Thoughts on the Market

Heather Berger of the U.S. Economics Team hosts Wealth Management Senior Economist and Strategist Sarah Wolfe to discuss what it takes to define the middle class in America today. They break down how factors like rising essential costs and the development of AI are reshaping consumer balance sheets and financial security.
Sarah Wolfe is a member of Morgan Stanley's Wealth Management Division and is not a member of Morgan Stanley’s Research Department. Unless otherwise indicated, her views are her own and may differ from the views of the Morgan Stanley Research Department and from the views of others within Morgan Stanley.
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Heather Berger: Welcome to Thoughts on the Market. I'm Heather Berger from Morgan Stanley's U.S. Economics Team.
Sarah Wolfe: And I'm Sarah Wolfe, Senior Economist and Strategist on Morgan Stanley's Thematic and Macro Investing team in the Global Investment Office.
Heather Berger: Today, the K-shaped economy, the middle class, and how AI could reshape both.
It's Friday, September 11th, at 10a.m. in New York.
The K-shaped economy has been a major theme this year. At its core, it describes an economy where households are experiencing very different circumstances. Those with more assets have benefited from rising wealth, while those with less wealth remain more dependent on income and more exposed to increases in essential costs. But that top versus bottom framing can miss an important part of the story: the middle class. Sarah, you recently wrote about what it takes to make it to the middle class in America. How would you define the middle class today, and how does that differ from the way that households define it themselves?
Sarah Wolfe: I think the important thing here is that economists and households define the middle class very differently from each other, and, and I'll get into why that's the case.
So if you're an economist, the middle class is roughly defined as two-thirds to twice the median household income, which today means if you're making around fifty-five thousand dollars a year to a hundred and sixty-eight thousand dollars a year, depending on where you live in the country, that is roughly the middle class. And that's where about half of Americans sit today.
We've actually seen that number decline, so sixty-one percent of Americans in the 1970s were in that middle class definition by economist terms. Now it's about fifty percent, so we have seen it shrunk. But even though it's shrunk, fewer and fewer households feel like they're in the middle class, and they don't define it by necessarily income or a specific number, but they really define it by milestones, I would say.
So do you own a home? Have you been able to build a family, and can you pay for childcare? Have you saved enough for retirement? Do you have an emergency fund? Are you constantly stressed about your bills? That feeling is really what the middle class is about today, and I would say that less than fifty percent of Americans actually feel like they're in the middle class once you start to put that definition around it.
Heather Berger: What are the key factors that actually make a household feel financially secure?
Sarah Wolfe: I think there's four things that determine household security and stability. The first, of course, is income, stable income. Do you have a job, and do you think you're going to continue to have a job six months from now? We love the University of Michigan Consumer Sentiment survey that asks consumers this.
Do you have affordable fixed costs, like housing, childcare, healthcare, and transportation? Do you own assets? This is critically important because if we look at where gains have come from from the last five years, it hasn't really been that much through the labor income channel. It's been through the asset channel, like home equity, retirement savings, are you invested in the stock market, et cetera.
And then the last one is this emergency fund and a manageable debt. What is your debt load? Is it fixed rate, or is it revolving? The more of these pillars that a household has, the more financially fulfilled and comfortable they are, and the more likely they are to feel like they've made it to the middle class, but the reality is, is that fewer and fewer households are meeting these four boxes that define the middle class by historical terms.
Heather Berger: And what has made that security harder to achieve? Which of those costs that you mentioned have moved the furthest out of reach?
Sarah Wolfe: I think these numbers are going to maybe surprise our listeners, but in some ways feel very real to them as well. So if we look at how much inflation has risen since the 1970s, shelter, the cost of housing, has risen 6.6 times more than the overall inflation basket. Childcare costs have risen by 14 times more than the overall inflation basket, and healthcare costs have risen 10 times more.
And if we dig more into childcare, we now like to call it the second mortgage. And we're not being sarcastic or anything. The reality is that to send two children to childcare in America costs more than a mortgage in 45 states, and costs more than rent in 49 states.
So it's really, this reality has gotten a lot more expensive, and these baskets, these individual things like childcare, healthcare, shelter, that define the middle class, have risen more than the overall inflation basket, and certainly have risen more than income growth over this period as well.
