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Speaking of the Economy
credit markets in turmoil
Speaking of the Economy

Nov. 9, 2022

Measuring Credit Market Sentiment

Topic: Financial Markets
Audiences: Bankers, General Public

Horacio Sapriza discusses a new measure of conditions in credit markets and how shocks in those markets affect the broader economy. Sapriza is a senior economist and policy advisor at the Federal Reserve Bank of Richmond.

Speaker


Transcript


Tim Sablik: Hello, I'm Tim Sablik, a senior economics writer at the Richmond Fed. My guest today is Horacio Sapriza. Horacio is a senior economist and policy advisor at the Richmond Fed. Previously, he was the principal economist in the Macro-Financial Analysis Section at the Fed's Board of Governors.

Horacio, welcome to the show.

Horacio Sapriza: Hi, Tim. Thanks for having me. It's a pleasure to be here.

Sablik: Our topic of conversation today is the credit market sentiment index that you've been developing with several co-authors. We'll put a link up in the transcript to an August Economic Brief that you wrote on this work. Today, I want to break down with you what the index is, what it's trying to measure, and why it's important.

My sense is, and you can correct me if I'm wrong, it's that there's been a growing interest since the Great Recession of 2007-2009 to study how turmoil in the financial markets can spill over and affect the broader economy. This year's Nobel Prize winners in economics — Douglas Diamond, Philip Dybvig and former Fed Chair Ben Bernanke — were awarded the prize in part for their research on how troubles in banking and financial sector can precipitate an economic crisis.

What role does credit play in the transmission of financial market shocks?

Sapriza: As highlighted by the very severe disruptions in credit markets at the center of the global financial crisis, understanding the sources and the transmissions of financial distress in the economy is key for macro[economic] stabilization policy. Both policymakers and academics have pointed to the excess in credit markets, including abnormally low risk premiums, as key drivers of the incredibly large buildup of risk in the U.S. financial system in the years leading to the crisis. Since then, there have been many questions regarding the role of credit factors in business cycle fluctuations.

What we do in our work is to address two related questions that are particularly important for policymakers. First, do frothy conditions in credit markets create risks to future macro performance? If that is the case, the second question is how can we measure such froth? By froth, we mean conditions in which the expected returns to bear risk are driven below what is justified by fundamentals because credit is being priced too aggressively.

Sablik: That suggests if we could measure credit market tightness, or froth as you were talking about, we might be able to get an early warning of some economic troubles. How have economists typically tried to measure that credit market froth or stress in the past?

Sapriza: So much of the empirical research that's been trying to measure credit market conditions and future outcomes has focused on the growth in broad aggregates or in balance sheet measures of leverage. This branch of the literature documented that rapid and prolonged increases in credit outstanding presage economic downturns and sharp declines in the stock market.

Another strain of the literature emphasizes time variation in sentiment – sentiment on the part of investors in credit markets — and highlights that as an important determinant of the credit cycle and business cycle dynamics are associated to it. This second approach starts from the narratives of work by [Marvin] Minsky in 1977 and [Charles] Kindleberger in 1978 on manias, panics and crashes, which argue that periods of frothy market conditions are predictably followed by a widening of credit spreads, which reliably tend to precede an economic downturn.

Sablik: Mm-hmm.

Sapriza: An important behavioral perspective that links the price of credit risk and, therefore, sentiment to economic outcomes is related to the way investors in credit markets tend to update their beliefs given the incoming data they receive.

In particular, investors may overreact to recent news and, therefore, overweight future outcomes that now become a little more likely. For instance, investors may become complacent about default risk after a few years of economic expansion and that may lead to a compression in credit spreads, looser credit standards and increase the issuance of credit to risky borrowers. In this environment, when you receive unfavorable economic news — unfavorable by the optimistic standards at the time — this can lead investors to revise disproportionately their assessment of the default risk. That may lead to a pullback in credit supply, which triggers an economic downturn.

This reasoning implies that investor psychology can itself be a cause of the volatility that may occur even absent significant changes in economic fundamentals. So that's the building block of our work.

Sablik: Sentiment is a bit of a nebulous topic. I'm wondering if you can talk a bit more about what you specifically mean by sentiment, and then how you go about trying to measure that.

