英文标题：
《Deep learning calibration of option pricing models: some pitfalls and
  solutions》
---
作者：
A Itkin
---
最新提交年份：
2019
---
英文摘要：
  Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for the financial industry and mathematical finance. In this paper we consider a classical problem of mathematical finance - calibration of option pricing models to market data, as it was recently drawn some attention of the financial society in the context of deep learning and artificial neural networks. We highlight some pitfalls in the existing approaches and propose resolutions that improve both performance and accuracy of calibration. We also address a problem of no-arbitrage pricing when using a trained neural net, that is currently ignored in the literature.
---
中文摘要：
人工智能、机器学习和计算机工业领域的最新进展导致了将这些技术应用于解决科学和工业中的复杂任务的持续繁荣。当然，金融业和数学金融也是如此。在本文中，我们考虑了数学金融的一个经典问题——期权定价模型与市场数据的校准，因为它最近在深度学习和人工神经网络的背景下引起了金融社会的一些关注。我们强调了现有方法中的一些缺陷，并提出了提高校准性能和准确性的解决方案。我们还解决了一个使用训练过的神经网络进行无套利定价的问题，这一问题目前在文献中被忽视。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
--
一级分类：Quantitative Finance        数量金融学
二级分类：Pricing of Securities        证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
--

---
PDF下载：
-->