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Do Credit Markets Respond to Macroeconomic Shocks? The Case for Reverse Causality

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The response of corporate bond credit spreads to exogenous macro shocks is large. Giorgio Ottonello suggests this countercyclicality is driven largely by credit risk premia and translates into significant return predictability.

Image courtesy of interviewee. January 10, 2024

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This video video discusses a research study focused on the interaction between credit markets and macroeconomic activity, specifically how macroeconomic shocks influence credit markets. The study aims to fill a gap in existing literature, which has primarily concentrated on how credit markets predict macroeconomic conditions, but not the reverse relationship.

Key points include:

1. **Research Objective**: To understand if and how macroeconomic shocks, such as changes in GDP growth or other economic disruptions, impact credit markets, particularly corporate bond markets.

2. **Importance of Study**: Understanding this relationship helps clarify how economic shocks propagate through credit markets, affecting various sectors of the economy and significantly contributing to business cycle variations.

3. **Literature Context**: Previous theoretical frameworks established that frictions in credit markets can amplify macroeconomic shocks, and empirical studies have validated the predictive power of credit markets over macroeconomic activity. This video video study complements that work by investigating the influence of macroeconomic shocks on credit spreads.

4. **Methodology**: The study utilizes standard measures of macroeconomic activity (like GDP growth) and credit spreads (such as default spreads) and analyses the effects of various macroeconomic shocks using established econometric techniques like local linear projections.

5. **Findings**:
- Macroeconomic shocks are identified as significant drivers of credit spreads, on impact and through historical variations.
- A notable finding is the delayed response of credit spreads to macroeconomic shocks, attributed to information frictions in credit markets.
- The majority of the impact on credit spreads comes from variations in credit risk premiums rather than direct losses from defaults.

6. **Implications**: The findings open avenues for further research into the two-way relationships between credit markets and the macroeconomy, emphasizing the need for models that incorporate the interplay between the two.

7. **Conclusion**: The study concludes that macroeconomic shocks significantly influence credit markets, and understanding this relationship is crucial for developing better economic models and policies. The results point to the necessity of addressing both sides of the credit market and macroeconomic activity relationship for a more comprehensive understanding of business cycles.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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So the starting point, of our study is actually,

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let's say a longstanding goal

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and question in, in macro, in Macrofinance,

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in the macrofinance literature.

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And this question is, how to disentangle

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and how to characterize the relation between credit markets

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and macroeconomic activity.

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And, when I talk macro about microeconomic activity

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of, I refer, for example, to popular,

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measures such as GDP growth.

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And when I talk about credit markets, I refer to,

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markets where, companies, corporations get financing

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for, for their operations, such as,

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corporate bond markets.

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Uh, so this is a question that has received a lot

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of attention in the literature

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and the empirical work on this question is mostly focused

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on, one direction of the story.

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And I'm trying to be now,

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let's say go a bit more into detail into that.

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So, the empirical work so far has shown that,

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corporate bond market,

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based measures lead macroeconomic activity, in other words,

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predict, macroeconomic activity in, in the future.

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Uh, what is missing, from the literature so far is,

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analysis from an empirical standpoint on the reverse

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empirical relation, which is whether macroeconomic activity,

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proxy by, for example, macroeconomic shocks, leads,

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or impacts credit markets.

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And this is actually what our paper is going to focus about,

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which is understanding whether,

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macroeconomic shocks are a first order driver of,

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of credit markets.

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And we are going to put a lot of effort on identifying this,

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relationship, precisely.

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Uh, this topic is important,

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because of course, it allows us to understand how,

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shocks, in the aggregate economy transmits, through,

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through credit markets to different sectors of, of,

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of, of firms and in, in the economy.

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And of course, if we can shed light on the two-way

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interactions,

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and the highlight two ways between credit markets

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and macroeconomic activity, of course,

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we can also better understand what are the economic sources,

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for example, of business cycle variation.

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And, I think it's not controversial to say

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that a recent crisis episodes such

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as the financial crisis in 2007 and 2008,

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or, the more recent, covid shock in, 2020.

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Uh, so such episodes has shown how little yet,

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we understand, about how the business cycle moves

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and what are the, key forces, behind it.

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Uh, our paper aims

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to make a contribution into improving this understanding.

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And the focus is going to be exactly into shedding light.

