This corona recession started in February 2020. Officially, it is still ongoing. But, perhaps, it has in fact already ended. This might seem confusing but it helps explaining the performance of financial markets during this “recession”.
In my soon-to-be-released book From Main Street to Wall Street (link and link), I – among many other things – carefully examine the historical relation between the business cycle and financial markets. I verify that stock markets typically perform considerably better during expansions than recessions. In the book, I examine and explain why this is so. I also explain that this is not a bulletproof finding. It is not always so. Sometimes stock markets do fine during recessions. Is this recession one of them?
If history is any guide, it will be four good years on the stock market if Biden wins on November 3. Historically, stocks have performed so much better under Democratic presidents. The question is whether history will be a guide also this time around.
Are Democratic or Republican presidents better for the stock market? To evaluate, let us recall the performance of the US stock market under Trump – a Republican – and compare it to its performance under Obama – a Democrat and Trump’s predecessor. Afterwards, let us look at the full history of the stock market under Democratic and Republican presidents. Finally, let us discuss what it implies for this election and the next four years on the US stock market.
Today, October 1, the Council for Return
Expectations publishes its updated expectations. We expect very low – negative –
returns on safe assets over the next five and ten years. We expect an equity
premium around 5-7 percent.
I chair the Council for Return Expectations (link). Twice a year, we update our expectations. Today, we publish our latest forecasts.
In a June post (link), I described the history of the council, how we operate, who we are, why we publish expected returns, what they are used for, and so on. Briefly, here, the Council consists of Torben M. Andersen (professor of economics, University of Aarhus; link), Peter Engberg Jensen (former CEO of Nykredit and current chairman of Financial Stabilitet; link ), and myself as Chairman. Based on inputs from Blackrock, J.P. Morgan, Mercer, and State Street (thanks!), we estimate expected returns, risks (standard deviations), and correlations between returns on ten different asset classes over the next five and ten years. We also publish expected returns on two assets classes (stocks and bonds) for investment horizons exceeding ten years.
The forecasts are important in
Denmark. When Danish pension funds project how the retirement savings of their customers
will most likely develop (with confidence bands), they base their projections on
the Council’s return expectations. Similarly, when banks advice Danish
investors on their non-retirement savings, they use the expected returns provided
by the Council. I think it is fair to say that Denmark has been a front-runner
in designing a system where banks and pension funds do not compete on expected
returns, but all use a
common set of return assumptions, determined by an independent Council.
forecasts published today will be used for pension projections and projections
for savings outside retirement accounts as of January 1, 2021. In spring 2021,
we will publish updated expectations that will be used from July 1, 2021, and
the following six months. And so on.
is as follows. The expectations we publish today are based on market data from
July 1, 2020. We use the period from July 1 until today (October 1) to
determine our expectations. The expectations we publish today are then used by
banks and pension companies as of January 1, 2021.
We publish forecasts for expected returns on ten asset classes over the next five years, years 6-10, and over the next ten years. Returns are average annual returns in Euros/Danish kroner (the Danish kroner is fixed to the euro). These are our expectations:
an investment in safe assets (defined as a five-year duration portfolio of 30%
Eurozone government bonds (minimum Triple-B), 20% Danish government bonds, and
50% Danish mortgage bonds – Danish mortgage bonds are triple-A rated, by the
way) to lose money every year on average over the next ten years. This is noteworthy.
On average, we expect such an investment to lose 0.1% per year over the next
ten years. Over the next five years, we expect it to lose 1.2% per annum. The
reason why we expect such low returns is that the underlying bond portfolio has
a duration of five years, as mentioned, and we expect a normalization of
interest rates, causing capital losses which drag down expected returns.
We expect lower returns from bond investments than we did in our June update (link). In June, we expected government and mortgage bonds to return a negative 0.3% per year over the next five years and a positive 0.3% per year over the next ten years. The reason why we expect lower returns now (-1.2% and -0.1% for the five and ten year periods, respectively) is that our forecasts today, as mentioned, are based on market data from July 1. On July 1, interest rates and credit spreads were lower than those used at our previous update. Our previous update was based on March 31 market data. In March, the corona virus had caused a hike in yields and a widening of credit spreads. Since then, yields and spreads have come down.
equities are expected to yield around 6% per year. This means that the equity
premium is expected to be 5%-7%, depending on the investment horizon.
low inflation. We expect Danish inflation to be 1.2% per annum over the next
five years and 1.5% over the next ten years.
investment horizons above ten years, we expect equities to return 6.5% per
annum and bonds 3.5%. This is the same as we expected last year (we update long-run
expectations once a year). Over the coming year, we will make a thorough evaluation
of these long-run expectations.
We also publish standard deviations for all asset classes and for all horizons, as well as correlations and fees. Standard deviations are high for high-return assets; nothing comes for free. We thus publish many numbers. You can find all these numbers on the webpage of the Council: link.
We (The Council for Return Expectations) expect that Danish investors will lose money – even before fees and inflation – if investing in safe assets over the next ten years. You can expect positive returns on other asset classes, but then you need to take on risks.
Historically, the risk-free real interest rate has been around 2% per annum and the nominal interest rate around 4%. Investors would see their savings grow, even without taking on risk. This is not what investors should expect going forward. The fact that you cannot expect your portfolio to grow without taking on risk tells us a lot about the low-return world we are living in.
Do asset purchases by central banks raise economic output and inflation? An interesting new paper finds an affirmative answer, but also – and this is the main point – that the size of the effect depends on whom you ask. If you ask central bankers, they will tell you that the effect of QE is large. If you ask independent academic researchers, they will tell you it is considerably smaller. This difference indicates that central-bank research on this topic might be biased. It also indicates that Quantitative Easing is probably not as effective as we are told.
One of the defining
characteristics of financial markets since the financial crisis in 2008 is the use
and influence of “unconventional policy tools” by central banks. As monetary policy
rates have been close to zero, central banks have been unable to stimulate the
economy via even lower interest rates. Instead, central banks have started
purchasing financial assets, mainly government bonds. These alternative policy
tools are labelled “asset purchases by central banks”, or simply “Quantitative
Quantitative Easing increases the demand for government bonds, thereby raising their price and bringing down their yields. When yields on government bonds fall, other yields in the economy, such as yields on mortgage bonds, fall, too. This should promote economic activity and raise inflation, central banks argue.
Quantitative Easing is not uncontroversial, though. In several of my posts (link, link, and link), I have argued that it raises other asset prices in the economy, such as stock prices. Some fear that this induces bubble-like behavior in asset prices. Also, QE might distort signals from asset prices, causing unclear signals from prices about the underlying state of the economy and financial markets. In addition, by raising other asset prices, Quantitative Easing might contribute to increasing inequality, as financial assets are typically held by the already wealthy. On the other hand, if QE helps promoting economic activity, it helps reducing unemployment among low-income groups, which should reduce inequality, central banks argue in return (link). In the end, then, to justify Quantitative Easing, it should have a sizeable impact on inflation and output, outweighing the potentially negative effects on other parts of the economy.
Many papers have analyzed the effects of quantitative easing. A brand-new paper (link) summarizes these analyses and asks the question whether results are more positive when central bank economists analyze QE. Given that central banks influence public opinion, the latter question is important when we evaluate the most significant policy intervention during the last decade.
two reasons why I think this paper is particularly interesting. First, it
summarizes research on QE in a neat way. It concludes that QE is effective, but
not as effective as we are often told. Second, it emphasizes the importance of
independent academic research. As the faculty representative on the Board of
Directors at Copenhagen Business School, stressing the importance of academic
research, I find this to be an important conclusion, too.
To avoid any
misunderstandings about my own view, let me stress two things before getting to
First, I believe that targeted central bank intervention can be useful. In my last post, I describe one monetary policy intervention that clearly fulfilled its goal (link). During a crisis, if markets are malfunctioning, there can be good reasons for policy interventions. On the other hand, I am skeptical towards the view that the advantages of endless asset purchases by central banks outside crisis periods outweigh their disadvantages. This paper indicates that QE is less powerful than central bankers tell us, lending some support to this view.
