AI predicts stock option prices using neural networks and the rough Bergomi model.

Decoding the Future: Can AI Predict Stock Option Prices?

Kai Mendoza in Business & Economy June 2025 • 4 min read.

"Discover how neural networks and innovative models are revolutionizing financial forecasting, offering new strategies for investors and traders."

The financial markets are constantly evolving, and with that evolution comes the need for more sophisticated tools to help investors make informed decisions. One area that has seen significant advancements is the prediction of stock option prices. Traditionally, these predictions have relied on complex mathematical models, but recent research is exploring the use of artificial intelligence to enhance accuracy and efficiency.

A particularly interesting development is the application of neural networks to the rough Bergomi (rBergomi) model. This model is known for its ability to accurately describe historical and implied volatilities, which are crucial for pricing options. However, the rBergomi model involves many complex parameters, making it challenging to implement in practice. Recent studies aims to tackle this challenge by using neural networks to learn the forward variance curve within the rBergomi model, potentially unlocking new levels of predictive power.

This article delves into the innovative approach of using neural stochastic differential equations (SDEs) to predict stock option prices. We'll break down the key concepts, including the rBergomi model, neural networks, and the numerical techniques used to make these predictions. Whether you're an experienced trader, a financial analyst, or simply someone curious about the intersection of AI and finance, this exploration promises valuable insights into the future of financial forecasting.

What is the Rough Bergomi Model and Why Does It Matter?

AI predicts stock option prices using neural networks and the rough Bergomi model.

The rough Bergomi model is a mathematical framework used in financial engineering to describe the volatility of asset prices. Volatility, which measures the degree of variation in a trading price series over time, is a critical factor in pricing options and managing risk. Unlike traditional models that assume volatility is smooth and predictable, the rBergomi model acknowledges that volatility can be “rough,” exhibiting erratic and sudden changes.

This “roughness” is captured by incorporating fractional Brownian motion (fBM), a mathematical process that allows for long-range dependencies in volatility. In simpler terms, the rBergomi model recognizes that past volatility can influence future volatility in a way that traditional models often miss. This makes it particularly useful for pricing options, as it can better reflect the actual dynamics of the market.

Here’s why the rough Bergomi model is gaining traction:

Accuracy: It provides a more accurate representation of volatility, leading to better option pricing.
Realism: It captures the erratic nature of volatility observed in real-world markets.
Adaptability: It can be adapted and combined with other techniques, such as neural networks, to further enhance its predictive capabilities.

While the rBergomi model offers significant advantages, it also presents challenges. It involves complex mathematical calculations and hidden parameters that can be difficult to estimate. This is where neural networks come into play, offering a way to learn and approximate these complex relationships.

The Future of Financial Prediction

The integration of neural networks with the rough Bergomi model represents a significant step forward in financial forecasting. By leveraging the power of AI, it’s becoming possible to tackle the complexities of the market with greater accuracy and efficiency. As AI continues to evolve, we can expect even more innovative solutions that transform the way investment decisions are made. Whether you're a seasoned professional or new to the world of finance, understanding these advancements is key to navigating the future of financial markets.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2402.02714,

Title: Neural Option Pricing For Rough Bergomi Model

Subject: q-fin.cp

Authors: Changqing Teng, Guanglian Li

Published: 04-02-2024

Everything You Need To Know

How does the rough Bergomi model differ from traditional volatility models?

The rough Bergomi model differs significantly from traditional volatility models by recognizing that volatility can be "rough," exhibiting erratic and sudden changes. Unlike traditional models that assume volatility is smooth and predictable, the rBergomi model incorporates fractional Brownian motion to capture long-range dependencies in volatility. This means the rBergomi model acknowledges that past volatility can influence future volatility in ways that traditional models often miss, making it more realistic for pricing options.

What are neural stochastic differential equations, and how are they used in predicting stock option prices?

Neural stochastic differential equations (SDEs) represent an innovative approach to predicting stock option prices by combining neural networks with stochastic calculus. They allow the model to learn the complex dynamics of asset prices over time. The neural network component helps to approximate the unknown functions within the SDE, enhancing the model's ability to capture intricate patterns and dependencies. By integrating these components, neural SDEs provide a more robust framework for financial forecasting, particularly useful when combined with models like the rough Bergomi model.

What are the main advantages of using the rough Bergomi model for option pricing?

The rough Bergomi model offers several key advantages for option pricing. First, it provides a more accurate representation of volatility, leading to better option pricing. Second, it captures the erratic nature of volatility observed in real-world markets, making it more realistic. Finally, it is adaptable and can be combined with other techniques, such as neural networks, to further enhance its predictive capabilities. Despite its complexities, the rBergomi model's ability to reflect actual market dynamics makes it a valuable tool in financial engineering. While the challenges lie in its complex mathematical calculations and hidden parameters, the use of neural networks helps to mitigate these complexities.

Why is the integration of neural networks with the rough Bergomi model considered a significant advancement in financial forecasting?

The integration of neural networks with the rough Bergomi model is considered a significant advancement because it addresses the complexities of the rBergomi model with the learning capabilities of AI. Neural networks can learn and approximate the complex relationships and parameters within the rBergomi model, enhancing its predictive power. This combination allows for a more accurate and efficient analysis of market volatility, leading to improved option pricing and risk management. By leveraging AI, the integration tackles the computational challenges associated with the rBergomi model, making it more accessible and practical for financial professionals.

What challenges exist when using the rough Bergomi model, and how can neural networks help overcome them?

The rough Bergomi model presents challenges due to its complex mathematical calculations and hidden parameters that can be difficult to estimate. Neural networks offer a way to overcome these challenges by learning and approximating these complex relationships. By training on historical data, neural networks can estimate the forward variance curve within the rBergomi model, improving its predictive accuracy and efficiency. This integration of AI helps to make the rBergomi model more practical and accessible for financial forecasting.