原标题：市场隐含信用风险指标：提前识别风险与变化Market implied credit risk indicators: identify risks and changes in advance 来源：彭博Bloomberg
牛牛敲黑板：
总体而言，市场隐含违约概率可加强基于公司基本面的传统风险分析。这些市场隐含指标有助于信用风险和对手方风险的主动管理，尤其是在单日损失天差地别的危机关头。由于基于传统信用评级的分析存在滞后性，风险敞口的主动管理需要使用市场隐含的分析视角。
新冠疫情将全球信用市场拖入了新的不确定性和压力时期，且展示出其与近年的危机都不相同的特点：在之前的全球金融危机中，金融信用逐渐恶化并最终溢出到了其它行业；而眼前由疫情引发的危机则迅速引发各行各业对信用质量的深度担忧。
仅在短短数日之内，原本相对积极的交通运输业、零售业和能源业前景展望，就变成了未来数月内将出现违约潮的警告预测。这一系列快速发生的事件不仅影响了信用评级和违约，而且还影响了底层投资标的。
在2020年初的数周内，交通运输业似乎还是安全的投资领域，航空业当时确实也是表现最强劲的行业之一，提供着较低的隐含波动率和极少数的评级下调，且股价也呈持续向上趋势（图1）。
但随后新冠疫情开始在全球蔓延。
图1：2019年1月-2020年2月道琼斯运输平均指数表现
虽然航空公司的基本面和财务报表并未立刻出现变动，但市场感知却在一夜之间发生了改变。交易员亲身感受到疫情引起的各种变化，从呆在办公室到居家办公，乃至一系列的全球旅行禁令，都使他们立刻明白运输/旅游业将受到重大影响。
虽然燃油成本、公司财务和负债等种种指标显示：由于财务报表的优化以及恰当的损益率，板块信用质量前景欣欣向荣。但是债券价格对全球旅行需求普降迅速作出了回应，暗示高压力和潜在的流动性问题。
这种市场数据能先于信用评级下调很长时间，且迅速量化市场对信用程度的感知，因此极具价值。彭博利用这一数据来计算市场隐含违约概率（MIPD）、密切跟踪市场上重大事件的发生和进展，进而在广泛的公司和行业范围内识别类似问题。
图2：美国全球航空ETF（JETS）的走势折射出债券定价迅速对全球新冠疫情作出的反应。
在某些情况下，基础市场数据最早在信用事件发生前45天就可预测到评级下调。
对历史数据进行分析，特别对2020年3月这样的高压期进行分析，就可以看出：市场事件发生和信用评级调整之间一直存在着时间差。
例如，在新冠疫情爆发前的2月份，多个评级机构仍确认了对美联航的信用评级和乐观前景的预测，一直到3月下旬才宣布了下调信用评级。有些机构甚至直到4月初，也就是疫情风险已广为人知时，才宣布了下调评级。这相比MIPD（图3）的指标存在着30-45天滞后。
虽然传统信用评级在金融风险管理方面仍发挥着重要的作用，但全球新冠疫情凸显了对市场数据驱动型信用指标与日俱增的需求。这些指标可以作为评级数据的补充，为管理和预测信用风险提供更为全面的手段。
除了MIPD，彭博还通过结合资本结构数据、公司财务数据和股权定价来计算发行人层面的违约概率，即：彭博DRSK数据解决方案。
图3展示了MIPD、DRSK以及信用评级对2020年3月美联航事件作出的反应。MIPD和DRSK均为即将发生的信用评级下调及相关股票的价格变化提供了清晰的早期预警指标。在市场低迷和失衡的环境下，风险水平可能在瞬间发生变化。
因此在分析中结合这类市场驱动型风险指标，对于捕获市场情绪的每日变动十分重要。
图3：美联航MIPD、DRSK和信用评级在新冠疫情引发的市场波动期间的比较。
这种分析不仅停留在理论层面，根据一份面向约500名在2020年参与了彭博专项活动的客户的调查：客户已经在工作流程中大范围纳入市场隐含信用指标：
通过识别CDS、公司基本面数据和隐含数据之间的错配来创建早期信号，因为数据差异可能意味着投资机遇
通过采用隐含违约概率指标，了解投资组合或投资标的在当前市场条件下的定位
通过纳入隐含数据丰富和加强违约模型、损失预测和压力情景分析
建立稳健的信用和交易对方风险管理监控框架来为投资组合设定限制，以更低的风险获取更高的回报。
总体而言，市场隐含违约概率可加强基于公司基本面的传统风险分析。这些市场隐含指标有助于信用风险和对手方风险的主动管理，尤其是在单日损失天差地别的危机关头。由于基于传统信用评级的分析存在滞后性，风险敞口的主动管理需要使用市场隐含的分析视角。
编辑/Phoebe
Niuniu knocks on the blackboard:
Overall, the implied default probability in the market can strengthen the traditional risk analysis based on corporate fundamentals. These market implied indicators are helpful to the active management of credit risk and counterparty risk, especially in the crisis of one-day loss. Because of the lag in the analysis based on traditional credit rating, the active management of risk exposure needs to use the analytical perspective implied by the market.
