论文标题
深度加权蒙特卡洛：使用神经网络的混合选择定价框架
Deep Weighted Monte Carlo: A hybrid option pricing framework using neural networks
论文作者
论文摘要
最近的研究表明，变异自动编码器（VAE）将高维隐含波动表面压缩为低维表示的效率。尽管此方法可以有效地用于定价香草选项，但它没有提供有关基础资产动态的任何明确信息。在我们的工作中，我们提出了一种克服这个问题的有效方法。我们使用加权的蒙特卡洛方法首先从简单的A先验Brownian动力学中生成路径，然后将路径权重正确计算为价格选项。我们开发并成功训练一个能够直接从潜在空间分配这些权重的神经网络。结合了VAE的编码器网络和这个新的“重量分配器”模块，我们能够构建一个动态定价框架，该框架可以从无关紧要的噪声波动中清洁波动率的表面，然后不仅可以为这款理想化的VOL表面上的异国情调选择。这种定价方法可以为期权交易者提供相对价值信号。
Recent studies have demonstrated the efficiency of Variational Autoencoders (VAE) to compress high-dimensional implied volatility surfaces into a low dimensional representation. Although this method can be effectively used for pricing vanilla options, it does not provide any explicit information about the dynamics of the underlying asset. In our work we present an effective way to overcome this problem. We use a Weighted Monte Carlo approach to first generate paths from a simple a priori Brownian dynamics, and then calculate path weights to price options correctly. We develop and successfully train a neural network that is able to assign these weights directly from the latent space. Combining the encoder network of the VAE and this new "weight assigner" module, we are able to build a dynamic pricing framework which cleanses the volatility surface from irrelevant noise fluctuations, and then can price not just vanillas, but also exotic options on this idealized vol surface. This pricing method can provide relative value signals for option traders.
