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Journal of Monetary Economics

Journal of Monetary Economics 59 (2012) 508–511

0304-39

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Discussion

Comment on: ‘‘ambiguity shifts and the 2007–2008 financialcrisis’’ by Nina Boyarchenko

Itamar Drechsler n

Stern School of Business, New York University, 44 West 4th Street, New York, NY 10012, United States

1. Introduction

The observation that credit spreads on corporate bonds are surprisingly high relative to historical default probabilitiesand recovery rates is known as the ‘‘credit spread puzzle’’ (see Huang and Huang, 2003). Credit spreads had narrowedsteadily in the years before the financial crisis, before widening in dramatic fashion during the onset of the crisis in 2007–08. This was especially true for financial institutions, whose widening credit spreads were at the heart of the financialcrisis. This paper builds a model to explain the high level of credit spreads on financial institutions and their significantwidening during the crisis of 2007–08. The model is based on a desire by the representative investor to make robustinvestment decisions in the debt and equity of financial firms given ambiguity about their current and future states.

I contrast this approach with an explanation for wide credit spreads and frozen credit markets based on investors’imperfect information regarding banks’ balance sheets, coupled with the resulting adverse selection faced by investors.While this explanation and the one centered on investor ambiguity contain several similar-sounding elements, it isimportant to understand the clear conceptual distinction between them. Moreover, they have different policy implications.Before discussing the adverse selection explanation, I first explain the approach taken by this paper, as well as somealternative approaches recently proposed in the literature on the ‘‘credit spread puzzle’’.

2. The ambiguity model

In the model of this paper, the debt of financial firms is valued by a risk neutral representative investor who wants hisdecision making to be robust to the ambiguity he faces about the current and future states of these firms. The growth rate ofthe firms’ cash flows are modeled as following a finite-state Markov chain, with the lowest-value state for a firm correspondingto default. The investor has two kinds of model uncertainty about firms’ cash flows. First, he is uncertain about the probabilitiesof transitioning between the states of the Markov chain. Second, he is also uncertain about what state the firms are currently in.The investor guards against this model uncertainty by making portfolio-choice decisions that are robust to his uncertainty.In turn, this implies a ‘robust’ valuation of the firm’s debt (and hence credit default swap spread) and equity.

The paper models robust decision making using the framework of Hansen and Sargent (see e.g., Hansen and Sargent,2008). Under this framework the investor considers a set of plausible alternative models for the transition probabilities ofthe Markov chain based around a best-guess model called the reference model. The investor also considers alternativeprobability distributions for the current state of the Markov chain, centered on the estimate obtained by standard Bayesianfiltering of the reference model. Two parameters control the degree of robustness desired by the investor and hence the setof plausible alternative models. One parameter controls robustness to uncertainty about the current state, the other to

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ail address: idrechsl@stern.nyu.edu

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uncertainty about the transition probabilities of the Markov chain. The representative investor chooses his portfolio so thatits payoffs are robust across the set of plausible alternative models, and this implies a robust valuation of debt and equity.

In general, the desire for robustness makes the investor overweight the probability of ‘bad’ outcomes relative to hisreference model. In the context of the model of this paper, he tends to (i) place a greater likelihood on currently being in astate that is close to default and (ii) place a greater probability on transitioning to future low cash-flow growth states,which are also close to default. The combined impact of these effects is to increase credit spreads, with (i) leading inparticular to an increase in short-term credit spreads and (ii) causing a greater increase in long-horizon credit spreads.

3. Alternative approaches

Several approaches have recently been advanced in the literature to try to explain the ‘‘credit spread puzzle’’. Chen et al.(2009) propose a habits-based model with the assumption that states of the economy in which risk-aversion is high arealso characterized by a greater likelihood of default and lower recovery rates upon default. The combination of these threeelements means that the representative investor assigns a large risk premium to defaults, since defaults occur morefrequently and are larger when they are also more painful for the investor to bear. This risk premium on defaults helpsgenerate the large credit spreads on corporate bonds. Bhamra et al. (2010), and Chen (2010), create long-run risks basedmodels. Firms default in states of low aggregate growth and high volatility, when their future prospects are dim. Recoveryrates in these states are then low, while the marginal utility of the representative investor is high. Again, this combinationproduces a large risk premium on default and hence wide credit spreads for corporate debt.

