英文标题：
《Nonparametric Bayesian volatility learning under microstructure noise》
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作者：
Shota Gugushvili, Frank van der Meulen, Moritz Schauer and Peter
Spreij
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最新提交年份：
2018
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英文摘要：
Aiming at financial applications, we study the problem of learning the volatility under market microstructure noise. Specifically, we consider noisy discrete time observations from a stochastic differential equation and develop a novel computational method to learn the diffusion coefficient of the equation. We take a nonparametric Bayesian approach, where we model the volatility function a priori as piecewise constant. Its prior is specified via the inverse Gamma Markov chain. Sampling from the posterior is accomplished by incorporating the Forward Filtering Backward Simulation algorithm in the Gibbs sampler. Good performance of the method is demonstrated on two representative synthetic data examples. Finally, we apply the method on the EUR/USD exchange rate dataset.
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中文摘要：
针对金融应用，研究了市场微观结构噪声下的波动率学习问题。具体来说，我们考虑了随机微分方程的噪声离散时间观测，并开发了一种新的计算方法来学习方程的扩散系数。我们采用非参数贝叶斯方法，将波动率函数先验建模为分段常数。其优先级通过反伽玛-马尔可夫链指定。通过在吉布斯采样器中加入前向滤波后向模拟算法，完成后向采样。通过两个具有代表性的合成数据实例，证明了该方法的良好性能。最后，我们将该方法应用于欧元/美元汇率数据集。
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分类信息：

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：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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