Neural Network Models for Bitcoin Option Pricing

Area of Science:

• Quantitative Finance
• Computational Finance
• Machine Learning Applications

Background:

• Cryptocurrencies, particularly Bitcoin, have garnered significant interest, yet financial instruments related to them remain underdeveloped.
• Existing pricing models for financial derivatives may not adequately capture the unique characteristics of Bitcoin.

Purpose of the Study:

• To develop a robust pricing model for options on Bitcoin.
• To leverage artificial neural networks for enhanced derivative pricing in the cryptocurrency market.

Main Methods:

• Utilized an artificial neural network (ANN) approach.
• Integrated classical pricing models (trinomial tree, Monte Carlo simulation, finite difference method) as input layers for the ANN.
• Trained and validated the ANN model for Bitcoin option pricing.

Main Results:

• Classical pricing methods systematically overprice Bitcoin options.
• The proposed ANN model demonstrates a noticeable improvement in price prediction accuracy for Bitcoin options.
• The study highlights the limitations of traditional models for novel digital assets.

Conclusions:

• Artificial neural networks offer a superior approach to pricing Bitcoin options compared to traditional methods.
• The developed ANN model provides a more accurate and reliable valuation for Bitcoin-derived financial products.
• Further research into machine learning applications for cryptocurrency finance is warranted.