What Drives the Cost of Default Insurance?

26 Apr 2020 | Corporate Finance, The World Economy

One of the many negative consequences of the COVID-19 pandemic is that companies may default. Companies going bankrupt are indeed less important than deaths, but they still have human consequences. Employees lose jobs, suppliers lose customers (and then may themselves default), and investors lose money. We often downplay the third effect for two reasons. One is that that investors are nameless, faceless capitalists – but they include pension funds investing on behalf of ordinary citizens. The second is that we shouldn’t sympathize if investors lose, because they knew what they were getting themselves in for when they invested.

That’s true – and that’s why debtholders often buy insurance against default, known as a credit default swap (“CDS”). For example, a pension fund that owns a United Airlines bond that matures in 2025 might be concerned that this bond will default. Thus, it can buy a CDS on that bond from a seller, such as a hedge fund. The buyer of the CDS pays the seller a regular premium, similar to an insurance premium. If the bond defaults, the seller pays the buyer the difference between the face value of the bond and its market value – since this is the amount that the buyer has lost through the default. What counts as a “default” is defined in the CDS contract, but typically includes the company (United Airlines in the above example) either (a) going bankrupt, (b) failing to make interest payments, or (c) restructuring its debt in a way that harms existing creditors.

A key question is – what drives the cost of a CDS? This is critical as it affects investors’ willingness to insure against losses, and thus how vulnerable they will be to eventual defaults. A paper by Antje Berndt, Rohan Douglas, Darrell Duffie and Mark Ferguson studies this question. The obvious answer is that the cost of a CDS depends on expected losses, so it will rise in the COVID-19 crisis purely due to the increased risk of default. But it’s actually more than that. The full cost is as follows:

CDS Rate = Expected Loss Rate + Credit Risk Premium

The first component is the Expected Loss Rate. For example, if there is a 2% chance that United Airlines will default, and the price of the United Airlines bond will fall by 50% upon default, then the Expected Loss Rate is 1%.

However, the CDS rate will typically be higher than the expected loss rate. This difference is the Credit Risk Premium, which arises for two reasons:

  1. The United Airlines bond will most likely default in recessions (such as the one that will almost certainly follow COVID-19). So, the CDS seller has to pay up in bad times – when the rest of its investments are likely doing poorly and so making this payment is particularly painful. As a result, the CDS seller charges a premium for this risk.
    • This is similar to why stocks offer a much higher expected return than bonds – to compensate for the fact that stocks perform poorly in recessions. For further detail, please see the Managing Editor’s blog on How Macroeconomic Conditions Affect Stock Prices.
  2. The CDS is illiquid. There may be few sellers of CDS, and many buyers. Thus, the sellers can charge a premium.

Substantial Variation in Corporate Credit Risk Premia

The authors study 500 firms from 2002-2015 and find that corporate credit risk premia vary substantially over time. They scale the Credit Risk Premium by the Expected Loss Rate, to calculate the premium per unit of expected losses. This ratio peaked in 2002, during the Global Financial Crisis of 2008-9, and during the second half of 2011 (which was marked by both the European sovereign debt crisis and the US government’s debt ceiling crisis). Its lowest level was 0.7 in March 2005 and its highest was 9.7 in January 2009 – a fluctuation of more than a factor of ten.

Figure 1 illustrates this substantial variation. While expected losses are relatively stable over time, the CDS rate fluctuates wildly. It also shows that the Credit Risk Premium / Expected Loss Rate ratio is countercyclical. It is higher in recessions because they are more risky. Moreover, it is more countercyclical for investment grade bonds than high yield bonds. This is consistent with recessions leading to a decrease in investors’ willingness to sell bond insurance across the board, in an indiscriminate way that does not take into account the fact that investment grade bonds are less risky. Since investment grade bonds are affected to roughly the same degree as high yield bonds, and they are less risky to begin with, the premium per unit of risk rises more in recessions.