Second, my point here is not to say that central bank research is suspicious in general. On the contrary, I strongly recommend central banks to invest in economic research. I believe that better decisions are taken when based on solid academic analyses. So, central banks should be encouraged to invest in research, but their own evaluations of their own actions are probably not unbiased.
Brian Fabo, Martina Jancokova, Elisabeth Kempf, and Lubos Pastor (link) study 54 analyses, written/published between 2010 and 2018, of the effects of quantitative easing in the US, the UK, and the Eurozone. 57% of the papers have been published in peer-reviewed journals. 60% of the authors are affiliated with central banks.
Lubos Pastor and co-authors collect estimates of the effect of QE on economic output and inflation across the 54 studies and report the average (and median) effects. They also investigate whether the reported effects are different if a study is conducted by central bank researchers.
Bias in central bank research
Pastor et al. list five reasons why central bank research might be biased (directly taken from the paper, page 2, here):
”First, the economist may worry that the nature of her ﬁndings could aﬀect her employment status or rank. Is she less likely to get promoted if her ﬁndings dent the bank’s reputation? Could she get ﬁred?”
”Second, the economist may be unsure whether her research will see the light of day. Bank management could in principle block the release of studies that ﬁnd the bank’s own policy to be ineﬀective, or to have undesirable side eﬀects.”
”Third, the economist may suﬀer from a conﬁrmation bias (Nickerson (1998)). A central bank employee may believe a priori that the bank’s policies are eﬀective, and she may select evidence supporting her prior.”
”Fourth, the economist may care about the bank’s reputation.”
”Finally, the economist may care about her own reputation if she is senior enough to have participated in the formation of the bank’s policy.”
The main findings of the paper are collected in this graph:
Pastor et al. report that a QE-program at its peak, i.e. when a QE program has its maximum effect, on average (across the 54 studies) raises GDP by 1.57% and the price level in the economy by 1.42%, as indicated by the blue columns in the graph (”Average all studies”). This seems to be relatively large effects, I would say.
Do these effects depend on the affiliations of researchers? Pastor and co-authors find that central-bank affiliated researchers report significantly larger effects. If counterfactually changing the share of central bank researchers in a study from 0% to 100%, the peak effect on output is estimated to be 0.723%-points larger and the peak effect on the price level 1.279%-points larger. I illustrate this in the figure above as the “Effect of CB authors”. Compared to the overall average effect, the effect of central-bank authorship is large. For the price level, going from 0% to 100% central-bank authorship almost corresponds to the total average estimated effect of QE on price levels across all 54 papers . In other words, if you think central bank estimates might be biased, the ”true” average effect of QE programs is considerably smaller than the estimated overall average effect.
Pastor and coauthors report the average point estimate of all papers and the additional effect of central bank affiliation, as indicated in the figure above. They do not report the average estimate from papers written by central bank authors, respectively written by academic authors. I asked Lubos Pastor about this. In private email correspondence with Lubos and Elisabeth Kempf, they inform me that papers written by central bank authors (defined as papers with at least one central bank author) estimate a 1.752% peak effect on output. Papers written by academics (defined as papers with zero central bank authors) find a considerably smaller effect, 0.996%. For inflation, the peak effect on output is 1.791% for papers written by central bank affiliated authors. Academics estimate a much smaller effect, only 0.545%. In spite of massive asset purchases (we are literally talking trillions of dollars, euros, and yen), the average effect on inflation is small, at 0.5%, academics report. Less than a third of what central bank affiliated researchers report.
As academics, we are not only interested in the size of the coefficients/effects, but also whether effects are statistically different from zero. Pastor and coauthors report a striking finding here. While all papers written by central bank researchers find that QE has a statistically significant effect on output/inflation, only 50% of papers written by independent academics find significant effects.
Pastor and co-authors note that the German central bank (the Bundesbank) has
been particularly skeptical towards ECB QE. So, what happens if you look at
researchers affiliated with the German central bank? Bundesbank researchers
find much smaller effects of QE. In fact, Bundesbank researchers find an even
smaller effect of QE on economic output than independent academics. Again, this
indicates that the preferences of an institution seem to influence the
conclusions of its researchers.
presents additional analyses, such as looking more closely at the mechanisms at
play, i.e. career concerns, involvement of management in research, and so on.
Read the paper if you want to know more about this.
I think the paper by Brian Fabo, Martina Jancokova, Elisabeth Kempf, and Lubos Pastor is interesting also because it summarizes what the average effect of QE is, based on a large number of studies. Across more than 50 papers, the average maximum effect of QE on GDP and the price level is around 1.5%. This is useful information in itself.
Some papers are written by central bank researchers and some by independent academics. It seems reasonable to hypothesize that central bankers might have a tendency to view their own policies in a more favorable light. This is what Lubos and co-authors find. They report that the effect of having central bankers as authors of an analysis is almost as large as the average reported effect of QE on the price level itself. In private email correspondence, they also tell me that the average effect, estimated in academic papers, of QE on the price level is only 0.5%, i.e. very small, almost negligible. This is of course a controversial result. I predict it will generate intensive debate.
The fact that I discuss this paper here should not be taken to imply that I am skeptical towards central bank research in general. In fact, I am sure the quality of monetary policy decisions is improved when central bankers have access to the latest research. Also, I have no reason to believe that central bank research on other topics than monetary policy should be biased. But, when it comes to assessing their own actions, researchers in central banks might be subject to certain biases. It requires some guts to tell senior management that the trillions they have spent on quantitative easing probably has not been very effective. Instead, it might further your career if you paint a rosier picture. This is important to recognize.
I view the bottom line as follows: QE probably has some effect, but its effect is considerably smaller than we are told by central banks.
looming recession and financial market panics, the Fed intervened heavily in
late-February/early-March, lowering the Fed Funds Rate to zero and expanding its
balance sheet dramatically. In spite of this, markets kept on panicking. Then,
suddenly, on March 23, everything changed. Stock markets started their rally.
This was not because the Fed lowered the rate or expanded its asset purchases
even further, nor because the economic data improved. What happened? The Fed made
an announcement. Nothing else. It is a fascinating illustration of how
expectations can change everything on financial markets.
Much has been written about the massive interventions of the Fed during February and early March. In my previous post (link), I list Fed interventions as one of the reasons why the stock market is back to pre-crisis highs. In this post, I dig one step deeper and explain the fascinating story of how the Fed said something and thereby rescued markets.
Asset purchases and rate reductions did not save markets in February/March
Let us start by
illustrating how the actual Fed interventions (interest rate changes and asset
purchases) did not save markets in
March. The Fed lowered the (lower range of the) Fed Funds Target Range to 1% from
1.5% on March 2 and then again to 0% less than two weeks later, on March 14. At
the same time, it bought Treasuries and mortgage-backed securities to the tune
of USD 600bn per week. These interventions succeeded in lowering yields on government
and mortgage-backed bonds, but did not cheer up stock markets.
This graph shows how
Treasury yields came down significantly in February/March, by basically
1.5%-point (from close to 2% to close to 0.5%; I show yields on 10-year
Treasuries in this graph), as a result of reductions in the policy rate (the Fed
Funds Rate) and asset purchases by the Fed.
The graph also shows that the SP500 continued falling throughout February/March. In other words, the massive interventions by the Fed in late-February/early-March (and these interventions really were massive – buying for USD 600 bn per week and lowering rates to zero is indeed a massive intervention) did not convince stock markets that the situation was under control. And, remember, these were not minor stock-market adjustments. It was the fastest bear market ever (link).
What turned the tide?
On March 23, the SP500 reached its low of 2237, a drop of 31% compared to its January 1 value. Since then, everything has been turned upside down and markets have been cheering, as the above graph makes clear.
What happened on March 23? The Fed had its finest hour. It did not do anything. It merely said something. A true “Whatever it takes” moment.
