英文标题：
《Benchmarking Deep Sequential Models on Volatility Predictions for
  Financial Time Series》
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作者：
Qiang Zhang, Rui Luo, Yaodong Yang, Yuanyuan Liu
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最新提交年份：
2018
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英文摘要：
  Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets. As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities. On the other hand, recent advancement in deep learning techniques has shown strong capabilities in modelling sequential data, such as speech and natural language. In this paper, we empirically study the applicability of the latest deep structures with respect to the volatility modelling problem, through which we aim to provide an empirical guidance for the theoretical analysis of the marriage between deep learning techniques and financial applications in the future. We examine both the traditional approaches and the deep sequential models on the task of volatility prediction, including the most recent variants of convolutional and recurrent networks, such as the dilated architecture. Accordingly, experiments with real-world stock price datasets are performed on a set of 1314 daily stock series for 2018 days of transaction. The evaluation and comparison are based on the negative log likelihood (NLL) of real-world stock price time series. The result shows that the dilated neural models, including dilated CNN and Dilated RNN, produce most accurate estimation and prediction, outperforming various widely-used deterministic models in the GARCH family and several recently proposed stochastic models. In addition, the high flexibility and rich expressive power are validated in this study.
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中文摘要：
波动率是衡量股票或期权价格变动的一个数量，表明金融市场中的不确定性。作为风险水平或变化程度的一个指标，波动性对于分析金融市场非常重要，在金融活动的各种决策过程中都会加以考虑。另一方面，深度学习技术的最新进展表明，在模拟语音和自然语言等顺序数据方面具有强大的能力。在本文中，我们实证研究了最新深度结构对波动率建模问题的适用性，旨在为未来深度学习技术与金融应用之间的结合的理论分析提供经验指导。我们研究了波动率预测任务中的传统方法和深度序列模型，包括卷积网络和递归网络的最新变体，如扩展结构。因此，对2018交易日的1314个每日股票系列进行了真实世界股票价格数据集的实验。评估和比较基于真实世界股票价格时间序列的负对数似然（NLL）。结果表明，扩张的神经模型，包括扩张的CNN和扩张的RNN，能够产生最准确的估计和预测，优于GARCH家族中广泛使用的各种确定性模型和最近提出的几种随机模型。此外，本研究还验证了其高度的灵活性和丰富的表达能力。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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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