一个长短期记忆随机波动模型

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摘要翻译：
随机波动(SV)模型在金融领域得到了广泛的应用，而长短时记忆(LSTM)模型在深度学习的许多大规模工业应用中得到了成功的应用。本文将这两种方法结合起来，提出了一个模型，我们称之为LSTM-SV模型，以捕捉随机波动率的动力学。该模型克服了传统SV模型的短期记忆问题，能够捕捉潜在波动过程中的非线性相关性，具有比SV模型更好的样本外预测性能。通过对美国股市周指数SP500、澳大利亚股市周指数ASX200和澳大利亚-美国元日汇率三个金融时间序列数据的模拟研究和应用，说明了这些性质。基于我们的分析，我们认为SP500和ASX200数据集的波动过程与汇率数据集的波动过程在潜在动力学上存在显著差异。对于股票指数数据，在波动过程中有很强的长期记忆性和非线性依赖性，而对于汇率数据则不是这样。在https://github.com/vbayeslab上提供了一个用户友好的软件包以及本文报告的示例。
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英文标题：
《A long short-term memory stochastic volatility model》
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作者：
Nghia Nguyen, Minh-Ngoc Tran, David Gunawan and R. Kohn
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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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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英文摘要：
  Stochastic Volatility (SV) models are widely used in the financial sector while Long Short-Term Memory (LSTM) models are successfully used in many large-scale industrial applications of Deep Learning. Our article combines these two methods in a non-trivial way and proposes a model, which we call the LSTM-SV model, to capture the dynamics of stochastic volatility. The proposed model overcomes the short-term memory problem in conventional SV models, is able to capture non-linear dependence in the latent volatility process, and often has a better out-of-sample forecast performance than SV models. These properties are illustrated through simulation study and applications to three financial time series datasets: The US stock market weekly index SP500, the Australian stock weekly index ASX200 and the Australian-US dollar daily exchange rates. Based on our analysis, we argue that there are significant differences in the underlying dynamics between the volatility process of the SP500 and ASX200 datasets and that of the exchange rate dataset. For the stock index data, there is strong evidence of long-term memory and non-linear dependence in the volatility process, while this is not the case for the exchange rates. An user-friendly software package together with the examples reported in the paper are available at https://github.com/vbayeslab.
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PDF链接：
https://arxiv.org/pdf/1906.02884

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关键词：波动模型 长短期 econometrics Applications Successfully exchange model short stock 示例