英文标题：
《Transaction Cost Analytics for Corporate Bonds》
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作者：
Xin Guo, Charles-Albert Lehalle and Renyuan Xu
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最新提交年份：
2021
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英文摘要：
  Electronic platform has been increasingly popular for the execution of large orders among asset managers dealing desks. Properly monitoring each individual trade by the appropriate Transaction Cost Analysis (TCA) is the first key step towards this electronic automation. One of the challenges in TCA is to build a benchmark for the expected transaction cost and to characterize the price impact of each individual trade, with given bond characteristics and market conditions.   Taking the viewpoint of an investor, we provide an analytical methodology to conduct TCA in corporate bond trading. With limited liquidity of corporate bonds and patchy information available on existing trades, we manage to build a statistical model as a benchmark for effective cost and a non-parametric model for the price impact kernel. Our TCA analysis is conducted based on the TRACE Enhanced dataset and consists of four steps in two different time scales. The first step is to identify the initiator of a transaction and the riskless-principle-trades (RPTs). With the estimated initiator of each trade, the second step is to estimate the bid-ask spread and the mid-price movements. The third step is to estimate the expected average cost on a weekly basis via regularized regression analysis. The final step is to investigate each trade for the amplitude of its price impact and the price decay after the transaction for liquid corporate bonds. Here we apply a transient impact model (TIM) to estimate the price impact kernel via a non-parametric method.   Our benchmark model allows for identifying and improving best practices and for enhancing objective and quantitative counter-party selections. A key discovery of our study is the need to account for a price impact asymmetry between customer-buy orders and consumer-sell orders.
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中文摘要：
在资产管理公司交易台中，电子平台在执行大额订单方面越来越受欢迎。通过适当的交易成本分析（TCA）正确监控每一笔交易是实现电子自动化的第一个关键步骤。TCA面临的一个挑战是，根据给定的债券特征和市场条件，为预期交易成本建立一个基准，并描述每个交易的价格影响。从投资者的角度出发，我们提供了一种在公司债券交易中进行TCA的分析方法。由于公司债券的流动性有限，现有交易的信息不完整，我们设法建立一个统计模型作为有效成本的基准，并建立一个非参数模型作为价格影响核心。我们的TCA分析是基于跟踪增强数据集进行的，由两个不同时间尺度的四个步骤组成。第一步是确定交易发起人和无风险原则交易（RPT）。对于每个交易的估计发起人，第二步是估计买卖价差和中间价变动。第三步是通过正则化回归分析估计每周的预期平均成本。最后一步是调查每笔交易的价格影响幅度以及流动公司债券交易后的价格衰减。在这里，我们应用瞬态影响模型（TIM）通过非参数方法估计价格影响核。我们的基准模型允许确定和改进最佳做法，并加强客观和定量的交易对手选择。我们研究的一个关键发现是，需要考虑客户购买订单和客户销售订单之间的价格影响不对称。
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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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