收益率曲线特征提取的机器学习：应用于非流动性公司债券（初稿）
Machine Learning for Yield Curve Feature Extraction: Application to Illiquid Corporate Bonds (Preliminary Draft)
摘要：
本文研究了机器学习在从历史风险中性公司债券收益率中提取市场隐含特征中的应用。我们考虑一个假设的非流动性固定收益市场的例子。在选择了一个替代流动性市场后，我们应用计算机视觉和模式识别领域的去噪自动编码器算法，从所选流动性市场交易的工具的历史隐含数据中学习缺失收益率参数的特征。将经训练的机器学习算法的结果与称为薄板样条的时间点二维插值算法的输出进行比较。最后，对两种算法的性能进行了比较。
英文摘要：
This paper studies the application of machine learning in extracting the market implied features from historical risk neutral corporate bond yields. We consider the example of a hypothetical illiquid fixed income market. After choosing a surrogate liquid market, we apply the Denoising Autoencoder algorithm from the field of computer vision and pattern recognition to learn the features of the missing yield parameters from the historically implied data of the instruments traded in the chosen liquid market. The results of the trained machine learning algorithm are compared with the outputs of a point in- time 2 dimensional interpolation algorithm known as the Thin Plate Spline. Finally, the performances of the two algorithms are compared.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