Heather Berger: Right. So the overall inflation measure can kind of understate the increases in some of these essential costs. And when people talk about a K-shaped economy, the middle class itself isn't necessarily moving as one group. You mentioned homeownership a lot. How much do homeownership, age, and geography determine who is moving up and who is getting squeezed?
Sarah Wolfe: Homeownership is always incredibly important, right? Because it's this large asset that is more equally distributed across the income distribution, as opposed to if we think about equities, and you've done a lot of great work on this. That is the most highly concentrated asset across the income distribution, right? Where the top 20% is sitting on 70%, at least, of equities. So homeownership remains the best channel towards wealth accumulation. Obviously, though, timing of homeownership matters a lot. If we were all so lucky to have bought a home in 2019 and 2020, we got a low fixed-rate mortgage, and we would've benefited from the tremendous run-up in home prices over the last five years, right, over 50% home price appreciation over this entire period. So that's been really important. Also, geography, where you bought a home, did that benefit from the COVID home price appreciation? And then the geography also matters because someone living in New York versus someone living in the Midwest is living with really different fixed costs, realities of fixed costs, and that's also gonna help define do they feel financially secure, and do they feel like they're in the middle class?
The other component I don't wanna leave out, though, equities is really important. And we did some work looking at the Fed's distributional financial accounts, and if you look seven years ago, Gen X was doing way better than Gen Y or the millennials were at that same age 15 years ago. But then, because the millennials were sitting on so much equity wealth because they've built up their 401Ks, they really couldn't get as successfully into homeownership, so they had more stored away in equities. They have now surpassed Gen X at this age, two and a half times. It is a tremendous reversal in wealth and in who's doing well, and it's because of what's happened in the stock market. And it's not because they were better savers. It was just a lot of timing and luck. So I would say that our fate is not prewritten, as we also think about Gen Z entering the workforce and becoming wealth builders.
I want to dig in, though, to a really important part of the K-shaped economy, though, and that's AI. We can't talk about anything without talking about AI, for better or for worse. And that the common view is that white collar, high-income workers are the most exposed to displacement, and we're seeing that in some of the job numbers recently, right, where tech and financial services are shedding jobs. But your work, I think, is really unique, and it's the only thing I've seen on this that argues that that's only part of the story. So what are we missing about how AI is going to affect high-income households in the K-shaped economy?
Heather Berger: Yes. Yeah, I think it's hard to talk about the economic outlook, the consumer outlook these days without thinking about AI. And as you mentioned, I think really the main focus so far has been potential white collar job loss, and this, of course, is an important channel. Labor income is really the main driver of consumer spending. But there are also several other transmission channels through which AI will affect consumer balance sheets.
And so ultimately, you were just talking about equity wealth, AI will also affect asset markets, which we've already started to see. It will affect consumer prices and policy decisions, and each of these will flow through to consumer spending and consumer credit performance. And so since different subgroups of consumers differ in the types of goods and services they buy and the composition of their balance sheets, the effects will not be uniform across the spectrum.
As we've seen with past innovation waves, AI has the ability to potentially widen income and wealth inequality, or it could help to close the gaps.
Sarah Wolfe: Can you dig a little bit more into some of these other channels outside of the labor market? So what is the wealth channel, and how does it filter through to high-income households? And then what also is the inflation channel that we should be looking at?
Heather Berger: Sure. So the wealth channel is really important for high income consumers because they have equity wealth that is very elevated relative to their labor income. So for that top twenty percent cohort, their equity wealth is around six times their annual labor income. Whereas for the lower income groups, they're about in line with each other.
And so even if the marginal propensity to consume out of income is higher than that out of wealth, for this high income group, asset markets are still a really important driver of spending. Now, for lower income groups and really across the spectrum, of course, inflation will be important as well and will really help determine purchasing power.
When we think about the price channel, we're really thinking in two phases. The first is that in the near term, AI could potentially create price pressures. So if we look at areas like electricity and software, we've already started to see that the demand from AI has led to increases in these prices. But over the longer term, we are expecting that eventually AI will lead to productivity gains, and therefore could lead to disinflation.
Sarah Wolfe: In which categories are we expected to see disinflation, and who does that benefit?
Heather Berger: So we're really first expecting to see it in the industries that have higher adoption rates. And so far those have been industries like financial services, tech. And so if we think about these services categories of spending, they really make up larger shares for the high income group, the older group. And so we do think they will benefit first from that disinflation channel.
