英文标题：
《Kalman filter demystified: from intuition to probabilistic graphical
  model to real case in financial markets》
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作者：
Eric Benhamou
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最新提交年份：
2018
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英文摘要：
  In this paper, we revisit the Kalman filter theory. After giving the intuition on a simplified financial markets example, we revisit the maths underlying it. We then show that Kalman filter can be presented in a very different fashion using graphical models. This enables us to establish the connection between Kalman filter and Hidden Markov Models. We then look at their application in financial markets and provide various intuitions in terms of their applicability for complex systems such as financial markets. Although this paper has been written more like a self contained work connecting Kalman filter to Hidden Markov Models and hence revisiting well known and establish results, it contains new results and brings additional contributions to the field. First, leveraging on the link between Kalman filter and HMM, it gives new algorithms for inference for extended Kalman filters. Second, it presents an alternative to the traditional estimation of parameters using EM algorithm thanks to the usage of CMA-ES optimization. Third, it examines the application of Kalman filter and its Hidden Markov models version to financial markets, providing various dynamics assumptions and tests. We conclude by connecting Kalman filter approach to trend following technical analysis system and showing their superior performances for trend following detection.
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中文摘要：
在本文中，我们回顾了卡尔曼滤波理论。在给出了一个简化的金融市场例子的直觉之后，我们重新审视了它背后的数学基础。然后，我们展示了使用图形模型可以以非常不同的方式表示卡尔曼滤波器。这使我们能够在卡尔曼滤波器和隐马尔可夫模型之间建立联系。然后，我们研究了它们在金融市场中的应用，并就其对金融市场等复杂系统的适用性提供了各种直觉。虽然本文的写作更像是将卡尔曼滤波器与隐马尔可夫模型联系起来，从而重新访问已知结果并建立结果，但它包含了新的结果，并为该领域带来了额外的贡献。首先，利用卡尔曼滤波器和HMM之间的联系，给出了新的扩展卡尔曼滤波器推理算法。其次，由于CMA-ES优化的使用，它为使用EM算法的传统参数估计提供了一种替代方法。第三，研究了卡尔曼滤波器及其隐马尔可夫模型版本在金融市场中的应用，提供了各种动力学假设和测试。最后，我们将卡尔曼滤波方法与趋势跟踪技术分析系统相结合，并展示了它们在趋势跟踪检测方面的优越性能。
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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：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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