Today I would like to share with you Lecture no 9 (out of 14) of the Computational Finance series!
We will discuss Monte Carlo simulation: stochastic sampling, implementation, convergence and different types of process discretizations. Enjoy!
Lecture slides and Python codes you can find in the description of the lecture on YouTube.
Content of today’s lecture is as follows:
9.1. Monte Carlo and Integration via Sampling
9.2. Examples of Stochastic Integrals in Python
9.3. Smoothness of a Payoff and Impact on Convergence
9.4. Types of Convergence
9.5. Option Pricing and Standard Error
9.6. Euler Discretization
9.7. Milstein Discretization
—–> Lecture 9- Monte Carlo Simulation
Lecture 10- Monte Carlo Simulation of the Heston Model
Lecture 11- Hedging and Monte Carlo Greeks
Lecture 12- Forward Start Options and Model of Bates
Lecture 13- Exotic Derivatives
Lecture 14- Summary