C++ Options Pricing

This repository is a work of Marwen Khelifa, Adrien Letellier and Tsitohaina Ravelomanana for the 2023-2024 C++ course project at ENSAE.

It contains implementations of options pricing algorithms using Black-Scholes, Binomial Tree, and Monte Carlo models for various vanilla and exotic options.

Additionally, a replication strategy for European options without dividends has been implemented.

Here is a list of all options and methods available in our repository:

Option Name	Black-Scholes	Binomial Tree	Monte Carlo	Replication Strategy
Example	✅ (supported)	❌ (not supported)	- (not applicable)
American	❌	✅	✅	❌
Asian	✅	❌	✅	❌
Barrier	❌	❌	✅	❌
European	✅	✅	✅	✅
European with dividends	✅	❌	✅	❌
Lookback	❌	❌	✅	❌

How to use our repository?

• For Code::Blocks users, the project is available in the codeblocks folder.

• If not using Code::Blocks, the project can be accessed manually using the 'Makefile' in the manually folder.

• An online C++ compiler link is provided for further accessibility: https://onlinegdb.com/nfIJWt32S

How to run algorithms?

An example of utilisation is included in the main.cpp file. To price an option using a specific method, the process is the following:

1. Create an instance of the class of the option type you want to price in the main.cpp file:

[name_of_the_option]_[call] example([args]); // for a call option
[name_of_the_option]_[put] example([args]); // for a put option

2. Apply a method to compute the price according to the chosen method. The names of the methods are standardised for each method:

cout<<example.price_BSM()<<endl; // for Black-Scholes method
cout<<example.price_BT(10000)<<endl; // for Binomial Tree method with 10000 timesteps
cout<<example.price_MC(100, 10000)<<endl; // for Monte Carlo method with 100 timesteps and 10000 simulations
example.replication(); // for Replication Strategy. Note: .replication() is a void function