Machine Learning Methods for Pricing Financial Derivatives

ArXiv ID: 2406.00459 “View on arXiv”

Authors: Unknown

Abstract

Stochastic differential equation (SDE) models are the foundation for pricing and hedging financial derivatives. The drift and volatility functions in SDE models are typically chosen to be algebraic functions with a small number (less than 5) parameters which can be calibrated to market data. A more flexible approach is to use neural networks to model the drift and volatility functions, which provides more degrees-of-freedom to match observed market data. Training of models requires optimizing over an SDE, which is computationally challenging. For European options, we develop a fast stochastic gradient descent (SGD) algorithm for training the neural network-SDE model. Our SGD algorithm uses two independent SDE paths to obtain an unbiased estimate of the direction of steepest descent. For American options, we optimize over the corresponding Kolmogorov partial differential equation (PDE). The neural network appears as coefficient functions in the PDE. Models are trained on large datasets (many contracts), requiring either large simulations (many Monte Carlo samples for the stock price paths) or large numbers of PDEs (a PDE must be solved for each contract). Numerical results are presented for real market data including S&P 500 index options, S&P 100 index options, and single-stock American options. The neural-network-based SDE models are compared against the Black-Scholes model, the Dupire’s local volatility model, and the Heston model. Models are evaluated in terms of how accurate they are at pricing out-of-sample financial derivatives, which is a core task in derivative pricing at financial institutions.

Keywords: Stochastic Differential Equations (SDE), Neural Networks, Option Pricing, Stochastic Gradient Descent, Derivatives

Complexity vs Empirical Score

• Math Complexity: 9.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including stochastic differential equations, partial differential equations (Kolmogorov), and stochastic gradient descent theory. Empirically, it demonstrates out-of-sample pricing and hedging performance on real market data (S&P 500/100 options, American options) and compares results against established models, indicating a backtest-ready framework.

  flowchart TD
    A["Research Goal: Develop & evaluate neural network<br>models for pricing financial derivatives<br>(European & American options)"] --> B["Key Methodology: Use Neural Networks to model<br>drift & volatility in SDEs (vs. classical models)<br>Adaptive SDE Calibration"]
    B --> C["Inputs: Real Market Data<br>S&P 500, S&P 100, Single-Stock Options"]
    C --> D["Computational Processes:"]
    D --> E["European Options:<br>Stochastic Gradient Descent (SGD) on SDE<br>via unbiased path estimates"]
    D --> F["American Options:<br>Optimization via Kolmogorov PDE<br>with Neural Network coefficients"]
    E --> G["Key Findings/Outcomes:"]
    F --> G
    G --> H["NN-SDE models outperform<br>classical benchmarks (Black-Scholes,<br>Dupire, Heston) in out-of-sample pricing"]
    G --> I["Demonstrated feasibility of<br>scalable training on large<br>derivative datasets"]