Introduction to Implied, Local and Stochastic Volatility
Implied Volatility - Ito's Lemma
Applying Ito to the Hedging Portfolio
Risk-Neutralization and No-Arbitrage
Implied Volatility (Smiles and Skews)
Coupled SDEs for Stochastic Volatility
Risk-Neutralization and No-Arbitrage
Exact Solution for Heston Volatility
Suppose that we can find the solution of the PDE, say G(w, ν, τ ), with the property that at t = T , G(w, ν, 0) = 1. Then the solution to the transformed PDE {48} with payoff condition U (w, ν, 0) is just the product of this with G.
(49)
Lewis (refer to [11]) discusses how to solve {49} for the general case, but here we will only solve for the Heston model (γ = 1/ 2 ).
Greeks for free
Before figuring out G, we should point out that {49} is a remarkably useful representation. If you want to differentiate V with respect to S to obtain ∆, you merely multiply the integral by:
![]()
and for Γ, the integral is multiplied by:
![]()
This representation also makes obvious the link between ρ and ∆.
Finding the fundamental solution
For γ = 1/ 2 and Heston parameters, the PDE {48} yield the form:
(50)
What Heston [6] does is try to find a solution in the form:
![]()
with:
A [0, w] = B [0, w] = 0
in order to satisfy the condition that G[0, w] = 1 (at maturity). If we substitute this assumption for the form of G into the PDE {50}, we obtain the following condition:
![]()
The A' and B' denote the τ − derivative. This must be true for all ν so we separately equate the terms that are independent of ν and linear in ν to obtain the pair of ordinary differential equations:

Solving this, we obtain:

where:

So the exact solution of the option price using Heston volatility is:
(51)
using the condition:
for
Call options Im(w) > 1
Put options Im(w) < 0
For further information or more details, see [16] or [6].
Prof. Klaus Schmitz

Introduction to Implied, Local and Stochastic Volatility
The purpose of this document is to introduce implied, local and stochastic volatility, to review evidence of non-constant volatility, and to consider the implications for option pricing of alternative random or stochastic volatility models. We focus on continuous time diffusion models for the volatility, but we also briefly discuss certain classes of discrete time models, such as ARV or ARCH.
By Prof. Klaus Erich Schmitz Abe