英文标题：
《CDS Rate Construction Methods by Machine Learning Techniques》
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作者：
Raymond Brummelhuis and Zhongmin Luo
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最新提交年份：
2017
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英文摘要：
  Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. After testing 156 classifiers from 8 most popular classifier families, we found that some classifiers achieve highly satisfactory accuracy rates. Furthermore, we have rank-ordered the performances and investigated performance variations amongst and within the 8 classifier families. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data. Its findings both confirm and contrast existing classifier performance literature. Given the typically highly correlated nature of financial data, we investigated the impact of correlation on classifier performance. The techniques used in this paper should be of interest for financial institutions seeking a CDS Proxy method, and can serve for proxy construction for other financial variables. Some directions for future research are indicated.
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中文摘要：
监管机构要求金融机构从流动CDS报价中估算交易对手违约风险，以便对场外衍生品进行估值和风险管理。然而，绝大多数交易对手没有流动的CDS报价，需要代理CDS利率。现有方法无法考虑交易对手特定的违约风险；我们建议基于机器学习技术，通过关联非流动交易对手的流动CDS代理来构建代理CDS利率。在测试了8个最流行的分类器家族中的156个分类器后，我们发现一些分类器达到了非常令人满意的准确率。此外，我们对性能进行了排序，并调查了8个分类器系列之间和内部的性能变化。据我们所知，本文是第一次利用机器学习技术对CDS代理构造进行系统研究，也是第一次完全基于金融市场数据的系统分类器比较研究。其发现证实并对比了现有的分类器性能文献。鉴于金融数据具有典型的高度相关性，我们研究了相关性对分类器性能的影响。本文使用的技术应该对寻求CDS代理方法的金融机构感兴趣，并且可以为其他金融变量的代理构建服务。指出了今后的研究方向。
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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：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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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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