Network Architectures Improve Option Pricing Accuracy
Researchers empirically compare neural network architectures for supervised learning of option prices and implied volatilities, evaluating Black‑Scholes and Heston models and real market data. They find generalized highway networks achieve the best mean-squared error and training-time trade-off for Black‑Scholes and Heston pricing, while a simplified DGM variant yields lowest error for transformed implied‑volatility tasks; capacity‑normalized comparisons are included.
Scoring Rationale
Practical empirical comparison offering clear architecture guidance, limited by a niche finance focus and being a single arXiv preprint.
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