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How, on how macroeconomic activity,

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or better macroeconomic shocks impact,

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credit markets in the first place.

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Uh, our paper mainly contributes to the literature

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that focuses on indeed the relation between,

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credit markets and macroeconomic activity.

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Uh, briefly, let's say this literature has,

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from a theoretical standpoint,

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really influential papers in the, let's say, early to late

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nineties, such as, for example, Bernanke

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and Gertler, KTA Moore, or Bernanke Gertler and Gilchrist.

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So this theoretical paper show,

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from a theoretical standpoint that,

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frictions in credit markets can be essential,

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or first order amplifier, of,

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macroeconomic shocks, meaning

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that if frictions in credit markets that prevent

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efficient allocation of capital across firms are large,

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this can, severely amplify, primitive shocks,

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fundamental shocks to the economy.

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And these papers, have been followed up by, a long

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and productive empirical literature

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that actually takes the intuition from these theoretical

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models into, into the empirical data

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and shows, convincingly

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and with different, let's say, methodologies that

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credit markets forecast microeconomic

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activity unambiguously.

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And, examples of these papers

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and, this is, I think, useful also

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to understand which type of indicators have been developed

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are, for example, Greenwood and Hanson in 2000

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and thirteens, which show that, the share

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of high yield bonds relative

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to the total corporate bonds outstanding

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is an extremely good predictor of,

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of macroeconomic activity.

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Uh, this measure has also been, used,

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to build a credit market sentiment indicator in another

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paper by, Lopez and Ali

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and co-authors, for example, as well,

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sch hand Taylor in 2012 showed that the balance sheet growth

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of financial intermediaries, which

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of course play an important role in credit markets,

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is also an important measure, when we want

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to forecast macroeconomic activities.

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More recent examples are, an average,

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in spread across all corporate bonds in the markets,

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developed by Gil Gilchrist and Za in 2012.

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This paper has been, extremely influential and,

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and has spurred even more work on that dimension.

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And last, but not least, in 2021, Rafa Cho

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and Goldstein showed that, the flows of, capital

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from investment grade to high yield corporate bond funds

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is an important predictor of,

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macroeconomic activities, which is, again, based on,

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credit markets information.

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So this overview shows that the direction of causality from,

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credit markets

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to macroeconomic activities has been well established

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and widely, widely studied.

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We contribute to this literature by showing the opposite,

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direction and focusing on identifying the strength

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of the impact of macroeconomic shocks on credit markets.

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And, this is of course, complimentary to the studies

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that have been, looking at the other directions.

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And, we will show them, with the results,

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more or at least as important

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as the direction from credit markets to,

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macroeconomic activity.

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The data that we're using in our paper are actually,

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quite standard.

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So, most of them are, actually quite,

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publicly available, so a anyone could,

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could easily retrieve them.

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Uh, in terms of, measures

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of macroeconomic activity in our paper, we focus on,

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real GDP growth and, the CF n ai index.

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Uh, credit spreads are captured by two measures.

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Uh, one is the default spread, which is the difference

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between VAA and the a, a a, rated corporate bonds.

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This measure spends a longer period from 1919 up

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to 2017.

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The second measure, which is a bit more precise,

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but spends a, a shorter sample period starting in 1973,

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is the, GC spread based on the georis theologic paper

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that I mentioned earlier, which is essentially

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a evaluated average

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of ill spreads across all corporate bonds,

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in the market at each point in time.

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And, these are the two measures.

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And, of course, we need, measures

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of macroeconomic shocks to better identify the impact

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of macroeconomic shocks on, on credit markets.

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Uh, so in this case,

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we leverage on the literature macroeconomics,

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which has done a lot of work in properly identifying,

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macroeconomic shocks of, diverse origin.

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And we use three of those.

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First, we use oil supply shock, which were developed by,

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be meister in Edmonton in, 2019.

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And, you can think of those shocks,

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as a large oil supply disruption, for example, due

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to conflicts in the Middle East,

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or, unexpected decision

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of the OPEC organization when it comes to setting the,

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supply of oil, worldwide.

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Then we also use defense spending news shocks,

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which comes from, from Rami, a paper by

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by Rami in 2011.

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And you can think of those as, unexpected,

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military buildups, from, from the government.

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And obviously those are shocks

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that are particularly strong in the early part of the

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20th century, for example, around, the Second World War.

