arXiv Machine Learning By Harris Cobb, Wenbo Hao, Yingjie Liu

Neural Networks with Local Converging Inputs for Efficient Options Pricing Models

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arXiv:2608. 02778v1 Announce Type: new Abstract: We present a novel application of Neural Networks with Local Converging Inputs (NNLCI) to improve the efficiency of existing numerical methods for pricing multi-asset options.

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arXiv Machine Learning
Sep 10

Asymptotically-informed neural networks for Black-Scholes implied volatility computation

The paper introduces asymptotically-informed neural‑network architectures for computing Black‑Scholes implied volatility. By learning a trainable partition of the price‑log‑moneyness domain and combining specialised local approximations, the models outperform standard feed‑forward networks across a wide range of parameters. The neural‑network outputs also serve as highly accurate initial guesses for a third‑order Householder scheme, enabling near machine‑precision results after only two refinement iterations.

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