1. Credit Spreads and the Severity of Financial Crises Krishnamurthy and Muir Discussion by Robin Greenwood

2. Main Idea: Crisis Research 2.0 Many of the empirical papers that either forecast crises or uses crises to forecast X are a bit ad hoc – Selecting crisis dates in particular involves judgment Supplement crisis data with (impressive data on) credit spreads from historical sources Can this data help us say anything about crises? – Yes! Cross-sectional variation helps – Helps sort crises ex ante as to which ones will be particularly bad – Helps look at role of expectations

3. Position of the Financial Sector

4. Main Empirical Innovations GDP(t+1) = a + b*CrisisDummy(t)*Spread(t) Cross-sectional variation in the spread across crises is useful for forecasting impact of the crisis Also shows that spread crises preceded by credit growth turn out poorly for subsequent GDP, but unclear whether the credit spread adds anything here – Does not run horse race compared to traditional crisis variables

5. Table 3 Credit spreads are mainly useful in forecasting output during financial events During garden variety recessions, the credit spread is not particularly useful Poses a bit of a puzzle in terms of the forward looking nature of the credit spread

6. Which Spread Crises Turn Out Badly? Table 11 Regress GDP growth on dummy for SpreadCrisis and SpreadCrisisxLaggedCreditGr Bottom panel just uses credit growth with nearly identical results Is the paper saying that we can ignore whether a financial crisis actually happened? I would have liked a bit more nuance here, perhaps showing quantile regressions – Most of the time, credit growth is good for GDP growth, except when it leads to financial crisis

7. Comment 1: Timing of the Credit Spread

8. Crisis Forecasting & the Credit Spread

11. Forecasting is Hard In the future, when financial crises occur, we will be watching them in real time Which credit spread will be relevant for forecasting GDP? When should we measure it? – At the onset of the crisis? – 6 months in? – Data from the last crisis show that this is not at all obvious – Paper is not very clear about exact timing of this measurement

12. Comment 2: Value Added of Incorporating Credit Spreads

13. Credit Growth and Credit Spreads Paper shows very clearly that credit spreads help forecast impact of crisis on economy Less clear that there is much new in the results the paper shows using credit spreads We know from many other papers that credit growth is associated with – Financial crises (Schularik and Taylor) – Low subsequent returns on credit (Greenwood and Hanson) and equities (Baron and Xiong) Paper does not show very persuasively that using credit spreads increases the information content of credit growth for forecasting outcomes Would be helpful to show some horse races (like in Table 7, but using GDP growth as the dependent variable) with crisis measures vs. the authors’ augmented crisis measures

14. Forecasting Power Crisis forecasting literature often focuses on R- squared concept, on which the paper is largely silent – How much of an edge are credit spreads giving us in forecasting?

15. Comment 3: Forecasting the Crisis vs. The Impact of the Crisis Conditional of it Happening

16. Paper’s perspective on expectations Crisis itself is (not very) forecastable, but high credit growth before the event leads to fragility, so that if a crisis unfolds, credit spreads quickly change “It occurs at first very slowly, then all at once.” - Ernest Hemingway describing the process of going broke I would have liked help in separately unpacking the – Forecastability of the crisis itself Is the “surprise” predictable? – Fragility of the crisis Results in Lopez-Salido, Stein, and Zakrajsek (2015) suggest that it is the forecastability of credit spreads, rather than fragility, that is the driving factor forecasting GDP in the US Could easily be extended using the authors’ database

17. My Preferred Crisis Narrative Periods of credit growth are driven by investor optimism – Extrapolation of GDP growth; extrapolation of past default rates – One result is misallocation of credit Higher credit growth forecasts low returns on average In some particularly severe incidents, losses affect financial institutions, resulting in a crisis Can the paper reject this narrative?

18. Are Credit Spreads too low?

19. Are credit spreads too low? A more natural way to capture this is using return forecasting regressions – Greenwood and Hanson (2013) – Lopez-Salido, Stein, and Zakrajsek (2015) Suppose we start with baseline view that returns to credit are constant over time Then “forecastable part” of returns is then the amount that credit spreads are too low

20. Table 4: Panel A Quantity vs. Quality Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary Credit growth of low quality firms is most useful for forecasting returns Quality beats Quantity in a horserace Differential debt growth of low vs. high quality firms is a strong predictor →Similar results for HYS

21. Open Questions The calm before the storm “It occurs at first very slowly, then all at once.” - Ernest Hemingway describing the process of going broke Credit growth vs. Credit Levels The role of expectations in driving the crisis in the first place

22. “The US housing marketplace developed an axiom over time. Since housing prices had never fallen, housing prices could therefore never fall. This is exactly how repetitive events, whether it’s housing moving higher year after year, or JGBs going higher in price every single year, become cognitive biases. Then, at some point in time, they actually become axiomatic. These axioms are rooted in belief systems that are developed through inductive reasoning based upon repetition of perceived fact patterns. When these patterns move to excess and can no longer be sustained, they reverse themselves in a violent manner.” – Kyle Bass (investor short subprime)