Besides the difference in its underlying mechanism, this paper also differs from these other papers in that it is focusedon the financial crisis and on the credit spreads of financial firms. During the crisis both default rates and credit spreadsincreased dramatically. It would be interesting to see a comparison of these alternative models’ ability to explain the leveland evolution of credit spreads for the period of the financial crisis.

3.1. Asymmetric informationþadverse selection

There is another alternative story with the potential to explain the dramatic widening of financial firms’ credit spreadsduring the crisis, in conjunction with the freezing up of credit markets. This explanation is based on information asymmetryand adverse selection with respect to investors. An important aspect of the crisis, which was particularly acute early on, wasuncertainty on the part of investors about the health of banks’ balance sheets. Investors were uncertain about the extent ofbanks’ holdings of ‘toxic’ assets, such as low-grade mortgages. It was also difficult for investors to judge how ‘toxic’ the badassets really were. This uncertainty was not diversifiable across banks since it related in some measure to all of them.

At the same time that investors wanted to know the quality of banks’ balance sheets, banks had strong incentives toconceal bad information from them. For example, if investors realized that a bank, or a group of banks, have more badassets than they suspected, then they would reduce funding to those banks and make it more difficult and expensive forthem to roll over funding or issue new debt. The risk of a bank run would also increase. At the same time, ‘good’ banks,who may want to reveal good information about themselves, would have a very difficult time doing so in a credible waygiven the strong incentives to mislead and the complexity of banks’ balance sheets. A detailed audit by a trustworthy thirdparty could potentially help in this regard, but it is not clear who could represent such a credible and capable entity.

As investors discount banks’ debt and equity to account for this adverse selection, CDS spreads widen relative to what ispredicted by the ‘normal’ relationship between prices and banks’ reported fundamentals. At the same time, concern aboutadverse selection deters banks from lending freely to each other and leads to a freezing up of the interbank lending market.

3.2. Different kinds of ‘uncertainty’

There are similarities in the descriptions of the ambiguity-based model and adverse selection story since both are basedon notions of ‘uncertainty’. Nevertheless, the two explanations are conceptually distinct. A central difference is in thenature of the uncertainty in the two explanations. The idea of ambiguity is that no one knows the true probabilitydistribution for the current state of nature or its dynamics over time. During the onset of the crisis it is indeed plausiblethat there was a high level of uncertainty about how the state of the economy (productivity, employment, output, etcy)would evolve given the limited historical experience with this type of event. Under the ambiguity model, investors and

banks had some estimate but were not too confident in it and wanted to be robust to alternative possibilities.In comparison, under the adverse selection model investors don’t know the state of the banks, banks certainly do know

their own state but either don’t want to reveal it or cannot credibly do so. This type of uncertainty would be resolved ifbanks balance sheets were fully revealed to investors. In contrast, model uncertainty would remain high even if the state ofbanks’ balance sheets became fully known.

Hence, these two types of ‘uncertainty’ are conceptually quite different. However, their impacts are not mutuallyexclusive, and where both are present, ambiguity can exacerbate the adverse selection concern. For example, when, incontrast to historical experience, it became clear that US real-estate prices were falling markedly, concerns arose aboutwhat impact this would have on mortgage defaults and economic growth. Ambiguity about the relationship between

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real-estate prices and these important quantities could then intensify investors’ concerns about the level of banks’exposure to these kinds of risks and hence amplify the adverse selection problem.

4. Policy implications

A large increase in bank credit risk leads to high CDS spread and fuels a banking crisis. Banks’ funding constraintstighten as the availability of funding to the whole banking system decreases, interbank lending stalls, and the risk of bankruns increases. The lack of bank lending hurts output through the ‘‘bank lending channel’’, whereby the resulting ‘creditcrunch’ makes it impossible for firms to attain funding for profitable projects. Hence, the increase in bank CDS spreads hasimportant welfare implications, so that alleviating any frictions that increase bank credit risk is an important target forgovernment policy.

The policy implications of ambiguity and adverse selection are very different. Since information asymmetry betweeninvestors and banks fuels the adverse selection concerns, policy should aim to reduce this asymmetry. One potential waythis could be done is to have a credible third party investigate banks’ asset holdings and publicly reveal this information ora summary of it. Bank regulators and the Federal Reserve could potentially play this role. A related question is whethergovernment could help to create a mechanism for good banks to credibly reveal their quality to investors, as this could alsobe valuable in reducing the level of information asymmetry.

