Black Scholes

Wiener Process Ito 's Lemma Derivation of Black-Scholes Solving Black-Scholes

Introduction to Financial Derivatives
Understanding the Stock Pricing Model

22M:303:002

Understanding the Stock Pricing Model

22M:303:002

Wiener Process Ito 's Lemma Derivation of Black-Scholes

Stock Pricing Model

Solving Black-Scholes

Recall our stochastic dierential equation to model stock prices:

dS = σ dX + µ dt S where µ is known as the asset 's drift , a measure of the average rate of growth of the asset price, σ is the volatility of the stock, it measures the standard deviation of an asset 's returns, and

dX is a random sample drawn from a normal distribution with mean zero.
Both µ and σ are measured on a 'per year ' basis.
Understanding the Stock Pricing Model 22M:303:002

Wiener Process Ito 's Lemma Derivation of Black-Scholes

Ecient Market Hypothesis

Solving Black-Scholes

Past history is fully reected in the present price, however this does not hold any further information. (Past performance is not indicative of future returns) Markets respond immediately to any new information about an asset.

Understanding the Stock Pricing Model

22M:303:002

Wiener Process Ito 's Lemma Derivation of Black-Scholes

Markov Process

Solving Black-Scholes

Denition A stochastic process where only the present value of a variable is relevant for predicting the future. This implies that knowledge of the past history of a Markov variable is irrelevant for determining future outcomes. Markov Process⇔Ecient Market Hypothesis

Understanding the Stock Pricing Model

22M:303:002

Wiener Process Ito 's Lemma Derivation of Black-Scholes

Investigating the Random Variable
Consider a random variable, X , that follows a Markov stochastic process. Further assume that the variable 's change (over a one-year time span), dX , can be characterized by a standard normal distribution (a probability distribution with mean zero and standard