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
Now we derive the Black-Scholes equation for a European option V with arbitrary payoff V (S, T) = Ψ(S). Let us construct a portfolio Π consisting of one option and a number "−φ" of an underlying asset. The value of the portfolio is:
Π = V − φS
where φ is constant and makes Π instantaneously risk-free. The jump in the value of this portfolio in one time step is:
dΠ = dV − φdS
Let us consider that the dividend yield is defined as the proportion of the asset price paid out per unit time, so then, at time dt, the underlying asset pays out a dividend D * S * dt. Since we receive D * S * dt for every asset held and since we hold "−φ" of the underlying, our portfolio changes to:
dΠ = dV − φdS − φDSdt
We suppose that the stock price S satisfies the following SDE:
dS = S(r − D)dt + SσdW
where "r" is the risk free bank rate, "D" is the dividend, and "σ” is the volatility for the stock price "S". Applying Ito’s lemma to V , we find:

and so:
(7)
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