Option Pricing

This repository contains application of Heston and Black-scholes models and techniques for pricing financial options. The focus is on closely match market structure using the Black-Scholes model, visualizing implied volatility surfaces, and utilizing Monte Carlo simulations and FFT pricing for exotic option pricing using the Heston model.

Folder Structure:

tiny_pricing_utils/

• This folder is the source code for a python library to price derivatives using FFT and monetcarlo methods for BS and Heston.
• Available at: https://pypi.org/project/tiny-pricing-utils/
• Documentation: https://option-pricing-1ld1qd386-michael-carlos-projects.vercel.app/

'Option pricing/'

Black_Scholes_Pricing

• This folder contains notebooks related to the Black-Scholes option pricing model.
• black_scholes_calibration.ipynb: Calibrates the Black-Scholes model to find the optimal volatility.
• implied_vol_surface.ipynb: Finds and visualizes the implied volatility surface based on market data.
• exotic_montecarlo_pricing.ipynb: Implements Monte Carlo simulations for pricing Asian and Barrier options.

Heston_Pricing

• This folder contains notebooks and scripts related to option pricing using the Heston model and numerical integration methods.
• Heston_FFT_pricing.ipynb: Implements call option pricing using the Heston model.
• Carr_Madan_formula.ipynb: Uses the Carr-Madan formula with Rectangular Rule and Simpson's Rule for pricing options under the Heston model.
• BS_FFT_pricing.ipynb: Implements call option pricing using the Black-Scholes model.

Heston_calibration

• This folder contains notebooks and scripts related to option pricing using the Heston model using Montecarlo simulations.
• Heston_calibration.ipynb: Implements call option pricing using the Heston model using Euler and Milstein Montecarlo methods.

Requirements:

• Python 3.x
• Necessary libraries: numpy, pandas, matplotlib, scipy, ipython, etc.

Setup:

To get started, make sure all dependencies are installed. You can install the necessary Python packages using:

pip install -r requirements.txt