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Uh, last but not least, we use, technology shocks,

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from benza and k which, cover a period from,

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1952 in this case up to up to 2017.

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And as you can see from, from the description

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of these shocks, they are all quite diverse in,

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in origin, motivation, and even sample periods.

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And we believe that this is actually, let's say

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an advantage of our study, meaning

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that finding a consistent impact

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of such diverse macroeconomic shocks on credit markets

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is not obvious, and it sets quite a high bar in terms

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of identification and in terms of, the robustness

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and the strength of, of our finding in, in our study.

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Um, so in terms of other databases that we use

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before getting into, into the methodology,

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when we analyze the economic mechanisms, be, be

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behind our main results, we need to rely on,

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bond level data on, bond returns

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and characteristics, which we collect

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by merging the Lehman Brothers database

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and the trace database to create a time series

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that goes from, 1973 to 2017.

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And, we also need to use, measures of,

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expectations of credit spreads and macroeconomic activity,

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and we rely for that on the professional survey forecast

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from, the survey

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of professional forecasters and, and blue chip.

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So this is a general overview of the databases

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that we use in our paper in terms of empirical methodology,

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the first, thing we do is

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to understand whether macroeconomic shocks have an impact at

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all on, on credit spreads.

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And to do that, we rely on local linear projections based on

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the paper by, Georgia in, in 2005.

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And, in a nutshell,

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this approach is essentially regressing credit spreads on

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macro shocks, and of course, a wide set of, of controls

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and doing so at different horizons.

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So we are going to have one regression per, per horizon.

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And then analyzing the impact,

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the coefficient on the macros shocks at the different

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horizon to gorge the, the dynamic effect of,

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over time of, macros shocks on, on credit spreads.

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And this, type of exercise allows us to understand,

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whether when a macroeconomic shock hits,

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there is a large movement in credit spreads in response

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of, of this shock.

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Uh, the second, let's say methodology that we,

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that we use, serves to answer, a related,

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but different questions, meaning are the macro shocks

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that we use, able to explain a large fraction

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of the historical variation in credit spreads over time.

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In other words, are there enough of those shocks,

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to be economically relevant, o over time?

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It could be, for example,

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that there is an extremely large shock

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that impacts credit spreads at one point in time,

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but this is once in a lifetime shock.

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So there are no other shocks of that kind.

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So if you collect the total variation of credit spreads,

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in relation to macro shocks,

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it might turn out to be pretty small.

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So, to answer this question, we need

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to use a forecast error variance decomposition, tailored

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to our local linear projections that I described

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above, be before.

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And, to do that we apply methodology developed

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by Chenko and Lee in their 2020 paper,

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Which, exactly,

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Creates a forecaster variance composition tailored

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to local linear projections.

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And,

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Uh, a, a last, let's say, comment on,

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on our empirical methodologies that the type of shocks

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that we study, are large, but in, rather

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Infrequent. And

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Therefore, of course, the length of

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The sample period is essential,

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to get enough statistical power.

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So we do have quite a long sample, but

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Given the strength Of our results

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and the, the important implications of, of this type

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of shocks, we are also working in follow-up projects

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to expand even further the sample period

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and go back, to the 19th century, to see if we,

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if we can learn anything new, in terms of

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how macroeconomic shocks, o over over the history

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as, as, as impacted the, the economy

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and whether there is something that we can learn also for,

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let's say, policies. Uh, nowadays.

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To start with, the main finding of our paper is,

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Uh, that macro Shocks are

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A first order driver of, of credit spreads.

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And

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We, we can show this both when the shock hits.

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So there is a large impact on credit spreads when the

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shock hits, but also, as I mentioned earlier,

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Equally important macros shocks can explain a large

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Variation of credit spreads historical.

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So even in the forecast error variance decomposition,

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we can show that credit spreads indeed are,

13:19.515 --> 13:21.655
are a major driver of historical variation.

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Uh, macros shocks are a major driver

13:23.475 --> 13:25.855
of historical variation in, in credit spreads.

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Uh, to give an idea of,

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Why am I saying large, what are the magnitudes?

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Uh, for example, a one standard

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Deviation negative macro Shocks on impact,

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Increases credit spreads by about 40 basis points, which

13:42.555 --> 13:43.615
Is relatively large.

