英文标题：
《Exploring the predictability of range-based volatility estimators using
  RNNs》
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作者：
G\\\'abor Petneh\\\'azi and J\\\'ozsef G\\\'all
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最新提交年份：
2018
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英文摘要：
  We investigate the predictability of several range-based stock volatility estimators, and compare them to the standard close-to-close estimator which is most commonly acknowledged as the volatility. The patterns of volatility changes are analyzed using LSTM recurrent neural networks, which are a state of the art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index, and report averaged evaluation results. We find that changes in the values of range-based estimators are more predictable than that of the estimator using daily closing values only.
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中文摘要：
我们研究了几种基于区间的股票波动率估值器的可预测性，并将其与标准的近距离估值器（最常见的波动率）进行了比较。使用LSTM递归神经网络分析波动率变化模式，这是一种最先进的序列学习方法。我们对道琼斯工业平均指数的所有当前成分进行分析，并报告平均评估结果。我们发现基于范围的估计值的变化比仅使用每日收盘值的估计值的变化更容易预测。
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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
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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
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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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