Rainer Jankowitsch, Florian Nagler and Marti G. Subrahmanyam
The Determinants of Recovery Rates in the US Corporate Bond Market
Journal of Financial Economics | Volume 114, Issue 1 (Oct 2014), 155-177

The recovery rate in the event of default is an important risk factor in pricing financial contracts exposed to credit risk. More recently, the occurrence of several default events has highlighted the stochastic nature of recovery rates for corporate bonds. It is, therefore, important to understand the determinants of this risk factor in greater detail. In this paper, we examine the recovery rates of defaulted US corporate bonds, based on a complete set of transaction data over the time period from 2002 to 2010 obtained from the Trade Reporting and Compliance Engine (TRACE), a database maintained by the Financial Industry Regulatory Authority (FINRA). This data source is rather unique for an over-the-counter (OTC) market since, in almost all other cases, price information must typically be obtained either from an individual dealer's trading book, which provides a very limited view of the market, or by using bid-ask quotations, which are often unreliable in OTC markets. In particular, we investigate the trading microstructure of defaulted bonds for a broad set of default event types covering formal bankruptcy procedures, out-of-court restructurings and downgrades to default status by rating agencies representing payment defaults and unlikely-to-pay events. This analysis allows us to provide reliable market-based estimates of the recovery rates, study liquidity and, hence, study price formation for individual defaulted bonds.

Market-based recovery rates are highly relevant for investors

The global financial crisis has highlighted the importance of credit risk in the pricing of financial contracts and emphasized the multifaceted nature of its key determinants: the probability of default and the recovery rate in the event of default. Traditionally, credit risk modeling has focused on the probability of default, while the recovery rate has been set to parametric values that do not necessarily recognize its potential cross-sectional and time-series variation. However, the magnitude and variability of defaults, especially during the global financial crisis, have emphasized the importance of obtaining more precise estimates of recovery rates, and explaining their variation across issues and issuers. It is now intuitively understood that recovery rates are potentially driven by many different factors: endogenous variables (such as the specific characteristics of the assets of the firm and industry), or exogenous factors (such as overall macroeconomic conditions and market liquidity). It is important, therefore, to document the determinants of this risk factor, and to analyze their interaction effects with other dimensions of default risk. We investigate these relationships at the issue and obligor levels, for the US corporate bond market.

Specifically, we provide a detailed analysis of the microstructure of trading in defaulted bonds, working with a complete set of default events over the most recent decade, offering crucial insights. The study of market prices and trading behavior around different default events is important as many institutional investors are directly exposed to these post-default prices, e.g., because they have to immediately liquidate their positions, deliver the bonds through the settlement of credit default swaps (CDS) positions, or mark down the values of the defaulted bonds on their balance sheets. Furthermore, the examination of market prices provides us the opportunity to analyze all default events (including, e.g., distressed exchanges), and not only the outcomes of formal bankruptcy procedures, often revealed only years after the actual filing dates. Overall, this analysis allows us to discuss trading activity at different stages following default and to derive market-based estimates of recovery rates, which are of fundamental relevance to various market participants.

Trading activity reveals temporary sell-side pressure after default

We examine the trading activity of the defaulted bonds, as defined by traded prices and volumes, in a time window starting 90 days, before and ending 90 days after, the observed default event date (see Figures 1 and 2). We find that, although the price level is already rather low before the default event, the traded price falls significantly to its lowest level on the default day itself, to around 35% of face value, on average. The price recovers, in the first 30 days following default, to about 42% of face value and shows a less volatile evolution, thereafter. Furthermore, we find that the trading volume of a defaulted bond is relatively high on the default event day, providing evidence of temporary sell-side pressure, as prices are low on the default event date. This high level of trading activity dies down, within the first 30 days after default, to pre-default levels. Thus, this time window apparently represents the relevant trading period following default, in which investors split up and sell larger positions in defaulted bonds. Based on these findings, we define the recovery rate of a defaulted bond as the average daily traded price per unit of face value, over the default day and the following 30 days, covering the phase of high trading activity, as we conjecture that the price evolution in this time window is mostly driven by the default event itself.

1: Trading activity in defaulted bonds
jankowitsch-nagler-subra-1
This figure shows average trade prices on the default day and in the time window from 90 days before to 90 days after default.
2: Trading activity in defaulted bonds
jankowitsch-nagler-subra-2
This figure shows average volumes, as well as the average number of trades per bond, on the default day and in the time window from 90 days before to 90 days after default.

Default event type, industry and seniority drive recovery rate

Recovery rates are studied across bonds along various dimensions.

3: Chapter 11 restructuring and distressed exchange
jankowitsch-nagler-subra-ch11
This figure shows average trade prices for Chapter 11 restructuring on the default day and in the time window from 90 days before to 90 days after default.

