Today I would like to share with you Lecture no 11 (out of 14) of the Computational Finance series! We will discuss the concept of the Greeks and hedging. I will also show you how to improve the convergence when computing the sensitivities with Monte Carlo simulation. Enjoy!
Lecture slides and Python codes you can find in the lecture’s description on YouTube.
The content of today’s lecture is as follows:
11.1. Hedging with the Black-Scholes Model
11.2. Dynamic Hedging- Python Experiment
11.3. Hedging with Jumps
11.4. Delta, Gamma and Vega Hedging
11.5. Monte Carlo Sensitivity: Finite Difference
11.6. Monte Carlo Sensitivity: Pathwise Sensitivities
11.7. Monte Carlo Sensitivity: Likelihood Ratio Method
—–>Lecture 11- Hedging and Monte Carlo Greeks
Lecture 12- Forward Start Options and Model of Bates
Lecture 13- Exotic Derivatives
Lecture 14- Summary