As you remember, a “Whatever it takes” moment refers to the July 26, 2012 speech by then ECB-president Mario Draghi (link). The speech was given at the peak of the Eurozone debt crisis. The debt crisis pushed yields on Italian and Spanish sovereign bonds to unsustainable levels. Italy was too big to fail, but also too big to save. The pressure on Italy was a pressure on the Eurozone construction. Mario Draghi explained the situation and said the by-now famous words:
“But there is another message I want to tell you. Within
our mandate, the ECB is ready to do whatever it takes to preserve the euro. And
believe me, it will be enough.”
Investors understood that the ECB would be ready to buy sovereign bonds to save the Euro. Markets calmed down. Italian and Spanish yields fell. And this – and this is the point here – without the ECB actually intervening, i.e. without the ECB buying Italian or Spanish bonds. The announcement that the ECB would intervene was enough to calm down investors.
Why is this relevant here? Because on March 23 the Fed sent out a press release, announcing that:
“…the Federal Reserve is using its full range of authorities to provide powerful support for the flow of credit to American families and businesses.”
And, then, as one of the new features announced the:
“Establishment of two facilities to support credit to large employers –
the Primary Market Corporate Credit Facility (PMCCF) for new bond and loan
issuance and the Secondary Market Corporate Credit Facility (SMCCF) to provide
liquidity for outstanding corporate bonds.”
This statement turned everything upside down. The important thing is that this turn of events happened without the Fed using any money at all. It was a “Whatever it takes” moment (though, perhaps not as Dirty-Harry dramatic as Mario Draghi’s “And believe me, it will be enough”): The Fed announced what they would do, investors believed the Fed, and markets started cheering. It is a prime example of how investor expectations influence financial markets.
What are the PMCCF and
SMCCFs, why did the Fed announce them, and
why were their effects so dramatic?
The lowering of the Fed Funds Rate and the purchasing of Treasuries succeeded in lowering yields on safe assets, such as Treasury bonds, as explained above, but did not lower yields on corporate bonds. In fact, during the turmoil in late-February/early-March, credit spreads (the spread between yields on corporate bonds and safe bonds) widened dramatically. When yields on corporate bonds rise, it becomes more expensive for corporations to finance their operations. And, when yields rise a lot, as in February/March, investors get nervous about the profitability and survival of firms.
This graphs shows how yields on both the least risky corporate bonds (Triple A) and speculative grade bonds (Single B) rose dramatically in relation to 3-month Treasury Bills, with yields on lower-rated bonds (Single-B) naturally rising more than yields on higher-rated (Triple-A) bonds.
The important point in the picture is that the spreads continued rising throughout late-February/March, in spite of the intensive interventions described above, i.e. in spite of Fed purchases of government and mortgage bonds. The Fed was happy that safe yields fell, but was concerned that credit spreads kept on rising. Volatility in corporate bonds markets also rose (link), making the whole thing even worse.
More or less all firms saw their funding costs increase, even when there were differences across firms in different industries, with firms in the Mining, Oil, and Gas, Arts and Entertainment, and Hotel and Restaurant sector hit the hardest, and firms in Retail and Utilities sectors less affected (link). The Fed became nervous because higher funding costs for firms affect firms negatively, causing them to cut jobs, reduce investments, etc.
The Fed decided to act. It announced on March 23 that it would launch two new programs, PMCCF and SMCCFs. The PMCCF is the ’Primary Market Corporate Credit Facility’ and the SMCCF the ’Secondary Market Corporate Credit Facility’. Primary markets are where firms initially sell their newly issued bonds. The PMCCF should thus ease the issuance of newly issued corporate bonds, i.e. help firms raise funds. Secondary markets are where bonds are traded afterwards, i.e. the SMCCF should ease the trading (liquidity) of already existing corporate bonds.
The effect on equity and credit markets of the announcement of the PMCCF and SMCCFs was immediate and spectacular. Immediately after the announcement, credit spreads narrowed (see, e.g., link, link, and link).
Interestingly, stock markets reacted immediately, too. The stock market, thus, did not react to the lowering of the Fed Funds Rate and the extensive expansion of the Fed balance sheet in late-February/early-March, but reacted strongly to the announcement that the Fed would buy corporate bonds on March 23.
This (supercool, I think 🙂 ) graph shows developments on corporate bond and equity markets. The graph shows the spread between yields on AAA-rated corporate bonds and 3-month Treasury Bills and the stock market inverted, both normalized to one on January 1. The graph for the inverted stock market means that when the SP500 is at 1.45 on the y-axis on March 23, the stock price at January 1 was 45% higher than it was on March 23.
The parallel movements in equity and credit markets are striking. Equity and credit markets moved in parallel during January, when stock markets rose and credit spreads narrowed, in late-February/early-March when credit spreads widened dramatically and stock markets fell like a stone, as well as after March 23, when both credit spreads and stock markets improved spectacularly. Since then, stock markets have continued to rise and credit spreads have continued to narrow.
The correlation between the two series is an astonishing 0.93 for the January 2 through May 31 period.
The Fed has experience with and a mandate to buy mortgage-backed
securities and Treasuries. It had no such experience when it comes to purchases
of corporate bonds and corporate bond ETFs. This means that the Fed could not
start buying corporate bonds on March 23, it needed an institutional set-up. It
created an SPV with capital injections from the Treasury and leverage from the
New York Fed, it asked a financial firm (Blackrock) to help them purchase the
bonds, etc. These things take time. The Fed only started buying corporate bond ETFs
in mid-May and corporate bonds in mid-June.
Given that the Fed only started buying bonds and ETFs in May/June, the spectacular
turnaround on March 23 really was due to the announcement only.
In slightly more detail, it played out as follows. The March 23 announcement primarily dealt with higher-rated corporate bonds (Investment Grade), and spreads narrowed immediately. On April 9, the Fed announced that they would expand “the size and scope of the Primary and Secondary Market Corporate Credit Facilities (PMCCF and SMCCF)”, buying bonds that were Investment Grade on March 22 but had been downgraded since then as well as corporate bond ETF. High-yield spreads tightened even more (link). Today, the Fed explains that the programs allow the Fed to buy “investment grade U.S. companies or certain U.S. companies that were investment grade as of March 22, 2020, and remain rated at least BB-/Ba3 rated at the time of purchase, as well as U.S.-listed exchange-traded funds whose investment objective is to provide broad exposure to the market for U.S. corporate bonds.”
The actual purchases began in May/June, several
months after the announcement. Interestingly, the purchases themselves have had a
modest impact only. First, the amounts of corporate bonds and corporate bond ETFs
bought pale in comparison with the amounts of mortgage-backed securities (MBS) and
Treasuries bought. The Fed has bought MBS and Treasuries to the tune of USD 3,000bn
this year. It has “only” bought corporate bonds and ETFs for around USD 12bn. The
amount used to buy corporate bonds thus corresponds to less than 0.5% of the
amount used to buy MBS and Treasuries. This graphs shows the daily purchases. Using
the latest figures from August 10, the Fed has basically stopped buying
So, the Fed has not bought a lot of corporate debt. It has, however, the power to buy a lot. The PMCCF and SMCCF set-up is such that the Treasury has committed to make an USD 75bn investment in the SPV that buys the assets (USD 50bn toward the PMCCF and USD 25bn toward the SMCCF). The New York Fed then has the ability to level this up by a factor of ten, i.e. the Fed can buy corporate debt for up to USD 750bn. This is a sizeable fraction of the total US corporate bond market. This, that the Fed can potentially buy a lot, helps making the program credible and thus helps explaining its powerful impact.
Given the success of the PMCCF and SMCCFs, commentators have started arguing that the Fed should be allowed to buy corporate bonds as part of its standard toolkit (link). This might be relevant, but purchases of corporate bonds by central banks raise a number of dilemmas:
Keeping zombie firms alive. By easing up stresses in corporate bond markets, the Fed calmed down markets. This was the intention of the PMCCF and SMCCF announcementsand it worked. It eased access to funding for firms, and firms have raised a lot of cash as a consequence (link). It is positive that malfunctioning markets are stabilized, but if central bank intervention makes markets too cheerful it may allow firms that in principle should not receive funding to nevertheless get it. And, thereby, to keep firms alive for too long, and increase firm leverage too much. Basically, the fear is that programs such as the PMCCF and SMCCFs create too many zombie firms. My CBS colleague Fabrice Tourre and his co-author Nicolas Crouzet has an interesting paper that examines this (link). Fabrice and Nicolas find that when financial markets work perfectly (no disruptions), Fed intervention might be detrimental to economic growth. On the other hand, if markets are disrupted, Fed intervention might prevent a too large wave of liquidations. One thus needs to determine when markets are disrupted “enough” to rationalize interventions in credit markets. This is no straightforward task.