In addition, corporate credit risk premia vary substantially between bonds. Without scaling by the Expected Loss Rate, the Credit Risk Premium is less than 10 basis points of bond principal for Aaa rated bonds, and more than 700 basis points for Ca-C rated bonds. This is logical, since lower-rated bonds are riskier. More surprising is the pattern when scaling by the Expected Loss Rate, i.e. taking into account the fact that lower-rated bonds are riskier. Now, the (scaled) Credit Risk Premium is highest for mid-rated bonds (4.0 for Ba bonds) and 1.4 for Aaa bonds and 2.1 for Ca-C bonds.

Scaled premia also vary across industries, hitting a high of 8.5 for utilities and a low of 0.64 for financials. These differences can be largely explained by the difference in credit ratings between industries.

Explaining the Variation

The authors’ next step is to explain why corporate credit risk premia vary so substantially. In this step, they study the ratio of the CDS Rate to the Expected Loss Rate. They find that a reasonable component (26%) of the variation in this ratio can be explained by the expected loss rate itself. A 10% increase in this rate translates into a 4.2% decrease in the ratio. Credit spreads, per unit of expected losses, are decreasing in expected losses.

When adding firm and month fixed effects (variables to capture firm-specific or month-specific factors), the authors can now explain 84% of the variation in the ratio. The final step is to “open the black box” of what is behind the fixed effects. They thus consider two types of predictors for the ratio:

  1. Firm-Specific Predictors (to open the black box of firm fixed effects). These include
    • Refined Ratings. These are the raw Moody’s credit ratings, but adding (subtracting) one notch if the rating is on positive (negative) watch, and two if it is on upgrade (downgrade) watch. (The authors find that refined ratings exhibit much greater time-series variation than raw ratings). A higher refined rating is associated with a lower CDS Rate / Expected Loss Rate ratio, and thus lower scaled credit risk premia. This suggests clientele effects: many investors are constrained from buying high-yield (lowly-rated) bonds, or unwilling to do so due to their lack of sophistication. Thus, high-yield bonds end up in the hands of few investors, who bear a lot of risk and thus have a high demand for CDS protection
    • Temporary Clientele Effects. These are measured by recent credit upgrades, downgrades, and changes in investment grade vs. high-yield status. A recent downgrade leads to an increase in scaled credit risk premia, for similar intuition to the prior point. This effect of changes in ratings is over and above the effect of levels, although quantitatively much smaller, and suggestive of slow-moving capital (see also Duffie’s Presidential Address to the American Finance Association). A downgrade may lead to some investors fleeing from a bond, and new investors being slow to move in and purchase it.
    • Implied Volatility. A higher level of implied volatility is associated with higher scaled credit risk premia, since default is likelier. In addition, a higher implied volatility “smirk” (the ratio of out-of-the-money to at-the-money implied volatilities, a measure of left-tail risk) is associated with higher scaled credit risk premia.
    • Industry Fixed Effects. Scaled credit risk premia are higher in financial services, technology, telecoms and utilities, and lower in healthcare.
  2. Macroeconomic Predictors (to open the black box of month fixed effects). These include
    • Five-Year Treasury Rate. A lower interest rate is associated with higher scaled credit risk premia, consistent with prior literature
    • University of Michigan Consumer Sentiment Index. Higher consumer confidence is associated with lower scaled credit risk premia
    • CDS Market Liquidity. The authors proxy for this using the aggregate amount of CDS outstanding. A higher amount (indicative of higher liquidity) is associated with lower spreads

Adding these firm-specific and macroeconomic predictors to the Expected Loss Rate, and dropping the fixed effects, explains 82% of the variation in the CDS Rate / Expected Loss Rate ratio, very close to the 84% explained by fixed effects. Thus, the paper successfully opens the black box of what determines the substantial variation in corporate credit risk premia, showing that these premia vary with firm-level and macroeconomic determinants in a way consistent with existing theories and economic intuition.