 

First, we analyze them across different default event types, revealing that distressed exchanges have the highest recovery rates of 51.3% on average, whereas bankruptcy filings show significantly lower recoveries with an average of 37.1% (Figures 3 and  4 shows the evolution of prices for these two types of default events.) This finding provides evidence that bondholders are confronted with lower recoveries in formal legal procedures compared to out-of-court restructurings.

4: Chapter 11 restructuring and distressed exchange
jankowitsch-nagler-subra-de
This figure shows average trade prices for distressed exchanges on the default day and in the time window from 90 days before to 90 days after default.

Second, we find significant differences in recoveries between the default grades of the major rating agencies, which represent payment defaults and unlikely-to-pay events, respectively; in particular, the rating frameworks of Moody's and Fitch seem to incorporate recovery rate information to a greater extent than that of Standard and Poor's, which is focused on the probability of default. Third, we find that, among non-financial industries, utility and energy-related firms recover the most in default (e.g., electricity 48% or oil & gas 44.4%), while retailers recover the least at 33.4%. Interestingly, among financial firms, the banking and credit & financing industries recover the most in default (56.6%), whereas the financial services industry recovers the least (10.6%). Fourth, in terms of seniority levels we find, as expected, that secured bonds (49.3%) recover more than unsecured (39.1%) and subordinated bonds (15.1%). Fifth, we document substantial time-series variation in recoveries, as shown in Figure 5.

5: Recovery rates over time
jankowitsch-nagler-subra-recovrate
This figure shows the time-series of average recovery rates (quarterly moving average) in the US corporate bond market over the period from July 2002 to October 2010.

Many other factors, especially liquidity, are important determinants

In the main part of our analysis, we employ regression models to explain the variation in recovery rates, using a comprehensive set of bond characteristics, balance sheet ratios, macroeconomic variables and liquidity measures. To begin with, we quantify the liquidity of defaulted bonds, applying different measures in our analysis, and explore their implications for recovery risk. These implications turn out to be of particular importance, since defaulted bonds are potentially illiquid. Consequently, we study to what extent changes in the underlying liquidity, following default, account for the observed post-default price evolution, as default might induce pressure on prices.

Overall, our regression analysis explains 66% of the total variation in recovery rates, with all four groups of variables contributing to the explanatory power. We demonstrate a clear link between the defined bond-specific liquidity measures and their recovery rates. The analysis reveals that trading in defaulted bonds is extremely costly. In particular, when measuring the transaction costs of trading using the price dispersion measure, estimated average transaction costs in defaulted bonds are 280 bp—about seven times compared to around 40 bp for non-defaulted bonds. Moreover, in our analysis, we document that illiquid bonds with higher transaction costs recover significantly less following default—an increase by 100 bp in transaction costs leads to a decrease in recovery rates by around 5% of face value.

Analyzing bond characteristics, we find that bonds that can be delivered into a CDS contract have a significantly higher recovery rate, possibly because of increased demand from protection buyers, who are required to physically deliver the underlying bond. In addition, we find that bond covenants significantly affect the level of the recovery rate. In particular, investment and financing covenants that provide protection for existing bondholders against potential adverse firm actions are important determinants. That is, restrictions on the investment and financing policy are effective tools that creditors can use to increase their recovery rates.

As for the other firm characteristics, among balance sheet ratios, we find significant effects for those ratios that are motivated by structural credit risk models, i.e., the higher is the equity ratio, and the lower the default barrier, the higher will be the recovery rate. Analyzing macroeconomic variables reveals a particularly strong effect for the market-wide and industry-specific default rates. Thus, a high default rate in the market as a whole, a systematic risk factor, or a high industry-specific default rate, as an indicator of industry distress, are both linked to significantly lower recovery rates for individual bonds, following default. Along the same lines, we find a positive relation between short-term interest rates, an indicator of the business cycle, and recovery rates.

Conclusion

In this paper, we examine the recovery rates of defaulted bonds in the US corporate bond market over the time period from 2002 to 2010. We provide a comprehensive analysis, going beyond the results that have been presented in the prior literature. We study the microstructure of trading activity and offer detailed insights into the stochastic nature and drivers of recovery rates by analyzing a broad set of explanatory variables, rather than only providing evidence on the effects of any one factor. We document temporary price pressure with high trading volumes on the default day and the following 30 days, and low trading activity at pre-default levels thereafter. Based on these observations, we quantify and analyze market-based recovery rates in the period representing the high trading activity window are estimated. We explore the relation between the recovery rates and these measures, considering additionally a comprehensive set of bond characteristics, firm fundamentals and macroeconomic variables. Our results on the effects of liquidity are particularly noteworthy, since they highlight the effects of liquidity on recovery rates.


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