Distributional aspects. By buying some bonds but not others, the Fed exposes itself to the critique that it helps some firms at the advantage of other. To alleviate such criticism, the Fed has been very transparent and publishes a lot of information about the bonds it buys, the prices at which it buys the bonds, etc. (link). Nevertheless, it is easy to imagine that some firms at some point will start saying ‘Why did you buy the bonds of my competitor, but not my bonds’?
The Fed put. The more the Fed intervenes when troubles arise, the more investors get reassured that the Fed will also come to the rescue next time around. When the Fed saves markets, it is sometimes called the Fed exercises the “Fed Put”. The potential problem here is that if investors believe that the Fed will exercise the Fed put, investors will be tempted to take on even more risk. Those of us concerned about systemic risks get nervous.
So, in the end, the announcement of the PMCCF and SMCCFs was crucial during this crisis. There are, however, important dilemmas that need to be addressed when evaluating whether such programs should be part of the standard toolbox. I am not saying they should not. I am saying that one needs to be careful.
launched massive “traditional” interventions in late-February/early March,
lowering the Fed Funds Rate and buying mortgage and government bonds. In spite
of these very large interventions, equity and credit markets kept on tail
spinning. When firms struggle, it hurts economic activity and employment. The
Fed got nervous.
The Fed announced – and this is the whole point here; they only announced – that they would start buying corporate bonds. Markets turned upside down. Credit markets stabilized, credit spreads narrowed, corporate bond-market liquidity improved, and stock markets cheered. And, all these things without the Fed spending a single dime until several months after the fact. Even today, the Fed has spent very little (some might say that USD 12bn is a lot, but compared to asset purchases of USD 3,000bn, it pales).
It was a “Whatever it takes” moment. It illustrates how managing investor expectations can be crucial. Understanding this announcement is thus important for understanding the behavior of financial markets during this pandemic.
The behavior of the stock market during this recession raises three main questions: (i) why did stock markets fall so spectacularly during February/March, (ii) why did stocks rebound so spectacularly during April/May, and (iii) why is the stock market currently at its pre-crisis level? My previous post (link) presented the stylized facts and addressed stories that cannot explain the facts. In this post, I provide some potential explanations.
I my previous post (link), I mentioned that the rebound in the stock market since its low in March cannot be explained by (i) revisions to the economic outlook – in fact, economic forecasts were revised down during spring, (ii) a preference for just a few (FAANG) stocks, as the rebound has been international and broad based, even when large-cap stocks have done particularly well, (iii) a hypothesis that this recession is not particularly bad – in fact, at least in the short run, this recession is much worse than the 2008 recession, or (iv) the fact that markets also recovered after 2008 – markets always recover, i.e. this is not an explanation.
What can explain it, then? First, a warning: I do not want to raise expectations too much (sorry). I will not be able to offer a full explanation of these puzzles in an app. 1,900 words blog post. Also, as you will see, puzzles remain, even if we can explain some things. Finally, I have not seen a good explanation of these puzzling stylized facts (though Gavyn Davies’ piece in the FT is a good place to continue after reading this post; link). Hence, it would not be serious nor academic if I claimed that I could explain everything. What I can do, though, is to provide some hints at what might be relevant parts of an explanation.
Explanations that contain elements of truth
Earnings suffered more in 2008
In the end, earnings and not economic activity (GDP) determine stock prices. In the long run, there is a strong relation between earnings and economic activity, which is why we focus on economic developments when we try to understand stock markets. The relation is not one-to-one, though, in particular in the short run. Hence, let us discuss earnings.
Earnings suffered dramatically in 2008. Often, we look at 12-month
earnings, as quarterly earnings are volatile. 12-month reported earnings per
share for the S&P500 came in at 6.86 in Q1 2009, which is app. 10% of
12-month reported earnings in Q1 2008. I.e., earnings fell by a mind-blowing
90% during the financial crisis of 2008.
Earnings fell in Q1 2020, too, but much less: From USD 139 per share in Q4 2019 to USD 116 (12 month reported earnings), i.e. by 17%. At the time of writing, around 90% of companies in the S&P500 have reported earnings for Q2 2020. Earnings (12-month reported) seem to be coming in at close to USD 96 per share, which is a drop of 17%, too, compared with Q1. Earnings have thus dropped by 17% during each of two consecutive quarters. But, and this is the main thing, these drops pale in comparison with autumn 2008. In autumn 2008, earnings dropped by 68% from Q3 to Q4 and then again by 54% from Q4 2008 to Q1 2009. This figure shows the quarterly percentage changes in 12-month reported earnings of the S&P500 in 2008 versus 2020. “0” on the x-axis is Q2 2020, respectively Q4 2008. The drop in earnings was just so much larger in 2008.
“1” on the x-axis in the figure is thus Q1 2009 and Q3 2020, i.e. firms’ expected earnings in Q3 2020 (and actual earnings in Q1 2009). If these expectations hold true, earnings are expected to do relatively fine going forward.
Earnings do fall from Q1 to Q2 this year. Earnings surprises matter, too, though, i.e. whether actual earnings are higher (or lower) than expected. At the time of writing, around 80% of companies have reported positive earnings surprises for Q2. This, that actual Q2 2020 earnings are better than expected, helps explaining why stock prices rise currently.
If investors primarily look at earnings, this helps explaining why stock prices fell considerably more in 2008 than they have done during this recession and why the stock-market recovery took longer in 2008. Earnings have simply not suffered so much this time around. For that reason, stock prices have not suffered so much this time around either.
Monetary and fiscal policies have been aggressive
Another factor that helps explaining why stock prices today are at
pre-crisis levels, in spite of the severity of the recession, is that policy
interventions have been aggressive. Immediately, at the onset of the crisis,
monetary policy turned very expansionary. This can be illustrated by the weekly
changes in the Fed balance sheet, as in this graph:
The figure plots the weekly changes in the Fed balance sheet due to purchases of financial assets, in millions of USD. The Fed balance sheet changes when the Fed buys/sells financial assets. During March this year, there were weeks when the Fed intervened to the tune of USD 600bn. This is an enormous amount of money. It is, by way of comparison, more than twice the weekly amounts spent during the financial crisis of 2008, the figure also shows.
At the same time, yields are very low. This forces investors to buy risky
assets if they want some kind of return, supporting stocks.
In addition to very aggressive monetary policy, fiscal policy has also been
Why did stock prices fall during February/March?
OK, so earnings have not fallen as much as in 2008, earnings for Q2 2020
have surprised positively, monetary and fiscal policies have been very
aggressive, central banks pump liquidity into the system, and interest rates
are very low, implying that investors need to invest their money in risky
assets if they want a positive expected return. These features help explaining
why stock prices today are not much lower than in the beginning of the year.
But, if this is the story, why did stock markets drop so dramatically in March?
Changes to expectations to earnings
Earnings have dropped in Q1 and Q2, even if considerably less than in 2008. Perhaps the stock-price drop in February/March was simply due to a downward revision in expected earnings. Alas, this is not the case. Landier & Thesmar (link) have an interesting analysis where they look at analysts’ expectations to the earnings of S&P500 companies during Q1 and Q2 2020. They find that analysts cut their expectations to earnings of S&P500 firms, but not so much that it can account for the drop in stock prices during February/March. In other words, the drop in stock prices in February/March cannot be explained by analysts reducing their earnings forecasts.
