Machine-learning regression methods for American-style path-dependent contracts
比较了机器学习与传统回归方法在定价亚式期权、回溯期权和可赎回凭证等美式路径依赖合约时的表现，发现机器学习在准确性和效率上与传统方法相当，随机神经网络最适合可赎回凭证，并首次将切比雪夫插值用于亚式期权的Delta和Gamma计算。
Evaluating financial products with early-termination clauses, particularly those with path-dependent structures, is challenging. This paper focuses on Asian options, look-back options, and callable certificates. We will compare regression methods for pricing and computing sensitivities, highlighting modern machine learning techniques against traditional polynomial basis functions. Specifically, we will analyze randomized recurrent and feed-forward neural networks, along with a novel approach using signatures of the underlying price process. For option sensitivities like Delta and Gamma, we will incorporate Chebyshev interpolation. Our findings show that machine learning algorithms often match the accuracy and efficiency of traditional methods for Asian and look-back options, while randomized neural networks are best for callable certificates. Furthermore, we apply Chebyshev interpolation for Delta and Gamma calculations for the first time in Asian options and callable certificates.