标题： 从深度滤波到深度计量经济学 显示英文标题

标题： From Deep Filtering to Deep Econometrics

摘要： 计算真实波动率是期权定价和风险管理的重要任务。然而，市场微观结构噪声使得这一任务变得困难。粒子滤波法被提出用于解决这个问题，因为它具有有利的统计特性，但依赖于关于潜在市场动态的假设。也有人提出了机器学习方法，但这些方法缺乏可解释性，并且通常性能不佳。在本文中，我们实现了SV-PF-RNN：一种混合神经网络和粒子滤波器架构。我们的SV-PF-RNN专门设计用于随机波动率估计。然后我们证明它可以改进基本粒子滤波器的性能。 显示英文摘要

摘要： Calculating true volatility is an essential task for option pricing and risk management. However, it is made difficult by market microstructure noise. Particle filtering has been proposed to solve this problem as it favorable statistical properties, but relies on assumptions about underlying market dynamics. Machine learning methods have also been proposed but lack interpretability, and often lag in performance. In this paper we implement the SV-PF-RNN: a hybrid neural network and particle filter architecture. Our SV-PF-RNN is designed specifically with stochastic volatility estimation in mind. We then show that it can improve on the performance of a basic particle filter.