13:43.995 --> 13:46.335
Uh, if you think about the fact that,

13:46.335 --> 13:48.615
40 basis points is around

13:48.615 --> 13:49.615
42%

13:49.615 --> 13:52.055
of the unconditional standard deviation in credit spreads

13:52.115 --> 13:53.215
in, in our sample,

13:53.765 --> 13:55.855
When we move to, the forecast

13:55.855 --> 13:57.055
Error variance decomposition,

13:57.595 --> 14:00.655
We see, Always from a magnitude standpoint that,

14:01.155 --> 14:04.415
Uh, macroeconomic shocks can explain between 40 to

14:04.415 --> 14:08.415
50% of, the historical variation in credit spreads.

14:08.515 --> 14:09.855
And I'm referring to

14:10.115 --> 14:13.295
The aggregate Impact of all of our shocks together to,

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to reach that, that number.

14:15.075 --> 14:17.615
And this is obviously a large number,

14:17.615 --> 14:19.495
it's definitely indicating that

14:19.585 --> 14:21.495
Macro shocks are a first order driver

14:21.595 --> 14:22.695
of credit spreads. And

14:22.695 --> 14:27.575
Importantly, if we look at how much, variation in

14:27.575 --> 14:30.375
Macroeconomic activity, macro shocks explain,

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for example, how much variation in GDP growth is explained

14:33.655 --> 14:34.775
by macro shocks, we

14:34.775 --> 14:36.655
Get about The same, the same quantity.

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So macroeconomic

14:38.395 --> 14:41.695
Shocks are as important in explaining

14:41.695 --> 14:43.055
The variation in credit spreads

14:43.075 --> 14:45.455
as they are in explaining the variation in macroeconomic

14:45.775 --> 14:48.335
activity, which are the thing they first impact

14:48.355 --> 14:49.455
in, in, in the first place.

14:49.755 --> 14:51.645
So this is actually, we believe

14:51.645 --> 14:53.605
that this result really speaks to the fact that

14:53.885 --> 14:55.445
whenever, you want

14:55.445 --> 14:59.095
to study the variation in credit spreads, you need to turn

14:59.095 --> 15:01.935
to Mhh shocks as a first order order effect.

15:02.395 --> 15:03.455
Uh, for sure.

15:03.725 --> 15:05.095
This is, one of the main,

15:05.155 --> 15:06.935
the main conclusions of our study.

15:07.675 --> 15:11.375
Uh, in the second part of our paper, we try

15:11.375 --> 15:12.615
to disentangle,

15:12.755 --> 15:14.895
and better understand the economic mechanism

15:14.895 --> 15:16.015
behind our findings.

15:16.075 --> 15:18.135
And we do so by answering two questions.

15:18.705 --> 15:23.535
First, which component of credit spreads, is,

15:23.765 --> 15:26.455
driving the reaction to micro shocks?

15:26.875 --> 15:31.635
And second, why do we observe a delayed response

15:31.635 --> 15:33.715
of credit spreads to micros Shocks, I,

15:33.835 --> 15:35.315
I haven't specified it earlier,

15:35.415 --> 15:38.035
but the impacts that,

15:38.065 --> 15:40.955
I've shown the 40 basis point impact is large,

15:40.975 --> 15:44.035
but comes with a slight delay relative to the impact

15:44.035 --> 15:46.035
of macros, shocks on macroeconomic activity.

15:46.175 --> 15:49.715
So we want to understand why, why this is the case,

15:49.735 --> 15:52.595
and this probably can, can also help

15:52.735 --> 15:55.875
to shed light on the economic mechanism behind,

15:56.135 --> 15:57.235
behind such effects.

15:57.695 --> 16:00.435
So, coming to the first question, which component

16:00.435 --> 16:02.675
of credit spreads impact the,

16:03.055 --> 16:04.835
is most impacted by micro shocks?

16:05.495 --> 16:09.275
Uh, we decompose, in this case, credit spreads into

16:10.415 --> 16:13.825
cash flows, meaning how much losses accrued

16:13.825 --> 16:16.505
to investors when, a company defaults,

16:17.005 --> 16:21.205
and the discount rates, meaning, what is the variation

16:21.205 --> 16:24.845
of the risk premium that the investors require

16:24.945 --> 16:28.085
to hold the same amount of risk at, at, at each,

16:28.085 --> 16:29.245
at a certain point in time.

