Computational Finance Course- Lecture 7/14

Dear all,
We almost halfway through! Today I would like to share Lecture no 7 (out of 14) of the Computational Finance course!
We will discuss Stochastic Volatility Models, in particular the Heston model. We will derive the pricing PDE and investigate the impact of Stochastic Volatility on Implied Volatility smile and skew.
Lecture slides you can find in the description of the lecture on YouTube.
Content of today’s lecture is as follows:
7.1. Towards Stochastic Volatility
7.2. The Stochastic Volatility Model of Heston
7.3. Correlated Stochastic Differential Equations
7.4. Ito’s Lemma for Vector Processes
7.5. Pricing PDE for the Heston Model
7.6. Impact of SV Model Parameters on Implied Volatility
7.7. Black-Scholes vs. Heston Model
7.8. Characteristic Function for the Heston Model

—–>Lecture 7- Stochastic Volatility Models
Lecture 8- Fourier Transform for Option Pricing
Lecture 9- Monte Carlo
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​