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
Clearly we wish to eliminate the stochastic component of risk by setting:
a = b = 0
so we rearrange the hedge parameters in the form:
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to eliminate the dS term in {20}, and

to eliminate the dν term in {20}.
The avoidance of the arbitrage, once these choices of φ1, φ2 are made, is the condition:
dΠ = rΠdt
dΠ = r (V − φ1S − φ2U ) dt (21)
where we have used the fact that the return on a risk-free portfolio must be equal to the risk-free bank rate r which we will assume to be deterministic for our purposes. Combining equations {20} and {21}, collecting all V terms on the left hand side and all U terms on the right hand side, we get:
Now V , U are an arbitrary pair of derivative contracts. The only way that this can occur is when both sides of the equation are equal to some function depending only on S, ν, t. So we write both sides as f (S, ν, t), where f is the real world drift term less the market price of risk Λ.
f (S, ν, t) = ((ω − ζν) − Λ)
In doing so, we arrive to the General PDE for mean reversion stochastic volatility:
(22)
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