16:29.425 --> 16:33.585
And, to decompose this empirically,

16:33.605 --> 16:35.905
we apply a decomposition of credit spreads,

16:35.905 --> 16:38.585
which is based on a methodology developed by,

16:38.585 --> 16:41.065
no zawa in in his 2017 paper.

16:41.685 --> 16:44.705
And the, once we do that, we essentially

16:45.275 --> 16:47.905
study the impact of micros shocks on each

16:47.905 --> 16:49.025
of the components separately.

16:49.085 --> 16:51.705
So this is, again, a new variable, for,

16:51.705 --> 16:54.345
for our local linear projection that is based only on one

16:54.345 --> 16:55.745
of the component of credit spread.

16:56.205 --> 16:59.905
And the answer is, again, pretty clear.

17:00.445 --> 17:03.425
The large part of, the impact

17:03.425 --> 17:05.745
of macroeconomic shocks in credit spread is driven.

17:06.365 --> 17:08.585
And we're talking about 80%

17:08.605 --> 17:12.145
or more of the vari of, of the impact is driven by,

17:12.145 --> 17:13.505
variation in credit risk premium,

17:13.505 --> 17:16.265
meaning credit spreads move, not because,

17:17.005 --> 17:20.345
or, not mainly because when, a bond

17:20.605 --> 17:21.905
or a company defaults.

17:21.905 --> 17:24.465
There are larger losses to investors, but mostly

17:24.465 --> 17:28.265
because investors when a negative shock,

17:28.265 --> 17:31.465
macroeconomic shock hits require a higher compensation

17:31.485 --> 17:32.625
for the same amount of risk.

17:32.845 --> 17:34.825
So their, compensation

17:34.935 --> 17:36.465
that they require for risk increases.

17:36.465 --> 17:41.385
So time variation in credit risk premium is the main,

17:41.885 --> 17:44.185
driver of the reaction of credit spreads

17:44.185 --> 17:45.465
to macroeconomic shocks.

17:45.485 --> 17:47.985
So this is an important, result also

17:47.985 --> 17:50.565
because it can speak to,

17:51.095 --> 17:54.885
let's say theoretical models that try to understand how,

17:54.885 --> 17:57.325
the variation in credit spreads,

17:57.745 --> 17:59.285
can be explained over time.

17:59.425 --> 18:03.325
So turning to time varying credit risk premia is,

18:03.605 --> 18:05.725
a really good direction given the,

18:05.785 --> 18:07.725
and promising direction given the empirical

18:07.725 --> 18:09.005
findings that, that we have.

18:09.745 --> 18:12.805
Um, the second question is that, is targeting

18:12.805 --> 18:15.565
to understand why the response of credit spreads is delayed.

18:16.185 --> 18:19.925
And, to answer this question, we,

18:20.865 --> 18:24.005
we try to provide evidence of the presence

18:24.005 --> 18:27.045
of information frictions in, in credit markets

18:27.075 --> 18:28.925
that could explain this delayed response.

18:29.065 --> 18:32.385
And, in simple words,

18:32.495 --> 18:35.105
information frictions just reflect the, the idea

18:35.215 --> 18:37.665
that there is not a full response

18:37.845 --> 18:39.545
of credit spreads when a shock hits,

18:39.695 --> 18:41.385
because the exact information,

18:41.385 --> 18:44.465
content is not immediately available, to all investors,

18:44.885 --> 18:46.025
at that point in time.

18:46.085 --> 18:48.185
So there is, there are frictions

18:48.185 --> 18:51.545
that prevent investors from accessing, the,

18:51.645 --> 18:54.065
the complete set of information at that point in time.

18:54.205 --> 18:56.785
It might require, a bit longer,

18:56.845 --> 18:58.825
and that's why, the response is delayed.

18:59.285 --> 19:00.785
Uh, to test for this empirically,

19:00.805 --> 19:03.145
we follow a methodology developed by Kion

19:03.225 --> 19:05.345
and Chenko in their 2012 paper.

19:05.525 --> 19:07.665
And essentially, the idea is pretty simple.

19:08.285 --> 19:12.185
Uh, you need to turn to measures of expectations of,

19:12.365 --> 19:16.385
of credit spreads, meaning, what investors expect about,

19:16.645 --> 19:18.385
credit spreads in the future,

19:19.005 --> 19:22.785
and check whether those measures respond indeed,

19:22.785 --> 19:24.665
with a delay to micro shocks relative

19:24.665 --> 19:26.145
to the realized credit spreads.