The COVID-19 epidemic dragged the global credit market into a new period of uncertainty and pressure, and showed its different characteristics from the crisis in recent years: in the previous global financial crisis, financial credit gradually deteriorated and eventually spilled over to other industries; the current crisis caused by the epidemic quickly triggered deep concerns about credit quality in various industries.
In just a few days, the relatively active outlook for transport, retail and energy has become a warning forecast of a wave of defaults in the coming months. This series of rapid events not only affect credit ratings and defaults, but also affect the underlying investment targets.
Transport seemed to be a safe place to invest in the early weeks of 2020, and aviation was indeed one of the strongest performers at the time, offering low implied volatility and a handful of rating downgrades. And share prices continue to show an upward trend (figure 1).
But then the COVID-19 epidemic began to spread around the world.
Figure 1: performance of the Dow Jones Transportation average from January 2019 to February 2020
Although the airline's fundamentals and financial statements did not change immediately, market perception changed overnight. Traders experienced first-hand the changes caused by the epidemic, from staying in the office to working from home to a series of global travel bans, which made them immediately understand that transport / tourism would be significantly affected.
Although various indicators such as fuel costs, corporate finance and liabilities show that the sector's credit quality prospects are booming due to the optimization of financial statements and the appropriate profit and loss rate. But bond prices have responded quickly to the fall in global travel demand, suggesting high pressure and potential liquidity problems.
This kind of market data is valuable because it can be downgraded long before the credit rating is downgraded and can quickly quantify the market's perception of credit. Bloomberg uses this data to calculate the market implied default probability (MIPD), closely track the occurrence and progress of major events in the market, and identify similar problems across a wide range of companies and industries.
Figure 2: the trend of US Global Aviation ETF (JETS) reflects the rapid response of bond pricing to the global COVID-19 epidemic.
In some cases, underlying market data can predict a downgrade as early as 45 days before the credit event.
By analyzing the historical data, especially the high pressure period such as March 2020, we can see that there has been a time gap between the occurrence of market events and the adjustment of credit rating.
For example, in February, before the COVID-19 outbreak, several rating agencies still confirmed their forecasts for United's credit rating and optimistic outlook, and did not announce a downgrade until late March. Some agencies did not even announce downgrades until early April, when the risk of the outbreak was well known. This has a lag of 30-45 days compared with the index of MIPD (figure 3).
Although traditional credit rating still plays an important role in financial risk management, the global COVID-19 epidemic highlights the growing demand for market data-driven credit indicators. These indicators can be used as a supplement to the rating data and provide a more comprehensive means for managing and forecasting credit risk.
In addition to MIPD, Bloomberg calculates the default probability at the issuer level by combining capital structure data, corporate financial data and equity pricing, namely:Bloomberg DRSK data solution。
Figure 3 shows the response of MIPD, DRSK and credit ratings to the United Airlines incident in March 2020. Both MIPD and DRSK provide clear early warning indicators for upcoming credit rating downgrades and price changes in related stocks. In an environment of market downturn and imbalance, the level of risk may change in an instant.
Therefore, it is very important to combine these market-driven risk indicators in the analysis to capture the daily changes in market sentiment.
Figure 3: comparison of United's MIPD, DRSK and credit ratings during the market volatility caused by COVID-19 's epidemic.
This analysis is not only theoretical, according to a survey of about 500 customers who participated in Bloomberg events in 2020: customers have widely incorporated market implicit credit indicators into their workflows:
Create early signals by identifying mismatches between CDS, corporate fundamental data, and implied data, as data differences may mean investment opportunities
By using the implied default probability index, we can understand the positioning of the portfolio or investment target under the current market conditions.
Enrich and strengthen default models, loss forecasts and stress scenario analysis by incorporating implied data
Establish a robust credit and counterparty risk management monitoring framework to set limits on the portfolio and get higher returns with lower risk.
Overall, the implied default probability in the market can strengthen the traditional risk analysis based on corporate fundamentals. These market implied indicators are helpful to the active management of credit risk and counterparty risk, especially in the crisis of one-day loss. Because of the lag in the analysis based on traditional credit rating, the active management of risk exposure needs to use the analytical perspective implied by the market.
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