英文标题：
《Conditional Density Estimation with Neural Networks: Best Practices and
  Benchmarks》
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作者：
Jonas Rothfuss, Fabio Ferreira, Simon Walther, Maxim Ulrich
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最新提交年份：
2019
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英文摘要：
  Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\\mathbf{x}$ and a dependent variable $\\mathbf{y}$ by modeling their conditional probability $p(\\mathbf{y}|\\mathbf{x})$. The paper develops best practices for conditional density estimation for finance applications with neural networks, grounded on mathematical insights and empirical evaluations. In particular, we introduce a noise regularization and data normalization scheme, alleviating problems with over-fitting, initialization and hyper-parameter sensitivity of such estimators. We compare our proposed methodology with popular semi- and non-parametric density estimators, underpin its effectiveness in various benchmarks on simulated and Euro Stoxx 50 data and show its superior performance. Our methodology allows to obtain high-quality estimators for statistical expectations of higher moments, quantiles and non-linear return transformations, with very little assumptions about the return dynamic.
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中文摘要：
给定一组经验观测值，条件密度估计旨在通过建模条件概率$p（\\mathbf{y}| \\mathbf{x}）$）来获取条件变量$\\mathbf{y}$和因变量$\\mathbf{y}$之间的统计关系。本文基于数学见解和经验评估，为神经网络在金融应用中的条件密度估计开发了最佳实践。特别是，我们引入了一种噪声正则化和数据归一化方案，缓解了此类估计器的过度拟合、初始化和超参数敏感性问题。我们将我们提出的方法与流行的半参数和非参数密度估值器进行了比较，在模拟和欧洲斯托克50指数数据的各种基准测试中证明了其有效性，并显示了其优越的性能。我们的方法可以获得高阶矩、分位数和非线性回报变换的统计期望的高质量估计量，而回报动态的假设很少。
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分类信息：

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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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：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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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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