19:26.525 --> 19:30.065
If this is the case, the, the implication

19:30.085 --> 19:33.305
that's forecast error, meaning the error in the forecast

19:33.305 --> 19:36.505
between the realization relative to the realization,

19:36.565 --> 19:38.985
if this is the case forecast errors will be predictable, and

19:38.985 --> 19:41.185
therefore this will be a sign of the presence

19:41.245 --> 19:43.745
of information frictions in, in

19:43.745 --> 19:46.025
that specific, dimension.

19:46.485 --> 19:50.375
And, once we turn to the data, we do indeed find

19:50.405 --> 19:53.415
that, the impact of,

19:53.705 --> 19:56.335
macro shocks on expectations of forecast

19:56.335 --> 19:59.375
of credit spreads is indeed delayed relative

19:59.455 --> 20:02.055
to the realization, which is consistent with the idea

20:02.055 --> 20:04.175
that there, the, there is a scope,

20:04.265 --> 20:05.615
there is an important role

20:05.615 --> 20:07.335
of information frictions in credit markets,

20:07.385 --> 20:10.855
which can explain the delayed response of credit spreads.

20:11.315 --> 20:15.015
Uh, so this is essentially, wrapping up the,

20:15.015 --> 20:19.415
the part on, on why credit spreads are delayed.

20:20.015 --> 20:23.455
I want to mention, really briefly, other two tests

20:23.605 --> 20:27.095
that we do to better, provide, an understanding

20:27.155 --> 20:29.175
of the economic mechanisms and,

20:29.195 --> 20:31.135
and the robustness of, of our findings.

20:31.305 --> 20:33.975
First, we are able to show that, the response

20:33.975 --> 20:37.335
of credit spreads to macro shocks is much stronger when we

20:37.335 --> 20:40.535
focus on credit spreads of firms with high leverage, meaning

20:40.535 --> 20:44.255
that if you, if you, restrict the sample to firms,

20:44.285 --> 20:46.935
with high leverage, you see that the response is,

20:47.365 --> 20:50.605
a double, of credit spread to micros shocks relative to,

20:50.605 --> 20:52.165
for example, firms with low leverage.

20:52.355 --> 20:54.045
This is important because it indicates

20:54.045 --> 20:56.085
that firm leverage is an important

20:56.835 --> 21:00.165
amplifi amplification mechanism when it comes

21:00.165 --> 21:02.565
to understanding the impact of micros shocks on,

21:02.745 --> 21:04.765
on the economy through credit markets.

21:05.425 --> 21:09.005
And, the last exercise I wanna mention is,

21:09.285 --> 21:11.205
a sort of external validity exercise.

21:11.305 --> 21:12.925
We, we take our macro shocks

21:12.925 --> 21:16.965
and we, underst we try to figure out what is the impact

21:16.965 --> 21:20.085
of these shocks on popular proxies of equity risk premium.

21:20.185 --> 21:21.565
So we turn to the equity market

21:22.025 --> 21:25.515
and check whether the impact

21:25.515 --> 21:28.955
that we find on credit markets is actually reflected also in

21:28.955 --> 21:32.515
other, financial assets, which if these are truly,

21:32.565 --> 21:33.715
let's say exogenous

21:33.855 --> 21:36.395
and primitive macros shocks, it should be the case.

21:36.775 --> 21:39.755
And we did find that the impact of, of these macros,

21:39.755 --> 21:43.955
shocks on popular, equity risk pri proxies is,

21:43.955 --> 21:46.795
really similar to what we observe both in direction

21:46.795 --> 21:49.115
and magnitude in, in, in credit markets.

21:49.375 --> 21:50.915
So this is important, first of all,

21:50.915 --> 21:53.395
because it, let's say, reinforces the idea

21:53.425 --> 21:55.955
that we are looking at, a proper set

21:55.955 --> 21:59.915
of microeconomic shocks, but also, importantly

21:59.915 --> 22:04.035
because it shows a common source of variation of,

22:04.275 --> 22:07.475
discount rates in, credit markets and equity markets.