If the drop in expected earnings cannot account for the drop in stock prices in February/March, what happened then? Financial economists have a straightforward way of explaining this. Stock prices are discounted cash-flows. To understand stock price movements, we must understand cash-flow and discount rate movements. When the fall in expected earnings is not large enough to account for the drop in stock prices, discount rates must have increased in February/March. In other words, during February/March investors required a higher expected return if they should invest in stocks. This is also the conclusion in Landier & Thesmar and in Gormsen & Koijen (link). Basically, this is the conclusion in most empirical asset-pricing literature: stock prices move too much to be justified by movements in cash-flows. Discount rate variation is the reason, the literature concludes.
When stock prices move, and it cannot be because of cash-flow movements, it
must be discount-rate movements. This is fine, but it is also somewhat
tautological: If A = B + C, and A moves but B does not, then it must be because C moves.
A more difficult question is what makes
discount rates move in the first place? I.e., what is the deeper economic
explanation? Perhaps/probably risk aversion spiked, when investors in February
suddenly realized the severity of the virus. Perhaps/probably some market
participants faced funding constraints. And so on. In other words, I agree that
discount rates most likely spiked during March, but I am not 100% sure why and
I do not know if the increase in risk aversion is large enough to account for
the increase in discount rates, and thus account for the drop in stock prices.
So, a potential reason why stock markets have done better than in 2008 is
that earnings have not suffered as much as in 2008 and central banks have
flooded the market with liquidity (and yields are at zero). The reason why we
saw a massive fall in stock prices in February/March, and an unprecedented
rebound during April/May/June, then probably is that discount rates increased
during the early phase of the crisis, and then stabilized. This is a story that
somehow makes sense.
Two things are still strange (to me at least), though.
First, if stock prices are at their right levels now, why did they have to fall so much in March? OK, because risk aversion increased and funding conditions tightened. But, if risk aversion increased in February because investors understood that a recession was looming, why would investors turn less risk averse three weeks later, when economic forecasts just kept on deteriorating? Probably the Fed and the Treasury (via monetary policy and fiscal policy) eliminated the spike in risk aversion via aggressive policy interventions, but it is still not 100% clear how to reconcile these features of the data.
Second, the behavior of earnings has been surprising. During recessions, earnings normally contract much more than economic activity. So, when economic activity in 2020 contracts much more than in, as an example, 2008, a reasonable hypothesis is that earnings in 2020 would contract much more than in 2008. This has not happened.
To some extent, this graph summarizes both the explanation of the relatively mild response of the stock market to this recession and the remaining puzzle:
The figure shows how earnings dropped 68% from Q3 to Q4 2008 but seems to drop “only” 17% from Q1 to Q2 this year. This explains why the stock market has performed considerably better during this recession compared to its performance in 2008, for instance. On the other hand, the figure also shows that GDP dropped by 10% from Q1 to Q2 this year and “only” by 2% from Q3 to Q4 2008, i.e. the fall in economic activity is five times larger during this recession. It is puzzling that earnings drop so much less during this recession when economic activity contracts so much more.
So, the relatively modest contraction in earnings helps explaining the relatively fine performance of the stock market during this recession, but it is difficult to understand why earnings have behaved reasonably well. Perhaps this is because banks were suffering in 2008, but are able to help firms getting through this recession. Perhaps it is because firms have been able to adapt better this time. We do not really know. From an academic asset-pricing perspective, it is interesting that the stock market has behaved so differently this time around, compared to how it normally behaves. From an investor perspective, we can only hope that the stock market will keep on behaving in a different way than it usually does, as, otherwise, stock prices will fall going forward.
The facts that earnings have done better than in 2008 and that monetary policy has been very aggressive help us understand why the stock market has done well during this recession. Some puzzles still remain, though. For instance, why have earnings done so reasonably well, given the severity of the recession, and why did risk aversion increase so dramatically in February only to normalize few weeks later, in spite of the worsening recession. Perhaps the sensible answer is that this recession has been unusual in many dimensions, and some things will remain difficult to understand.
The behavior of the stock market during this recession has been perplexingly. To understand it, we need to answer three main questions: (i) why did stock markets fall so spectacularly during February/March, (ii) why did stocks rebound so spectacularly during April/May, and (iii) why is the stock market currently at its pre-crisis level? This blog post explains why these stylized facts are so difficult to understand jointly.In my next blog post (link), I will offer my interpretation of the evens.
the stock market during this recession has been a like watching a roller
coaster. The fall in late-February/early-March was the fastest bear market in history,
the rebound has been historically fast, too, and – in spite of the worst
recession ever – the stock market is currently back at pre-recession levels.
Somebody leaving for the moon in January and returning now would not be able to
see any trace in the stock market of the worst recession ever. This is in stark
contrast to normal recessions. People interested in financial markets and the
economy should be puzzled.
The problem – in a nutshell – is that if you claim that you understand why stocks are currently at pre-recession levels, then you face a challenge explaining why we needed to go through the fastest bear market ever during February/March. On the other hand, if you claim that the fast fall and rise in markets was just a short-lived technical blip (perhaps due to funding squeezes and fear), then you face a challenge explaining why that technical blip should result in a loss to the tune of USD 20 trillion (link). Finally, if you claim that you understand why markets fell so fast in February/March (perhaps because we were facing the worst recession ever – which, by the way, is a good explanation), then you face a challenge explaining why markets recovered so strongly in April/May/June, as the recovery cannot be explained by a brighter economic outlook. In fact, we have seen continuous downgrades of the economic outlook. E.g., the IMF WEO in June lowered its April forecasts for global economic output to –4.9%, from –3.0%. A huge downgrade. Strange that stocks recover in spite of this.
In this post, I describe the facts. I also review a couple of suggested, but wrong, explanations. In my next post (that I will publish next week), I will offer my view on what has been going on.
Here is an
updated version of a graph that I have presented earlier. It shows the Danish,
US, emerging market, and world stock markets (MSCI), normalized to one on
January 1, 2020:
Stock markets reached a temporary peak on February 19, after which they tanked dramatically. As mentioned here (link), it was the fastest US bear market in history.
has been equally spectacular. The US stock market is already back at its
January 1 level. World and emerging markets are still a few percentages behind,
but, basically, they have also recovered from the crisis.
In the US,
it was the fastest bear market ever, as mentioned. In this graph, I document that
it has also been one of the fastest recoveries. I calculate rolling 11-week
gains/losses in the SP500 during the last 50 years.
As you can see from the encircled peak on the right-hand-side of the graph, from its bottom on March 23, the SP500 gained app. 45% until June 8. The graphs shows that no other 11-week period since 1970 has witnessed such a large gain (I cherry-pick the 11-week period, but the main message is that the rebound has been spectacular, and this message is robust).
You hear/read many explanations. Some of them are just not correct.
It all boils down to FAANG
Some claim that it is not the overall stock market (typically these people refer to the SP500) that has recovered. Instead, some argue, it is all due to FAANG (Facebook, Apple, Amazon, Netflix, and Alphabet’s Google). It is true that the behavior of FAANG has been impressive and has driven a significant part of the SP500’s recovery, but this is not the whole story. As you see from the first graph above, emerging markets have recovered, too. This is obviously not due to FAANG. Also, some markets, such as the Danish, have recovered even more spectacularly than the US market. Not FAANG either. Even US small-cap stock have recovered. Not fully, but significantly. The Wilshire US Small-Cap Index gained an astonishing 57% during the March 23 through June 6 period. True, small-cap stocks are still (early August) 12% below their January 1 level, but when small-cap stocks have rebounded spectacularly, too, it emphasizes that this is not all about all FAANG, even when FAANG stocks have done very well.
Markets also recovered after the 2008 financial crisis
This is a no-brainer: Markets always recover. The point here is that this rebound has just been amazingly strong. In this graph, I compare the behavior of the SP500 during autumn 2008 and the corona crisis. “0” in the figure is September 19, 2008, respectively February 21, 2020:
During the first couple of weeks of this spring’s crash, it looked like autumn 2008. The pain was short-lived, though. Stock markets fell for a couple of weeks and then rebounded. In 2008, stocks just continued falling.
It has taken a couple of months to recover this time around. After the
financial crisis of 2008, it took more than two years. This figure shows that
the US stock market was back at its summer-2008 level in early 2011 only, and the
world stock market later still.