Sapriza: Sure, I'd say quick credit growth and loose lending standards, increasing leverage, and narrow risk spreads may suggest accommodative or even frothy credit market conditions. They may also reflect, at least in part, strong borrower fundamentals and robust economic growth. The fact that credit market conditions are influenced by economic fundamentals implies that we need a methodology that takes into account the effect of these current economic fundamentals when measuring conditions in credit markets.

With this in mind, we developed a model that estimates a factor summarizing sentiment in U.S. credit markets, and it does so in real time. The factor summarizes credit market conditions beyond what can be attributed to the direct effects of economic activity on credit markets at that time. We want our signal to capture only information from credit markets that is not attributed to current fundamentals.

Sablik: Okay, that makes sense. You're capturing the movements in the credit markets that are being driven by these other factors that are not driven by fundamentals as you say.

Sapriza: Exactly. We purge the signal [of] the effects of economic conditions, which is something that most indicators currently do not do. To the extent that the relationship between credit markets and economic conditions may vary across, let's say, good and recessionary times. The methodology that we use takes into account or tries to accommodate these nonlinear dynamics.

Sablik: What did you find when you applied past data to your model?

Sapriza: The credit market sentiment indicator clearly identifies the extended period of credit market overheating that led to the '08-'09 global financial crisis and associated recession. It also picks up the quick deterioration in credit market sentiment in the summer of 2015. This is when there were concerns about global growth prospects. This was centered around China. These concerns sparked a broad repricing of risky assets, and that led to a sharp increase in financial market volatility. In fact, based on our estimates, I would say the gloom that engulfed credit markets in that period was quite persistent and only started to lift around 2019.

Credit sentiment worsened again in 2020 following the pandemic and deteriorated further in 2022, amid growing concerns of geopolitical tensions from the Russia-Ukraine crisis. That takes us to the current time.

Sablik: Are you hoping that your index might be able to serve as an indicator or a warning of potential future downturns driven by credit market shocks?

Sapriza: I would say that the results that we have indicate that fluctuations in credit market sentiment have economically and statistically important effects on real economic outcomes. The effects of credit market sentiment shocks on economic activity are asymmetric. They are different in normal versus stressful times.

These nonlinear dynamics reflect the slow build-up of financial imbalances during economic expansion. Once the economy is in a vulnerable state, a negative sentiment shock can easily tip the economy into an adverse state. In that context, interaction between the drop in asset prices, high leverage, and weak balance sheets can have a strong effect on growth for a long period.

In our framework, this distinction between states is informed by the interaction of economic activity itself and credit sentiment factors. This idea is consistent with empirical evidence in the literature that recessions accompanied by disruptions in credit markets tend to be much longer and deeper than any other type of recession.

Sablik: Oh, really?

Sapriza: One of the outputs of the model is precisely an estimate of the probability that the U.S. economy is going to experience an adverse economic state. These probabilities accord well with recessions, which suggests that the model can distinguish between expansionary and contractionary periods of economic activity.

Now, this sensitivity varies notably over the course of the business cycle and generally peaks in the late stages of an economic expansion. This pattern that I'm describing is consistent with a gradual build-up of financial excesses during economic booms that I was mentioning earlier. By contrast, the same size shock during a recession has very little effect on these probabilities because the economy is already in an adverse state.

Sablik: Very interesting stuff.

As I mentioned at the start, you have this Economic Brief that listeners can find on our website on this topic. What are you hoping to do next with this research?

Sapriza: We're hoping to upload the credit sentiment index onto the Richmond [Fed] website really soon. A natural next step, given that the credit sentiment Index can significantly affect both credit and business cycle dynamics, is to study the relationship between changes in this index and monetary and fiscal policies. This type of analysis can improve, hopefully, our understanding of the transmission mechanisms of these policies and, therefore, their optimal implementation.

Sablik: Sounds great. Horacio, thank you very much for coming on the show to talk about this research.

Sapriza: Thank you, Tim.

Sablik: As always, I'll remind listeners who want to keep up with this work and other research being done at the Richmond Fed, they can head over to Richmonded.org. And if you enjoyed the show, please consider leaving us a rating and review.

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