22:08.055 --> 22:09.115
And this is important

22:09.115 --> 22:12.875
because empirically, the correlation between equity risk,

22:12.875 --> 22:16.035
premier proxies and credit risk premier proxies,

22:16.035 --> 22:18.995
have been quite low, empirically,

22:19.345 --> 22:22.115
despite theoretically that should be, pretty high.

22:22.575 --> 22:25.195
Our paper shows that this is an identification problem.

22:25.305 --> 22:29.875
Once you can identify, a well, let's say shocks

22:29.875 --> 22:33.155
that affect, both markets at the same point in time

22:33.215 --> 22:36.235
and hold constant other confounding factors,

22:36.235 --> 22:38.155
this can actually resolve this puzzle

22:38.265 --> 22:40.795
that has been shown empirically when you look at the

22:40.795 --> 22:43.475
unconditional correlation between credit markets

22:43.575 --> 22:44.595
and, and equity markets.

22:45.055 --> 22:47.195
And we believe actually that this is,

22:47.615 --> 22:50.195
an interesting avenue also for, for future work.

22:50.415 --> 22:53.835
The findings that, that we have in our paper, can be,

22:54.255 --> 22:56.835
used,

22:57.095 --> 22:59.555
and extended in, in future research.

22:59.575 --> 23:02.835
And I'm going to give you just here, a few examples.

23:02.895 --> 23:07.355
So, the first part that, we believe is, is,

23:07.415 --> 23:09.675
is actually, useful for, for future,

23:09.815 --> 23:12.315
future research is the finding that the response

23:12.315 --> 23:14.195
of credit spreads to,

23:14.295 --> 23:16.355
to macroeconomic shocks is a combination

23:16.575 --> 23:20.435
of information rigidities, which explains, the, the low,

23:20.455 --> 23:23.915
the slow response and leverage related amplifications firms

23:23.915 --> 23:26.115
with higher leverage will, respond more to,

23:26.115 --> 23:27.355
to macroeconomic shocks.

23:27.975 --> 23:32.345
Uh, there are, to our knowledge,

23:32.605 --> 23:35.145
no macroeconomic models that include both

23:35.685 --> 23:39.705
of these components, when it comes to explaining the,

23:40.125 --> 23:43.025
the, the pricing of, of credit, of,

23:43.125 --> 23:44.985
in credit markets, and also the evolution

23:45.085 --> 23:46.445
of credit risk premium over time.

23:46.545 --> 23:49.365
So we believe that, developing models

23:49.365 --> 23:52.445
that incorporate these two features can go a long way into

23:52.455 --> 23:55.645
explaining the empirical facts, that we document,

23:56.265 --> 23:57.325
o overall.

23:58.065 --> 24:02.685
And of course, more broadly the fact that,

24:02.685 --> 24:04.885
we find an important,

24:04.885 --> 24:07.085
let's say a large impact on macroeconomic shocks on credit

24:07.085 --> 24:08.845
markets speaks to the fact

24:08.845 --> 24:11.125
that there are probably large feedbacks

24:11.125 --> 24:13.645
between credit markets and macroeconomic activity.

24:13.705 --> 24:15.885
As I said, the direction between credit markets

24:16.345 --> 24:18.165
and the macro economy has been well documented.

24:18.185 --> 24:22.725
We now show that also the reverse causality, direction,

24:22.865 --> 24:24.205
works pretty strongly.

24:24.425 --> 24:27.565
So models that incorporate the two-way interaction

24:27.565 --> 24:30.125
between the macro economy and the credit markets,

24:30.825 --> 24:34.805
and use this to understand, the contribution

24:34.805 --> 24:36.365
to business cycles, we believe

24:36.365 --> 24:38.165
that this remains a really important

24:38.195 --> 24:39.445
area for future research.

24:39.905 --> 24:43.565
And last, but not least, the fact that these shocks

24:43.565 --> 24:44.965
that we document,

24:45.155 --> 24:47.645
have a large impact on other markets too in equity

24:47.645 --> 24:48.805
markets that we have shown.

24:49.285 --> 24:52.365
I think it, it can open interesting avenues, to,

24:52.785 --> 24:55.085
to empirically measure common source of,

24:55.345 --> 24:57.325
of risk across different markets.

24:57.665 --> 25:00.885
And I think there is a, there is a lot of work

25:00.885 --> 25:03.645
that can be done, empirically in, into that direction.

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