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
The Heston model [6] (1993) is a special case of this scheme where γ = 1/2 and the market price of risk Λ. Furthermore, the real world drift is re-parametrized in the form:
ω − ζν = k(θ − ν)
Heston’s paper argues that there is evidence for the linear choice of Λ = λν. The partial differential equation (PDE) from the Heston model is then:
(23)
The solution technique involves an "extended transform" which in this case is a conventional Fourier transform.
The SDEs for Simulation and the General PDE
The PDE from Heston model {SDE-23} follows the next SDEs (with correlation ρ between the noise terms):
dS = S(r − D)dt + S√νdW1
dν = (k(θ − ν) − λν) dt + ξνγ dW2 (24)
However, if we try to be more general with respect to the second equation {24}, we need to represent it in the form:
dν = a(ν)dt + b(ν)dW2
and applying Ito’s formulae {2}, we arrive to the General PDE for stochastic volatility:
(25)
This allows us to consider how to solve without reference to a particular volatility. In practice, we shall be driven back to the Heston class, but (see e.g. Lewis [11]) we can establish the solution for this more general case.
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