论文标题
深度参数PDE方法：应用于选项定价
The Deep Parametric PDE Method: Application to Option Pricing
论文作者
论文摘要
我们提出了深度参数PDE方法来求解高维参数偏微分方程。单个神经网络在不需要样品溶液的情况下接受训练后近似于整个PDE的溶液。作为一种实际应用，我们在多元黑色choles模型中计算期权价格。在单个训练阶段之后，不同时间，状态和模型参数的价格以毫秒为单位。我们以多达25个维度的示例评估了价格的准确性和隐含波动率的概括。与替代机器学习方法的比较，证实了该方法的有效性。
We propose the deep parametric PDE method to solve high-dimensional parametric partial differential equations. A single neural network approximates the solution of a whole family of PDEs after being trained without the need of sample solutions. As a practical application, we compute option prices in the multivariate Black-Scholes model. After a single training phase, the prices for different time, state and model parameters are available in milliseconds. We evaluate the accuracy in the price and a generalisation of the implied volatility with examples of up to 25 dimensions. A comparison with alternative machine learning approaches, confirms the effectiveness of the approach.