This recession is not as bad as 2008
It should be obvious by now that this argument is very wrong indeed. I
have argued myself that there is hope that, when we get a vaccine and economic
activity picks up again, the economic rebound will be stronger than in 2008,
the reason being that we entered this recession with a more “balanced” economy
than in 2008 (in 2008, we had a housing bubble, a weak financial sector, etc.).
Nevertheless, the short-run loss this time around is just so much larger than
Last week, we got figures for GDP in the US and Europe. This graph
compares the cumulative falls in real GDP in the Eurozone and the US in Q3+Q4
2008 with the cumulative drops during the first two quarters of this year.
US real GDP fell by more than three percent in Q3 and Q4 2008, whereas
Eurozone real GDP dropped by five percent in autumn 2008. It was a severe
recession in 2008. Nevertheless, it pales compared to this recession. This time
around, US GDP has fallen by more than ten percent already and Eurozone GDP is
down by a staggering 15% during the first two quarters of 2020.
What about the length of the recovery? After 2008, it took around two years
before economic activity had reached its pre-crisis level. This figure shows
for, e.g., the Eurozone that it will last more or less equally long this time (expectations
for 2020 and 2021 are from the EU Commission, Spring 2020).
The figure shows that Eurozone GDP was 5% lower in Q4 2008 compared to Q2 2008 whereas it is 15% down in Q2 2020 compared with Q4 2019. After eight quarters, GDP is basically back at its pre-crisis level. This means that the recovery is expected to be stronger, but this recession is just very deep.
In other words, this recession is much deeper – one can multiply the drop in 2008 by a factor of three or so to get the drop this time – and it will take several years before economic activity is back at pre-crisis levels. This means that the short-run cost to society is bigger this time around (a deeper fall stretched over the same period).
And, in spite of this very strong recession, stocks are doing fine. That is perplexing.
Above, I have presented stories that cannot explain what has been going on. In my next post (link), I will offer my view on this.
The Council for Return Expectations published
its forecasts of expected returns last week. There is a wide dispersion across
asset classes. Returns on “safe” assets (government bonds) are expected to be very
low, even negative, on the short horizon, whereas emerging markets equities are
expected to return close to ten percent per year. The Council also publishes
forecasts for standard deviations, correlations, and inflation.
As one of my external activities, I chair the Council for Return Expectations (link). The Council estimates expected returns, standard deviations, and correlations on ten broad asset classes. The estimates are widely used in the Danish financial sector and public discussions. Danish pension funds use the estimates when they calculate pension projections for their customers and banks use them when they make projections for how outside-pension savings can be expected to develop. Newspapers also write about them, too.
I will argue that the Danish financial sector has been a front-runner in designing a way to make such independent return assumptions. I hope the set-up helps improving the credibility of the projections banks and pension funds make.
blog post, I present the latest estimates from the Council. As it is the first
time I present these estimates on this blog, I start out describing why the
Council was established, how the Council works, and the procedure we use in the
Council to find expected returns. Afterwards, I present the forecasts.
Background: The Council for Return Expectations
was established (under a slightly different name) two years ago. The background
was as follows.
Danes have large pension savings. Together with the Netherlands, Denmark has the largest pension savings in the world relative to GDP (We have a paper describing some of the key features of the Danish pension system here; link). Historically, pension savings have been guaranteed, meaning that pension holders were guaranteed a minimum average annual return on their pension savings. Because of low interest rates, longer life-expectancies, etc., Danish pension funds have shifted into so-called market-based pension products during the last decade or so. In these products, there is no minimum guaranteed return. This allows the pension funds to invest more freely, hopefully enabling them to secure higher risk-adjusted returns. Basically, you go from a constrained to an unconstrained (or at least less constrained) maximization problem, which should lead to a better outcome. However, when a pension holder is not guaranteed a minimum return, the expected pension payouts during retirement will obviously become more uncertain. The assumed expected returns and risks on the different assets that pension funds invest in thus become even more important (compared to a guaranteed pension product) for an individual’s expected pension payments during retirement.
A couple of
years ago, I was approached by the Danish pension industry and asked whether I
would help them design a set-up that could be used to generate expected returns
on pension funds’ investments. The result was the following: based on inputs
from international financial institutions, present expected returns over the
next ten years on ten different asset classes, as well as long-run (> 10
years) expectations on two assets classes (stocks and bonds), and update these
forecasts annually. The pension industry judged that an independent committee
should specify and regularly update the return expectations, in order to secure
arms-length. They asked me if I would chair this committee. The Committee is
now called the Council for Return Expectations
Last year, banks in Denmark asked whether we (The Council) would be able to expand the set of return assumptions. The background was – probably fair to say – a scandal in the largest bank in Denmark. Danske Bank had advised some of its customers to invest in an investment product that the bank itself expected would yield a lower return than a bank deposit. I.e., the bank had advised its customers to invest in a suboptimal product, given expected returns. It led to the resignation of the interim Danske Bank CEO (link). The Danish banking sector concluded that arms-length was needed in determining return expectations used for investments outside pension savings. They asked the Council if we could include return expectations for a shorter horizon (1-5 years, in addition to the 1-10 years horizon) and a more frequent update of the return assumptions (twice a year, instead of annually). These forecasts were published last week.
Procedure for determining expected returns
forecasts for nominal returns on ten broad asset classes. Having done extensive
research on the determinants of expected returns myself, I know that even
within a precisely defined asset class, forecasters can disagree substantially on
the outlook for the asset class. When designing the set-up, I thus suggested
that return expectations should be based on an arms-length principle and take
estimation uncertainty into account. We do this by basing our expectations on
inputs from several international investment houses.
Council) receive inputs from Blackrock, J.P. Morgan, Mercer, and State Street.
We are truly thankful for their help.
J.P. Morgan, Mercer, and State Street provide their estimates of expected
returns, standard deviations, and correlations on each of the ten asset classes
for each of the different horizons to the Council. The ten asset classes for
which we provide expected returns are:
Government and Mortgage Bonds
Emerging market sovereign bonds
Global equity (developed markets)
Emerging markets equity
nominal. Estimates are for the arithmetic average annual return per year during
the different horizons.
hedged into euros, i.e. are euro returns, except from emerging market equities
and local currency emerging market debt that are unhedged (emerging market debt
is 50%/50% local/hard currency). The Danish kroner is pegged to the euro, i.e. euro
returns are basically also Danish Kroner returns. Returns are before fees,
expect from the last four asset classes (private equity, Infrastructure, real
estate, and hedge funds) that are after fees to the funds that manage these
types of investments, but before the fees to the Danish pension/mutual fund
that in turn invests in the private equity funds, hedge funds, etc.
addition, we provide forecasts for Danish inflation based on inputs from a
number of Danish forecasters (the Danish central bank, ministry of finance,
The Council consists of three people: Torben M. Andersen (professor of economics, University of Aarhus; link), Peter Engberg Jensen (former CEO of Nykredit and current chairman of Financial Stabilitet; link ), and myself as Chairman. Our job in the Council is to specify the relevant asset classes and their characteristics, choose methods used to weight the inputs together, come up with reasonable forecasts, communicate these to the public, etc.
Forecasts of expected returns
The following table presents our forecasts of expected annual returns for the next five years, the next ten years, and years six through ten:
next five years, we expect “safe” investments (government bonds and Danish
mortgage bonds) to return a negative 0.3% per year on a pre-fees, pre-taxes, and
pre-inflation basis. This is a low return. On a net-of-fees, after-tax, and
real-terms basis, it is even lower. In the Danish media, much was written about
this number (-0.3%). For finance professors and professionals, the number is no
big surprise given low interest rates, but for ordinary investors, the
publication of numbers such as these from trustworthy sources helps communicating
the message that it is difficult to earn a decent return these days without
taking on risk.
On a more technical term, the return on this asset class is the return to a bond portfolio consisting of 50% Danish mortgage bonds (that are triple-A rated, as you probably know), 20% Danish government bonds, and 30% Euro government bonds, with a duration of five years.
