# Tag Info

3

Automatic Differentiation (aka AD) is a family of methods that are used to evaluate the derivative of a coded function. These methods are far more accurate than finite differences, since they are theoretically exact in the absence of floating point roundoff error. AD is, however, subtly different than symbolic differentiation. The key difference here is ...

1

Yes, you are right. It appears to be a trivial typographical error in the book. I checked the formulas on Wikipedia https://en.wikipedia.org/wiki/Greeks_%28finance%29 and they agree with yours. The signs are obvious also since N(.) is between 0 and 1, i.e. non-negative. Now, about the reasoning starting with "from a logical point of view". Are you familiar ...

1

If you have many strikes of european-exercise options for two dates $T_1$ and $T_2$, then the option skew $\sigma_{1,2}(x)$ implies model-free risk-neutral probability distributions $p_1, p_2$ for each of these dates, $$p_i(x) = {\left. \frac{\partial^2 }{\partial x^2}\right|} BS_{\text{Call}}(S_0, x, \sigma_i(x), r, T_i, q)$$ ...

Top 50 recent answers are included