In contrast, it is not obvious that the purest form of the ambiguity model has any policy implications, since there is notnecessarily anything that can be done regarding the lack of data about the true underlying economic dynamics. In otherwords, there is nothing we can do to get Nature to show us its ‘books’. On the other hand, if the interpretation of theambiguity model is relaxed then there could be some scope for policy. For example, if it is the case that investors (andbanks) are unable to fully gather or process important information that is in practice attainable, then government couldhelp by bringing this information together with the expertise to interpret it. This could help investors obtain a betterunderstanding of the current economic state and its potential future paths. For instance, it seems plausible that early in thecrisis investors did not have much knowledge or understanding of issues such as pre-crisis lending standards, the shadow-banking system, and the extent of the government’s powers for dealing with crisis events. Centralizing the availability andprocessing of public information relevant to these questions could potentially have reduced uncertainty along importantdimensions. In contrast, under adverse selection the issue is not aggregate uncertainty, but asymmetric informationbetween banks and investors.

4.1. Bank stress tests

A notable step taken by both European and US regulators over the last several years has been to conduct bank stresstests. Since 2009 the European Banking Authority has held annual stress tests, which require banks to calculate anestimate of their potential losses and resulting regulatory capital ratios under some adverse economic scenarios. Bankshave been further required to disclose data (albeit limited in nature) about their holdings of various assets. This providesregulators and investors with a view into the banks and allows them to form their own estimates for stress-scenario losses.

The release of the stress test results in 2010, when the majority of the banks were reported to safely pass, was followedby a temporary decrease in bank credit spreads. However, the tests were heavily criticized due to the fact that banks’ datawas self-reported, and oversight was conducted by national bank regulators, who are interested in positive outcomes fortheir banks. The scenarios envisaged by the stress-tests were also criticized for being too soft. The 2011 tests involvedsomewhat more oversight and disclosure, but were still open to the same criticism. Their disclosure was followed by, ifanything, a minor decrease in credit spreads.

An important question is why the disclosure of stress test results has (or could have) value in reducing credit spreads(on average). The adverse selection story predicts that it is because these tests reveal private information about the healthof banks’ balance sheets, thereby reducing the information asymmetry between investors and banks. The ambiguity modelpredicts that stress tests can help to reduce ambiguity by quantifying the worst-case scenario of the banking sector. Athird, alternative possibility is that there is nothing inherently of value in the stress tests themselves, but that investorsinterpreted a passing grade for a bank as signaling commitment by either the European sovereigns or the ECB to bail outthe bank in the future if it becomes necessary. Again, these possibilities are not mutually exclusive. If both adverseselection and ambiguity are in effect, a stress test can have value for both of the reasons mentioned above.1

5. Conclusion

Understanding the factors which drove the increase in credit spreads during the financial crisis is an interestingquestion whose answer potentially carries important policy implications. The ambiguity-based model proposed by this

1 The question of self-reporting and the credibility of the tests is also a difficult one. In Ireland the government took the unusual step of paying the

investment firm Blackrock to do on-site audits of banks books and provide values for individual assets. The resulting estimated costs for recapitalizing the

Irish banks were significantly higher than previous estimates.

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paper provides one potential explanation with intuitive appeal. An alternative model, based on asymmetric informationand adverse selection, provides another explanation and can also account for the freezing up of interbank lending. The twoexplanations sound similar in certain ways since both focus on ‘uncertainty’. However, conceptually they are clearlydistinct and have potentially very different policy prescriptions. In practice though, it may be difficult to distinguish whichis in effect. Moreover, it also seems very plausible that both mechanisms will be present and actually reinforce each other.

References

Bhamra, H., Kuehn, L.-A., Strebulaev, I., 2010. The levered equity risk premium and credit spreads: a unified framework. Review of Financial Studies 23(2), 645–703.

Chen, H., 2010. Macroeconomic conditions and the puzzles of credit spreads and capital structure. Journal of Finance 65 (6), 2171–2212.Chen, L., Collin-Dufresne, P., Goldstein, R., 2009. On the relation between the credit spread puzzle and the equity premium puzzle. Review of Financial

Studies 22, 3367–3409.Hansen, L., Sargent, T., 2008. Robustness. Princeton University Press.Huang, J., Huang, M., 2003. How much of the corporate-treasury yield spread is due to credit risk? Working paper, Stanford University.