Sarah Wolfe: I think if I sum up some of the key takeaways, it seems that the balance sheet is more important than income, and it's going to continue to be so. If you look at the top 1% wealth percentile, they're holding 70 times more wealth than the median wealth group, and that used to be 33 times in 1963, right? So that gap between those in the middle versus those at the top has widened, and this dynamic with AI is only probably going to continue to widen that gap, making people feel less and less secure about their finances, making it feel harder to be in the middle class, and in particular, making it feel unattainable to reach the next class, right, because that gap is so large. And so we'll be watching as a lot of these dynamics play out.
Heather Berger: Right. So asset markets will be just as important as labor markets in figuring out how the K-shape economy will evolve.
Sarah, thanks for taking the time to talk.
Sarah Wolfe: Great speaking with you, Heather.
Heather Berger: And thanks for listening. If you enjoy "Thoughts on the Market," please leave us a review wherever you listen and share the podcast with a friend or colleague today.

Our Global Head of Fixed Income Research Andrew Sheets discusses when markets may not adequately compensate investors for uncertainty around themes like Fed policy, AI financing and energy supply.
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Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley. Today, what American football can teach us about the value of ambiguity.
It's Thursday, September 10th at 2p.m. in London.
I really like this time of year. It's a little cooler outside. There's the excitement in the air of a start of a million new school years. And of course, it's finally American football season. Of the top one hundred US television telecasts in 2025, ninety were football games. In an increasingly divided world with an increasingly fragmented ecosystem for content, this unanimity is stunning. And while many factors explain football's popularity, one that I've come to appreciate more with time is its strategic complexity, especially the value of ambiguity.
Not tipping whether the play is a run or a pass, disguising whether and where you're going to blitz. Coaches work hard to keep their options open until the last possible moment. And as we enter September, this strategy is not just confined to football.
Take the Fed. Markets are pricing a roughly two-thirds chance of a rate hike next week, about the same chance that an NFL team passes on second and seven. Part of that uncertainty comes from exactly how you parse Fed Chair Warsh's comments at Jackson Hole. Chair Warsh said the Fed needs to be confident that underlying inflation is moving towards its objective, “clearly and at sufficient speed.” Otherwise, it has, "work to do." This was generally interpreted as a move closer to raising rates. But was it? What is sufficient speed? What counts as underlying inflation? And what does “work to do” actually mean? After all, if inflation is better in the second half of the year, as our economists expect, this framing could just as easily justify no action. We forecast the Fed to stay on hold next week. It is admittedly a close call.
Then there's ambiguity in AI financing. The numbers here are enormous. Morgan Stanley analysts forecast more than 1.3 trillion dollars of spending among the six largest hyperscalers in 2027, a sixty percent increase from the record-setting levels of this year. But how all this gets financed, that's less certain. There's an increasingly rich menu of options for financing across public and private markets, from investment-grade bonds to asset-backed securities, from direct financing to guarantees. The spending seems likely, but what form it takes and how much it impacts other markets is more ambiguous. My colleagues Matthew Hornbach and Vichy Tirupattur discussed some of these ambiguities and their potential effect on Treasury yields earlier this week.
Finally, ambiguity clouds the energy market. Some analysts are optimistic that oil flows are finally normalizing in the Strait of Hormuz. We are not. Coupled with major disruptions to Russian refining capacity, we've now raised our fourth quarter forecast to one hundred dollars per barrel for Brent oil and eighty-eight euros per megawatt hour for European natural gas.
Across these three themes, some of this ambiguity is intentional. Some simply reflects a wide range of possible outcomes. In football and in markets, keeping your options open can be valuable when you're calling the plays, but it's less attractive when you're being asked to price them. And that, for us, is the issue. There is plenty of uncertainty. We're not sure investors are being paid enough for it. A close call September Fed meeting, adverse seasonality, and very low levels of expected volatility leave us positioned for higher volatility across macro markets and cautious on mortgage-backed securities.
In credit, we think all of this issuance is a question of price, not capacity. We continue to expect record investment-grade supply this year with wider spreads as a release valve and prefer collateral-backed assets over unsecured corporates. And with oil a risk to both stocks and bonds, our US equity strategists think that energy equities offer an attractive hedge.
Ambiguity has value, but when the range of outcomes is wide and the price of uncertainty is low, we think investors should demand more compensation for it.
