Improving option pricing with the product constrained hybrid neural network

Area of Science:

• Quantitative Finance
• Computational Finance
• Machine Learning Applications

Background:

• Conventional option pricing models demonstrate inaccuracies across financial markets.
• Artificial neural networks (ANNs) are increasingly explored to enhance option pricing accuracy.

Purpose of the Study:

• To develop a constrained artificial neural network (ANN) for improved option pricing.
• To ensure option pricing adheres to rationality at boundary conditions.

Main Methods:

• Implementing boundary constraints within the ANN architecture.
• Modifying the ANN's regression surface through these constraints.
• Comparing performance against conventional and non-conventional pricing models.

Main Results:

• The constrained ANN demonstrated statistically significant out-performance.
• Economically significant improvements in option pricing accuracy were observed.
• Enhanced accuracy was particularly noted near option-pricing boundaries.

Conclusions:

• Constrained ANNs offer a superior approach to option pricing compared to existing methods.
• The rationality constraints effectively improve ANN performance in financial modeling.
• This method provides a more accurate and reliable tool for option valuation.