AAD
Adjoint algorithmic differentiation for many sensitivities at near-constant reverse cost.
Build the mental model first.
Record the pricing computation, then propagate sensitivities backwards through it.
Adjoint algorithmic differentiation for many sensitivities at near-constant reverse cost.
Now make it exact.
Notation and units
Decimal rates and volatilities, year-fraction time and continuous compounding unless stated otherwise.
Every model has a price.
Educational conventions are stated explicitly and may simplify market quotation or settlement details.
Rates are continuously compounded unless the section says otherwise.
Inputs are deterministic in the base model.
“An unstated convention is a future reconciliation break.”— THEQUANTBATEMAN
Why a quant cares.
Industry-standard technique for large-scale Greeks where the implementation supports it.
The hedge has opinions.
Start with the quote convention, then ask which Frontier risk survives the hedge. A number without its convention is merely well-dressed ambiguity.
Ask Bateman about this model →