Thank you, as always, for your time. If you find Thoughts on the Market useful, let us know by leaving a review wherever you listen. And also tell a friend or colleague about us today.

Big Tech is pouring more than $1.4 trillion into AI, prompting investors to ask: Is it worth it? Our U.S. Internet analyst Brian Nowak looks at three business models that could earn 25 to 50 percent returns for Gen-AI-enabled technologies.
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Brian Nowak: Welcome to Thoughts on the Market. I'm Brian Nowak, Morgan Stanley's U.S. Internet analyst.
Today, can the enormous investment behind Gen AI actually pay off?
It's Wednesday, September 9th, at 9am in New York.
AI has moved quickly into everyday life. It helps people write software, research purchases, automate work, find information, among myriad[s] of other use cases.
But we need an infrastructure build-out of extraordinary scale to support all of this activity and more activity to come.
In all, we estimate that the major cloud providers are going to spend more than $1.4 trillion on this AI build-out next year alone. But compute capacity is potentially going to quadruple from 2025 to 2028, reaching roughly 120 gigawatts.
But all of the spending has raised a lot of questions for investors. One of the most common questions is: What kind of return on invested capital can these companies earn from all of these trillions of dollars of data center infrastructure investment?
Well, our bottom-up work points to encouraging answers to this question.
We see paths to roughly 25 to 50 percent return on invested capital, or ROIC, across three emerging AI business models. Now, ROIC is a useful way of measuring whether investments pay off. Think of it as how much after-tax operating profit can be generated relative to the capital required in the first place.
The first business model we've analyzed is renting compute power. This is the infrastructure layer of the AI economy. Cloud providers build data centers filled with advanced graphics processing units, or GPUs, and rent that compute capacity to customers. In our base case, a large next-generation data center can generate a return on invested capital of roughly 30 percent simply renting AI compute power.
And even if rental prices move around, our scenarios still produce returns ranging from low 20s percent to nearly 40 percent. So, despite the enormous cost of building and capital being deployed for these facilities, we think the economics here are quite attractive.
The second business model we've analyzed is where an AI lab has their own model, and they also own their own infrastructure. They give access to their model through an API to consumers and enterprises who then build upon it, they utilize the model. In some cases, they build applications using that model that can be future sources of productivity or efficiency for the economy.
In this scenario, we think the economics can be even stronger. When the model developer owns their own underlying infrastructure, our base case generates a roughly 75 percent incremental operating margin and a return on invested capital of 40 percent plus.
These returns on invested capital are impressive, but what determines whether these returns can actually materialize?
Well, two things matter a lot. The first is the price the developers are able to charge for tokens, which are the units of information that AI models process. The second factor that matters considerably is how efficient[ly] can this infrastructure process these tokens.
This is why continued improvements in chips and software to drive higher token throughput – or more tokens per GPU per second – are critical to the long-term unit economics across this AI ecosystem.
The third model we've analyzed is when the AI developers rent their compute infrastructure rather than owning it. So, effectively, they are paying someone else for the data centers and the GPUs that they need. While this lowers their returns on invested capital because another provider takes a piece of the unit economics, our base case still produces roughly a 30 percent incremental operating margin and 25 percent post-tax return potential.
So, while the AI build-out requires enormous investment, the size of the spending alone doesn't tell the whole story about whether or not there are economic returns to come.
What ultimately matters is the revenue and profit that the infrastructure can generate. And as more of the infrastructure shifts from training AI models to serving customers through emerging products and inference, we think we're going to get a much clearer answer to this question investors are asking today.
Was all this spending worth it? Our research suggests: Yes.
Thanks for listening. If you enjoy the show, please leave a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

U.S. Treasurys are the foundation of the bond market. But our strategists Matthew Hornbach and Vishy Tirupattur explain the growing impact of corporate credit as AI financing accelerates.
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Matthew Hornbach: Welcome to Thoughts on the Market. I'm Matthew Hornbach, Global Head of Macro Strategy at Morgan Stanley.
Vishy Tirupattur: I am Vishy Tirupattur, Chief Fixed Income Strategist.
Matthew Hornbach: Today, the interplay between the U.S. Treasury market and the corporate bond market.
It's Tuesday, September 8th at 10am in New York.
