Option Pricing Models
This repository contains Jupyter notebooks that simulate and implement three commonly used option pricing models: Monte Carlo Simulation, Binomial Model, and Black-Scholes Model. These models provide insights into the valuation of financial options and are widely used in quantitative finance.
Option Pricing Models
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Monte Carlo Simulation: The Monte Carlo method involves generating random price paths for the underlying asset and calculating the option's payoff based on these paths. The average of these payoffs provides an estimate of the option's value.
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Binomial Model: The Binomial model breaks down the option's life into discrete time steps and models the asset's price movements as a series of up and down movements. The option's value is calculated recursively using probabilities of up and down movements.
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Black-Scholes Model: The Black-Scholes model is a closed-form solution for European-style options. It takes into account factors like the underlying asset's price, option's strike price, time to expiration, volatility, and risk-free interest rate.
Notebooks
- Monte Carlo Simulation: This notebook demonstrates how to use Monte Carlo simulation to estimate the value of European and American options.
- Binomial Model: This notebook implements the binomial option pricing model for both European and American options.
- Pricing Asian Option: Using different Techniques.
- Simulating Brownian Motion: Simulating and explaining Brownian Motion, a key concept in Quants.
Usage
Each notebook is self-contained and provides step-by-step explanations along with code. You can open and run the notebooks using Jupyter or any compatible environment. Adjust the parameters and input values as needed to explore different scenarios.
Disclaimer
The provided notebooks are for educational purposes only. Option pricing involves complex financial mathematics and should be thoroughly understood before making real-world investment decisions.
Author
These notebooks were created by me. You can reach out to me at [Frandile.aimane@gmail.com] or connect with me on LinkedIn.