Summary of Jump Diffusion-informed Neural Networks with Transfer Learning For Accurate American Option Pricing Under Data Scarcity, by Qiguo Sun et al.
Jump Diffusion-Informed Neural Networks with Transfer Learning for Accurate American Option Pricing under Data Scarcity
by Qiguo Sun, Hanyue Huang, XiBei Yang, Yuwei Zhang
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The presented study aims to improve American option pricing by developing a comprehensive framework combining nonlinear optimization algorithms, analytical and numerical models, and neural networks. The framework is designed to address the challenges of determining optimal exercise times and modeling non-linear payoffs resulting from stochastic paths. It also integrates transfer learning through numerical data augmentation and a physically constrained neural network to capture leptokurtosis in log return distribution. The study demonstrates the accuracy, convergence, physical effectiveness, and generalization of the framework through six case studies, showcasing superior performance in pricing deep out-of-the-money options. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about creating a new way to calculate the value of American options. Right now, this calculation is tricky because it involves figuring out when to exercise the option and dealing with weird payoffs that come from chance. The current method used, Black-Scholes, doesn’t always get it right, especially when there’s not much data. To fix this, researchers developed a framework with six parts that work together. It uses special algorithms, math models, and neural networks to improve the calculation. They also added some tricks to make it work better with limited data. The results show that their new method is more accurate and can handle tough options. |
Keywords
* Artificial intelligence * Data augmentation * Generalization * Neural network * Optimization * Transfer learning