So, Vishy, what I'd like to do is start by asking you what's going on in the corporate bond market? What's coming to market? How much duration does it have? Talk to us about the theme of AI in corporate bonds.
Vishy Tirupattur: So, this is what is happening. Hyperscalers have enormous CapEx needs, and they'll see opportunity for realizing return on invested capital; and in anticipation of that, the CapEx requirements for the AI infrastructure are enormous.
And the key motivation that underlies is that the demand for compute vastly exceeds the supply of compute. And that as long as that demand-supply imbalance is there, there is a continuing need for CapEx, and that CapEx needs to be financed.
And credit markets across the board, not just the unsecured market. You know, credit markets in public space, private, investment grade, unsecured, secured, high yield, below investment grade, leveraged loans, private credit – all of these channels of the credit markets are going to be deployed to enable that financing.
Matthew Hornbach: Now, Vishy, you've written about this extensively over the course of the past year and have really been on the forefront of expecting a lot of supply. But have you even been surprised at the scale of the supply that we've gotten from these hyperscalers?
Vishy Tirupattur: We are surprised, not so much by the scale of the issuance, but certainly by the breadth and the depth of these markets. And also, the ability of the markets to deal with complexity associated with this issuance. So, you know, about a year ago, we were expecting that much of this would be investment grade only; much of this would be only U.S. dollar denominated. We were wrong.
We have seen issuance in seven currencies, and we have seen issuance substantially happen in investment grade, but also in high yield and in leverage loans. And a lot more in structured private investment grade credit and in securitized credit. We have been surprised by the ability of the markets to be both in their depth and the breadth and complexity; clearly been surprised.
Matthew Hornbach: And one of the features of some of the issuance that may have been the most impactful on other markets has been the duration of unsecured AI-related financing. Talk to us a little bit about what's going on there.
Vishy Tirupattur: So, if you look at the AI infrastructure, you can think of it in many different forms. One way of thinking about is the data centers building – the fab, the LAN, the chips and the servers. If you took the whole data centers, their expected life is something north of 20 years. And there is a lot of CapEx requirements.
So initially, when you're financing the entire data center as one package, there has been issuance that went well beyond the 20-year point in the term. And keep in mind that the CapEx requirements are kind of across the board.
So, it's not just been 20-plus year bonds. There have been bonds issued of various tenors, including a substantial supply of 20-plus year of duration.
Now what is happening is that the focus of some of that is changing towards more shorter-term component of it. So, we've gone from financing the entire data structure, moving towards financing components, and in particular chips.
The chips have a technological obsolescence factor associated with them. So, the chips need to be refinanced in about five years. So, the structures that are now increasingly emerging are towards amortizing structures that are more five-year duration, five-year maturity loans.
Matthew Hornbach: So, this sounds like an interesting shift from much longer duration, longer maturity issuance to something in what the U.S. Treasury would call the belly of the curve. Kind of in the two to five-year maturity sector. Is that right?
Vishy Tirupattur: So yes and no, and I'm hedging only for the following reason: Because a lot of this issuance, these issuers are relatively new in their size of these issuance, so they have not established a certain cadence of issuance.
It is not that they have given up on the longer maturity, but the focus is shifting. We expect more to the five-year point of the curve.
Another important thing is there has been a significant political pushback on the data centers. We have seen moratoria in the state of New York. It's a very live issue in much of the midterm elections. And opposition to data center is bipartisan, and it's very much alive.
So, because of this, we may have some slowdown in the buildup of data centers, therefore slowdown in the long-term CapEx. But then near term, you know, the chips that were bought a few years ago need to be replenished and new chips need to be deployed.
So, that financing focus might shift from a longer term to a shorter term. But that said, they're not going to let go entirely of the longer-term financing. Just the focus will shift towards the mid five-year term.
Matthew Hornbach: That's very interesting because in the U.S. Treasury market, the focus has not been on the five-year sector. It has been further out the yield curve, where 30-year Treasury yields have been making highs for; that we haven't seen for a couple of decades now. And it hasn't been just in the nominal yield component of Treasuries; it's been in the real yield as well.
And, in fact, the difference between the nominal and the real yield, the so-called break-even inflation rate, has actually been very stable throughout this move higher in overall bond yields.
Vishy Tirupattur: So, Matt, let me ask you this question. For the last several weeks, we have seen long-end rates, particularly 20-plus year rates being persistently high. What is in your mind driving this persistently high yield in the 20-plus year category?
