Reader Guidelines
1 Preliminaries
1.1 Basic Concepts from Probability Theory
1.1.1 Random Variables
1.1.2 Random Vectors
1.1.3 Independence and Dependence
1.2 Stochastic Processes
1.3 Brownian Motion
1.3.1 Defining Properties
1.3.2 Processes Derived from Brownian Motion
1.3.3 Simulation of Brownian Sample Paths
1.4 Conditional Expectation
1.4.1 Conditional Expectation under Discrete Condition
1.4.2 About a-Fields
1.4.3 The General Conditional Expectation
1.4.4 Rules for the Calculation of Conditional Expectations
1.4.5 The Projection Property of Conditional Expectations
1.5 Martingales
1.5.1 Defining Properties
1.5.2 Examples
1.5.3 The Interpretation of a Martingale as a Fair Game
2 The Stochastic Integral
2.1 The Riemann and Riemann-Stieltjes Integrals
2.1.1 The Ordinary Riemann Integral
2.1.2 The Riemann-Stieltjes Integral
2.2 The Ito Integral
2.2.1 A Motivating Example
2.2.2 The Ito Stochastic Integral for Simple Processes
2.2.3 The General Ito Stochastic Integral
2.3 The Ito Lemma
2.3.1 The Classical Chain Rule of Differentiation
2.3.2 A Simple Version of the Ito Lemma
2.3.3 Extended Versions of the Ito Lemma
2.4 The Stratonovich and Other Integrals
3 Stochastic Differential Equations
3.1 Deterministic Differential Equations
3.2 Ito Stochastic Differential Equations
3.2.1 What is a Stochastic Differential Equation
3.2.2 Solving Ito Stochastic Differential Equations by the ItoLemma
3.2.3 Solving Ito Differential Equations via Stratonovich Calculus
3.3 The General Linear Differential Equation
3.3.1 Linear Equations with Additive Noise
3.3.2 Homogeneous Equations with Multiplicative Noise
3.3.3 The General Case
3.3.4 The Expectation and Variance Functions of the Solution
3.4 Numerical Solution
3.4.1 The Euler Appromation
3.4.2 The Milstein Appromation
4 Applications of Stochastic Calculus in Finance
4.1 The Black-Scholes Option Pricing Formula
