American Call Options Pricing With Modular Neural Networks

ArXiv ID: 2409.19706 “View on arXiv”

Authors: Unknown

Abstract

An accurate valuation of American call options is critical in most financial decision making environments. However, traditional models like the Barone-Adesi Whaley (B-AW) and Binomial Option Pricing (BOP) methods fall short in handling the complexities of early exercise and market dynamics present in American options. This paper proposes a Modular Neural Network (MNN) model which aims to capture the key aspects of American options pricing. By dividing the prediction process into specialized modules, the MNN effectively models the non-linear interactions that drive American call options pricing. Experimental results indicate that the MNN model outperform both traditional models as well as a simpler Feed-forward Neural Network (FNN) across multiple stocks (AAPL, NVDA, QQQ), with significantly lower RMSE and nRMSE (by mean). These findings highlight the potential of MNNs as a powerful tool to improve the accuracy of predicting option prices.

Keywords: American options pricing, Modular Neural Network, early exercise, option pricing models, RMSE, derivatives

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced neural network architectures (Modular Neural Networks) with complex non-linear modeling, warranting a high math score, and includes empirical backtesting on real stocks (AAPL, NVDA, QQQ) with reported error metrics (RMSE, nRMSE), justifying a moderate-to-high empirical rigor.

  flowchart TD
    A["Research Goal<br>American Call Option Pricing"] --> B["Data Preparation<br>AAPL, NVDA, QQQ Data"]
    
    B --> C["Model Architecture<br>Modular Neural Network"]
    
    C --> D["Computational Process<br>1. Feature Engineering<br>2. Training/Validation<br>3. Early Exercise Modeling"]
    
    D --> E["Traditional Baseline<br>B-AW & Binomial Models"]
    
    D --> F["Feed-forward Neural Network"]
    
    E & F --> G["Performance Comparison"]
    
    G --> H["Key Findings<br>MNN outperforms all benchmarks<br>Lower RMSE & nRMSE<br>Superior for complex early exercise"]
    
    style A fill:#e1f5fe
    style H fill:#e8f5e8
    style C fill:#fff3e0
    style D fill:#f3e5f5