Matthew Hornbach: So, this is something that Treasury Secretary Bessent alluded to in his recent interview on CNBC – that the month of August tends to be a month of lower transaction volumes in the U.S. Treasury market. And in particular, the middle of the month tends to be the lowest transaction volume period within any given month.
And so, what we think might be going on is that investors who have been investing in these corporate bonds that you've talked about – may be preparing their own balance sheets for the issuance that most people tend to expect to come in September.
Now, if that was the case, then it would be reasonable to assume that those investors tried to sell some of the bonds that they had. Or perhaps just stop buying any bonds in preparation for the supply that they would expect to come in September. If that was the case and the dealer community had to absorb that duration risk onto their balance sheets, they probably would want to recycle that back into the market.
And the most liquid way of doing that is to sell treasuries. And so, we do think that there was very likely some selling of treasuries by the dealer community, as they were absorbing corporate bonds from the investor base.
Vishy Tirupattur: So that makes sense, Matt. You know, if you think about the dealer community as well as investors, their anticipation of future; corporate bond issuance could drive their actions today.
But the only point I would make is that because these are new issuers, and because they have not established a cadence, there could be substantial variability in their frequency. And periodicity that will come to the market. And in what tenor.
You know, there's this change I talked about – longer term for financing needs versus component financing needs. There are all these degrees of freedom these issuers have that they can use that degrees of freedom. And the investors and the dealers don't have a lot of sense of what that might be.
Matthew Hornbach: It sounds like there's going to be a lot of uncertainty, which might mean that there's going to be a lot of volatility.
So, with that Vishy, thanks for sitting down and talking about the bond market with me.
Vishy Tirupattur: Great to hang out with you, Matt.
Matthew Hornbach: And thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen. And share the podcast with a friend or colleague today.

Investors have plenty to digest this month, from economic data to central-bank decisions. Our Global Head of Fixed Income Research Andrew Sheets outlines what could drive the next bout of volatility.
Read more insights from Morgan Stanley.

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Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley.
Today, several catalysts for more volatility later this month.
It's Friday, September 4th at 2pm in London.
Over more than a century of market history, Septembers have tended to see more volatility than the average month. You can't exactly set your watch by it, but the trend is definitely there. As investors come back from summer and capital market activity restarts in earnest, things historically tend to move.
This idea seems especially relevant this year. Despite the headlines, it was a pretty calm summer for markets. Since early June, U.S. stocks, yields, and credit were all modestly higher, and they got there with minimal movement. The realized volatility – that is how much these markets are moving on a daily basis – has been historically low.
September offers a number of catalysts that could test that.
First and foremost is the Fed. Inflation remains above the central bank's target, and markets are pricing a roughly two out of three chance of a rate hike at the September 16th meeting. That's more uncertainty this close to a meeting than we've had in a while – and the impact goes far beyond a single decision. Live meetings from the Bank of Japan and the European Central Bank also loom in September.
September is also a month that historically sees unusually heavy capital market activity. That makes sense. If you're a corporate and looking to raise money, it's often better to wait until investors are back from the summer before going out looking for those funds.
But this September could be unusually active, given a growing IPO pipeline and continued funding needs from AI-related construction. And so, it's fair to say that even adjusting for September's usually heavy pace, there's an unusually wide range of outcomes around where capital market activity could land this month.
Investors are also coming back from the summer with major uncertainty still hanging over global energy markets. Morgan Stanley's commodity team still sees global energy flows as severely restricted and recently raised their forecast for oil prices, seeing them reach about $100 a barrel in the fourth quarter of this year.
The price of what's in that barrel is becoming even more extreme, with the price of diesel fuel in Europe up 140 percent since January 1st. And so, as inventories continue to draw down and questions around the duration of this conflict persist, both factors could drive more market movements.
The good news is that while Septembers have historically been more volatile months, they're not necessarily a bellwether. And that could apply again. By month-end, we should have a much better idea of the Fed's path, the scale of capital market activity, and the state of energy supply.
But until then, the level of expected volatility across many markets, particularly interest rate and foreign exchange markets, remains unusually low. Given this backdrop, we think those levels of expected volatility can rise.
Thank you, as always, for your time. If you find Thoughts on the Market useful, let us know by leaving a review wherever you listen. And also tell a friend or colleague about us today.