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Option Pricing and Hedging Strategies

This repository provides Python implementations for pricing European options and developing dynamic hedging strategies. These implementations are based on the Black-Scholes model and Monte Carlo simulations.

Overview

The BlackScholes class allows users to price European call and put options using the Black-Scholes-Merton formula. This includes calculating the option's d1 and d2 terms, which are integral to the pricing formula.

The Strategy class uses the Black-Scholes model and Monte Carlo simulations to evaluate different trading strategies involving European call and put options. The HedgingStrategy class, on the other hand, focuses on dynamic hedging strategies involving self-financing portfolios.

Mathematical Background

The Black-Scholes model is a mathematical model for pricing options. It assumes that the market is efficient, there are no transaction costs or taxes, the risk-free interest rate is constant and known, the returns on the underlying asset are normally distributed and independent over time, and the volatility of the underlying asset is constant and known.

The option pricing formula used in the BlackScholes class relies on the concepts of d1 and d2, which are derived from the Black-Scholes-Merton differential equation. These terms account for factors such as the underlying asset's price, the option's strike price, the risk-free interest rate, the time to expiry, and the asset's volatility.

The Strategy class simulates trading strategies involving options using Monte Carlo simulations. These simulations involve generating random price paths for the underlying asset based on its expected return and volatility. By averaging the payoffs of the options across these simulations, we can estimate the expected wealth from a given strategy.

The HedgingStrategy class uses the concept of 'Delta', the rate of change of the option price with respect to changes in the underlying asset's price, to dynamically adjust the portfolio's holdings of the risky asset to remain self-financing.

Getting Started

Clone the repository and navigate to the downloaded folder:

git https://github.com/sachabinder/ProjCalculStoch.git 
cd ProjCalculStoch

This project uses Python 3.8.5 and the requirements can be installed by running:

pip install -r requirements.txt

Contact

Please open an issue if you encounter any problems while using this repository.

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A paradox of diffusion market model related with existence of winning combinations of options

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