How Implied Volatility Depends on the Pricing Model
Summary
The document asks how useful implied volatility remains when the Black–Scholes assumptions do not fit the market. It uses stock-borrowing fees as an example: different borrowing and lending rates can change the pricing equation, so an option price must be interpreted through a model that includes those costs. The resulting implied volatility may differ from the Black–Scholes value, and smiles from different models may not be directly comparable.
The discussion highlights a practical tension between using a familiar volatility measure and representing financing and stock-loan conditions explicitly. A model can potentially fold those effects into adjusted inputs, but doing so may obscure the distinct rates that matter to replication and valuation. The document raises the question rather than offering a settled industry convention or empirical comparison. Its examples, including unusual market events, motivate the issue but do not establish how practitioners consistently handle it. The central lesson is that implied volatility is conditional on the pricing framework and its assumptions, so interpretation requires knowing the model and funding inputs behind it.
Key ideas
- Implied volatility is the value that makes a chosen pricing model match an observed option price.
- Changing borrowing, lending, or stock-loan assumptions can change the pricing model and its implied volatility.
- Volatility smiles derived from different models may not be directly comparable.
- Combining several financing costs into adjusted inputs can fit prices while obscuring economically distinct rates.
- The document raises practical questions but does not establish a universal convention for handling these effects.
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Full text
# Is implied volatility really all that usefull? # Is implied volatility really all that usefull? I take implied volatility as the positive floating point number which lets the BS formula match an observed option price (assuming we have some useful interest rate, some underlying, etc). How useful is that information really? I know all about the mathematical side of things but through the recent events with GameStop and Wirecard, I kept thinking about how useful volatility is here at all if you wanted to deviate from Black-Scholes. Is there in practice a concept of 'model-dependent' implied volatility? Let's assume I model stock-borrowing fees, that turns the linear BS PDE into a non-linear PDE which you can handle but it's fundamentally a different equation (that I would solve numerically then). Given some interest rate and some borrowing rate I now can't use the implied volatility anymore from BS and have to imply it from my other model. How do you combine these issues? I mean with interest-rate risk of some sort I might also back out volatility smiles but they are difficult to compare with 'standard' smiles. For stock borrowing fees, you have up to three interest rates you have to consider and while you could for example come up with a "interest rate smile" (... shiver.) such that the BS impl. vola smile together with the IR smile fits for example the prices that consider a model with borrowing rates, then you have to ask yourself what's the point. Do you really fudge-factor everything just to keep on using BS? That sounds terrifying. Going back to the replicating portfolio aspect, if you borrow with one rate and lend with the other and have to pay stock lending fees which count towards the IR income, you can't just assume that there is some hypothetical interest rate that you can use for all three. You totally can and it will match prices but what the heck. How is that handled in practice? I mean stock borrowing fees are a good example for something not being a problem at all until it is. I also don't like these arguments in the manner of "yeah that's just traded then by ball-feeling, it's useless anyways (but the same person will tell me a "precise" DV01 estimate down to the 7th digit on the phone which is reeeally important for his swap trade to get his cashflow right down to the digit because he is a "precise person and likes to have order in his book" hurr durr)" or "implied parameters normally mean nothing and have no connection to the real world (in that case, as a numerical analyst, please use high-order polynomials to just interpolate everything, inferring parameters here is linear so much easier and by Weierstrass you can even interpolate option prices! Great.)"
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.