英文标题：
《Bayesian Nonparametric Adaptive Spectral Density Estimation for
  Financial Time Series》
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作者：
Nick James, Roman Marchant, Richard Gerlach, Sally Cripps
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最新提交年份：
2019
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英文摘要：
  Discrimination between non-stationarity and long-range dependency is a difficult and long-standing issue in modelling financial time series. This paper uses an adaptive spectral technique which jointly models the non-stationarity and dependency of financial time series in a non-parametric fashion assuming that the time series consists of a finite, but unknown number, of locally stationary processes, the locations of which are also unknown. The model allows a non-parametric estimate of the dependency structure by modelling the auto-covariance function in the spectral domain. All our estimates are made within a Bayesian framework where we use aReversible Jump Markov Chain Monte Carlo algorithm for inference. We study the frequentist properties of our estimates via a simulation study, and present a novel way of generating time series data from a nonparametric spectrum. Results indicate that our techniques perform well across a range of data generating processes. We apply our method to a number of real examples and our results indicate that several financial time series exhibit both long-range dependency and non-stationarity.
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中文摘要：
在金融时间序列建模中，区分非平稳性和长期依赖性是一个长期存在的难题。本文使用一种自适应谱技术，以非参数方式联合建模金融时间序列的非平稳性和相关性，假设时间序列由有限但未知数量的局部平稳过程组成，其位置也未知。该模型通过在谱域中建模自协方差函数，允许对依赖结构进行非参数估计。我们所有的估计都是在贝叶斯框架内进行的，在这个框架中，我们使用了一个可逆跳马尔可夫链蒙特卡罗算法进行推理。我们通过仿真研究了估计的频率特性，并提出了一种从非参数谱生成时间序列数据的新方法。结果表明，我们的技术在一系列数据生成过程中表现良好。我们将我们的方法应用于一些实际例子，结果表明，一些金融时间序列同时表现出长期依赖性和非平稳性。
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分类信息：

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：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：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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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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