英文标题：
《Financial Applications of Gaussian Processes and Bayesian Optimization》
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作者：
Joan Gonzalvez, Edmond Lezmi, Thierry Roncalli, Jiali Xu
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最新提交年份：
2019
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英文摘要：
  In the last five years, the financial industry has been impacted by the emergence of digitalization and machine learning. In this article, we explore two methods that have undergone rapid development in recent years: Gaussian processes and Bayesian optimization. Gaussian processes can be seen as a generalization of Gaussian random vectors and are associated with the development of kernel methods. Bayesian optimization is an approach for performing derivative-free global optimization in a small dimension, and uses Gaussian processes to locate the global maximum of a black-box function. The first part of the article reviews these two tools and shows how they are connected. In particular, we focus on the Gaussian process regression, which is the core of Bayesian machine learning, and the issue of hyperparameter selection. The second part is dedicated to two financial applications. We first consider the modeling of the term structure of interest rates. More precisely, we test the fitting method and compare the GP prediction and the random walk model. The second application is the construction of trend-following strategies, in particular the online estimation of trend and covariance windows.
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中文摘要：
在过去五年中，金融业受到了数字化和机器学习的影响。在本文中，我们探讨了近年来快速发展的两种方法：高斯过程和贝叶斯优化。高斯过程可以看作是高斯随机向量的推广，与核方法的发展有关。贝叶斯优化是一种在小维度上执行无导数全局优化的方法，它使用高斯过程来定位黑箱函数的全局最大值。文章的第一部分回顾了这两个工具，并展示了它们是如何联系在一起的。特别是，我们重点研究了高斯过程回归，这是贝叶斯机器学习的核心，以及超参数选择问题。第二部分介绍两个金融应用程序。我们首先考虑利率期限结构的建模。更准确地说，我们测试了拟合方法，并比较了GP预测和随机游走模型。第二个应用是构建趋势跟踪策略，特别是在线估计趋势和协方差窗口。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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