are increasing as you say, but when there is event risk (like earnings) the shorter dated vols might be higher than the longer dated vols. Also, during periods of extreme stress, the vol term structures tend to be decreasing due to the mean reverting nature of vol. In other words, usually the period of stress is short lived so the short dated vol should be very high - and the longer dated vol starting a few months down the road should revert to more normal level - i.e. the forward vols starting a few months out are much lower.