There is a
wide dispersion across the different asset classes. For instance, we expect
emerging market equity to yield 9.5% per year. There is thus an almost
ten-percentage point difference between the expected return on the safest asset
class and the asset class yielding the highest expected return.
that we generally expect higher returns after five years. We expect higher
interest rates, causing capital losses and thus low returns during the first five
years, but then higher returns later on. This in itself helps raising expected
returns on other asset classes.
Council, we want to stress that estimates of expected returns are surrounded by
uncertainty. To the finance professionals, this is obvious (though, one might sometimes
have the impression that even professionals tend to forget this), but to the
ordinary investor, this is even more important to emphasize. The ordinary
investor might otherwise conclude that when expected returns on emerging
markets is 9.5% per annum, but government bonds are expected to yield a
negative 0.3% per annum, I better put all my money in emerging market equity.
We want to stress that this is a risky strategy. This ambition has guided us when
it comes to our estimation of standard deviations.
expected returns we present are constructed as simple unweighted averages of
the expected returns of Blackrock, J.P. Morgan, Mercer, and State Street,
asset-class by asset-class. They also provide us with their expected standard
deviations. We do not use the simple averages of these as our estimates of
expected returns, though. Instead, we regress the standard deviations we receive
from the investment houses on the expected returns received from the same
investment houses. The standard deviations the Council presents are then the
fitted values from this regression. The main objective we achieve by following
this procedure is that we make sure that there is a clear relation between risks
The standard deviations surrounding the estimates of expected returns are here:
We expect a 3.5% standard deviation of government and mortgage bonds (over the next ten years) whereas we expect a 29.6% standard deviation surrounding the estimated returns to emerging markets equity. There is thus a clear relation between expected risks and returns. This clear relation also appears from this graph that plots risks on the ten asset classes against their returns (1-10 years horizon):
say that the price we pay from estimating standard deviations in this way is
that some of the individual estimates of standard deviations might differ slightly
from market consensus. As an example, our estimate of the standard deviation of
global equity is around 14%. Market consensus probably is that this is a little
higher, at 16%-17%. On the other hand, the advantage we obtain from proceeding
in this way, as mentioned, is that we secure a clear relation between risk and
We also present correlations between expected returns. Here they are for the first ten years:
are simple averages of the correlations we receive from Blackrock, J.P. Morgan,
Mercer, and State Street, asset-class by asset-class. The main thing to notice probably
is that we expect all correlations to be positive. This is bad news for
investors, one might argue, but we saw this during the March turmoil. In March,
bonds and stocks both lost in value, i.e. bonds did not hedge the risk of
stocks. Our correlations reflect this.
correlations and standard deviations are used by the pension funds to calculate
confidence intervals surrounding the individual’s expected pension income. I want
to stress that it is advanced – and good! – that pension holders in Denmark not
only learn about their expected pension income but also the uncertainty
surrounding this expectation.
We expect the Danish rate of inflation to be 1.2% over the next five years and 1.4% over the next ten years.
All the numbers
All the numbers – means, standard deviations, correlations, for all the horizons, etc. – are available on the webpage of the Council (link). On this webpage, addition information about the Council can be found as well.
We live in low-interest times. Investors might be tempted to invest more risky in order to generate a decent return. But what are decent returns? And how much extra risk do investors incur when investing more risky? In most countries, you get one set of answers when you visit one pension fund/bank/financial advisor, but another set of answers when you visit a different pension fund/bank/financial advisor. This makes investors uncertain and do not help building trust between the financial sector and investors. The financial sector in Denmark has found a cool way of presenting independent/arms-length forecasts. I hope that other countries might be inspired from this way of addressing this important issue.
PS. In my last post (link), I wrote about a new paper I have written on the financial stability in the Nordics. The journal where the paper is published is now available (link). It contains my own article and articles on the banking union, bail-in debts, household debts, and macroprudential policy, as well as a number of enlightening discussions.
In 2008, banks were too fragile given the risks
on and off their balance sheets. Many banks failed, others were rescued by
governments/taxpayers. The societal costs were enormous. In a new publication,
I evaluate the robustness of the Nordic financial sector today. I conclude that
the Nordic financial sector is more robust than in 2008. This is important
because we are currently going through a severe recession. If banks today were as
weak as in 2008, this recession would have been even worse.
Once a year, Nordregio publishes the Nordic Economic Policy Review. The theme of this year’s publication is “Financial Regulation and Macroeconomic Stability in the Nordics”. Because of my experience from analyzing the financial crisis in Denmark in 2008 (I chaired the government-appointed committee that investigated the causes and consequences of the financial crisis in Denmark, the Rangvid-committee, link), I was asked to write the first paper in the publication (link), assessing the robustness of the Nordic financial sector today.
It is a policy paper. No fancy equations and regressions, but straight-to-the-point analyses and conclusions. The Review was published Tuesday, June 16. At its launch, I participated in a panel debate with, among others, two former deputy-governors at Riksbanken (the Swedish central bank), a former governor at Riksbanken, and myself. In this post, I review my main conclusions and relate them to this crisis.
An ironic incident I start my paper emphasizing that it is difficult too foresee financial crises.
example, the first-page headline – typed in large bold letters – in the 2008
Financial Stability Report of Nationalbanken (the Danish central bank) was: ‘Robust Financial Sector in Denmark’. The
Financial Stability Report analyzed the robustness of the Danish financial
sector. It was published in May 2008, i.e. only a few months prior to the
outbreak of the worst financial crisis since the 1930s. Half of Danish banks
disappeared following the financial crisis of 2008.
this headline probably still haunts Nationalbanken, Nationalbanken was not
alone in not foreseeing the financial crisis. On the contrary. Financial crises
are almost per definition unpredictable. If it was generally accepted that a
financial crisis was in the making, action would surely be taken to prevent it.
In this sense, we tend to become surprised each time.
version of my analysis was written in autumn last year. The final version was submitted
in January this year. Right before the corona crisis.
I had not
seen the corona crisis coming. In particular, I had not seen how severe it
would be. Clearly, I was not alone either. Financial markets, for instance, had
not seen it, and financial markets summarize the average views of all investors.
Stock markets, credit spreads, stress indicators, etc. did not react before the
crisis was in fact happening. So, was my assessment of the financial sector in
January – that I present below – “robust” in light of the events that have
happened? I will return to this in the end.
Why an important question?
financial crises, and more generally stress in financial systems, are so hard
to predict, why do we bother? There is one main reason: The societal costs
associated with financial crises are enormous.
paper, I present new calculations of the societal losses resulting from financial
crises in the Nordics. I do as follows. Societal losses are calculated as
forgone economic activity due to financial crises. And this, forgone economic
activity, I calculate by projecting economic activity, from the start of a
financial crisis, by the growth rate of economic activity. The result is a path
of economic activity in the hypothetical event that there had been no crisis. I
contrast this with actual economic activity during the crisis. The difference
is the cost of the financial crisis.
In the paper, I present results from such calculations for Denmark, Finland, Norway, and Sweden for the crises in the early 1990s and the crises in 2008. Let me present just one of the calculations here. This figure shows the result for Denmark for the 2008 crisis:
line shows developments in real per capita GDP (in USD to allow for a
comparison across the Nordics) whereas the red line shows hypothetical GDP
assuming no crisis. Before the crisis, real GDP per capita was developing
steadily around the growth trend. The crisis changed this dramatically. GDP
fell 5% during the crisis, itself a huge drop in GDP. On top of this, however,
the recovery was slow. In 2018, ten years after the crisis, GDP had not
recovered to the level that could have prevailed had there been no crisis. In
fact, ten years after the crisis, the accumulated difference between hypothetical
no-crisis GDP and actual GDP amounts to 91% of GDP in 2008. One year of GDP was
lost due to the crisis.
these calculations for the other Nordic countries and other crises. The editors
of the publication, Lars Calmfors and Peter Englund, in their introduction,
summarize the results as follows: “Losses accumulate to a staggering one or two
years of economic output”. This is why it is important to deal with financial
paper, I describe the underlying causes behind the crises in the Nordics, as
well as similarities and differences across countries. I skip this here. Read
the paper to get it.
