英文标题：
《A Novel Approach to Forecasting Financial Volatility with Gaussian
  Process Envelopes》
---
作者：
Syed Ali Asad Rizvi, Stephen J. Roberts, Michael A. Osborne and Favour
  Nyikosa
---
最新提交年份：
2017
---
英文摘要：
  In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series. We provide a direct comparison of their performance to traditional approaches such as GARCH. We compare the forecasting power of three approaches: GP regression on the absolute and squared returns; regression on the envelope of the returns and the absolute returns; and regression on the envelope of the negative and positive returns separately. We use a maximum a posteriori estimate with a Gaussian prior to determine our hyperparameters. We also test the effect of hyperparameter updating at each forecasting step. We use our approaches to forecast out-of-sample volatility of four currency pairs over a 2 year period, at half-hourly intervals. From three kernels, we select the kernel giving the best performance for our data. We use two published accuracy measures and four statistical loss functions to evaluate the forecasting ability of GARCH vs GPs. In mean squared error the GP\'s perform 20% better than a random walk model, and 50% better than GARCH for the same data.
---
中文摘要：
在本文中，我们使用高斯过程（GP）回归提出了一种通过预测时间序列的包络来预测财务收益波动性的新方法。我们直接比较了它们与传统方法（如GARCH）的性能。我们比较了三种方法的预测能力：绝对收益和平方收益的GP回归；收益和绝对收益的包络回归；分别在负收益和正收益的包络上进行回归。在确定超参数之前，我们使用高斯先验的最大后验估计。我们还测试了每个预测步骤中超参数更新的效果。我们使用我们的方法预测两年内四种货币对的样本外波动率，每半小时一次。从三个内核中，我们选择能够为数据提供最佳性能的内核。我们使用两个已发布的精度度量和四个统计损失函数来评估GARCH与GPs的预测能力。在均方误差方面，对于相同的数据，GP的表现比随机游走模型好20%，比GARCH好50%。
---
分类信息：

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
--
一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
--
一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--

---
PDF下载：
-->