As the next
step, I venture into a kind of risk assessment.
on financial crises concludes that even when it is difficult to foresee crises,
some variables tend to be more informative about risks to financial stability
than others are. I mainly discuss credit growth, house price growth, and
household leverage. I conclude that lights are not flashing red.
growth is the indicator to which most attention is typically paid. For
instance, credit growth is the most important indicator when the
counter-cyclical capital buffer is determined in the new Basle capital-regulation
regime. Credit growth has been low during recent years.
prices and household leverage are other important indicators. In the Nordics,
they have been growing for almost three decades. House prices are currently high,
as are levels of household debt.
At the same
time, it is difficult to use an indicator that has been increasing for almost
three decades as a predictor of the timing of a turnaround. The literature
typically uses a three-year window for house-price growth when predicting
crises. House-price growth has not been particularly strong during the recent
couple of years.
prices and household debt levels are high today. But twenty years ago, they
were higher than they were thirty years ago. And, ten years ago, they were
higher than they were twenty years ago. Today, they are higher than they were
ten years ago. It is very difficult to say when the level is “too high”, in
particular when interest rates have been fallen throughout the last thirty
years, too (I return to this below). Had there been an explosion in house
prices during the last couple of years, like prior to the 2008 crisis, it would
have been a different matter. But this is not the case.
that house prices and levels of household debt are high but their developments
are not particular useful to predict the timing of a crisis, and thus not
supporting a conclusion that “a crisis is around the corner”
Robustness of the financial sector
indicators of risks to financial stability are not flashing red in the Nordics,
but my introductory point was that crises tend to arise in unforeseen ways. The
financial system should thus be robust to withstand unforeseen shocks. Prior to
the 2008 financial crisis, the financial system was clearly not resilient
enough. Banks “too large to fall” had to be rescued by governments and
taxpayers. As a consequence, increasing the resilience of the financial system has
been high on the agenda since the financial crisis.
liquidity requirements have been tightened, stress tests are conducted more
systematically now, assuming even more severe stresses than assumed in stress
tests conducted before 2008, restructuring and resolution regime have been
introduced, macroprudential instruments employed, etc. I conclude that the
financial system in the Nordics is more resilient today. This graph is just one
way of illustrating this:
The graph shows
capital ratios of selected large Nordic banks before (2006) and after (2018) the
financial crisis. Prior to the financial crisis, large banks financed around
8-10% of their risk-weighted assets with equity. Today, the ratio is between
18-20%. In other words, capitalization of banks has been basically doubled
since the financial crisis.
Causes for concern
Does all this
(no lights flashing red and a more resilient financial sector) mean that there
is no cause for concern? That would be going too far.
concerned by the current low-interest rate environment. Or, rather, I am
concerned about the consequences if interest rates at some point start rising.
interest rates are very low in an historical content. I use this graph to
shows monetary policy rates in the Nordics since the early 1990s. Rates have been
constantly falling, reaching negative territory in Denmark and Sweden (Sweden
now back at zero again).
mentioned above, house prices and debt levels have been increasing for app. 30
years in the Nordics, most likely driven by the fall in interest rates. Higher
house prices and levels of debt might be justifiable when interest rates are
low, but should interest rates rise, they might not be sustainable any more.
Furthermore, asset prices are even more sensitive to the interest rate when
interest rates are low. This means that it takes less of an interest rate
increase to cause drops in assets prices (such as house prices) at low levels
of the interest rate. Interest-rate risk is important to monitor, in my opinion.
other hand, if interest rates stay low, this might squeeze bank profits, if
banks are unwilling to pass on low or negative interest rates to customers.
This seems to be the case. In spite of negative policy rates since 2012 in
Denmark, it was only in 2019 that Danish banks started charging negative
deposit rates towards retail customers, and still only on large deposits
(typically above EUR 100,000). An incomplete pass through of negative rates
hurts bank profits, squeezing their resilience.
the paper, I discuss briefly the possibility that the next crisis might arise
in nontraditional banking areas, such as the emergence of credit extension by
non-regulated financial institutions or cyber risks.
The publication (Nordic Economic Policy Review) also includes great comments from two discussants of my paper: Anneli Tuomenin, CEO of the Finnish Financial Supervisory Authority and Peter Englund, Professor at the Stockholm School of Economics. Both largely agree with my assessment that the Nordic financial system is more robust today. Anneli is, though, perhaps even more worried than I am about the current low interest rate environment and outlines an additional number of non-traditional risks, such as climate risk and cyberrisk. Peter discusses whether banks really are that much better capitalized today (has capitalization gone up or risk weights down?) as well as additional risks associated with low interest rates. Read their comments. The main point is, I believe, that all of us share the view that we are particularly concerned about risks associated with low levels of interest rates.
publication also contains additional interesting articles on the banking union,
on macro prudential regulation, on monetary policy and household debt, on
bail-in instruments, and other interesting topics. If you are interested in
financial stability, the publication should be a must-read.
Lessons in light of the corona crisis
As mentioned, I wrote this paper in January 2020, right before the outbreak of the corona crisis. In the paper, I conclude as mentioned that the financial sector is more resilient today than in 2008. Now, we are in the midst of a recession that will cause a considerably larger fall in economic activity (on the short run) than during the financial crisis in autumn 2008. In autumn 2008, banks went belly-up and many had to be rescued. What has happened this time around? No bank has gone bankrupt, no banks have been saved, spreads are up on financial markets but not as much as in autumn 2008 (they were briefly in March, but came down quickly again). In autumn 2008, banks cut lending dramatically. Today, banks are helping customers getting through the crisis. I think this is important to emphasize, as you can still find people who argue that banks are not much more robust than they were in 2008. I judge that they are.
I am not saying that all banks will survive this, and I am not saying that we will not face any threats to financial stability. But I am saying that, given the fact that this recession is even deeper (on the short run) than the 2008 recession, and the fact that we are not witnessing the problems we did in 2008, this indicates – at least until now – that the financial system is more resilient today. Of course, should the situation turn to the worse, e.g. with a new wave of the virus, things will look different.
The paper was presented at a webinar on June 16, 2020. The presentation was followed by a panel discussion on “Will the corona crisis also trigger a financial crisis?”. Panelists included Karolina Ekholm, Stockholm University and former deputy-governor at Riksbanken, Lars Heikensten, the Nobel Foundation and former governor of Riksbanken, Kerstin Hessius, CEO of the Third Swedish National Pension Fund and former deputy-governor at Riksbanken, and myself. The panel was moderated by Peter Englund. It was an interesting debate.
In many European countries, lock-downs have been comprehensive. Now that we have more data, it is time to ask the question whether all types of interventions are equally effective. New research indicates that restricting large gatherings and public events is effective. Restricting international and internal travel is not.
Given the fact that the number of new cases continues to fall in Europe, even when more and more is opened up (link), I cannot help wondering whether all types of lock-downs are equally effective. It must so that some measures are more effective than others are. And, if some measures are not effective, they should be applied with greater caution if we will face a second wave of the Covid-19 virus.
It is understandable that lock-downs were comprehensive early on, as data and studies on the effectiveness of different non-pharmaceutical interventions were lacking. Now we have more data. A new study is interesting in this regard.
Nikos Askitas, Konstantinos Tatsiramos, and Bertrand Verheyden (link) use data from 135 countries to study the effectiveness of different types of non-pharmaceutical interventions. By studying such comprehensive data, Askitas et al. are able to exploit variation in the timing, type, and effectiveness of interventions within and across countries in order to identify the effects of different types of interventions. The data also allow the authors to control for the contemporaneous influence of multiple non-pharmaceutical interventions, i.e. they can do their best to isolate the effect of each type of intervention.
Askitas et al. classify interventions into eight different categories: (i) international travel controls, (ii) public transport closures, (iii) cancelation of public events, (iv) restrictions on private gatherings, (v) school closures, (vi) workplace closures, (vii) stay-at-home requirements, and (viii) internal mobility restrictions (across cities and regions). Their main results are here:
The figure reveals how the number of daily new infections develop prior
and subsequent to the introduction of different types of interventions. The
figure shows that the most effective interventions are cancellations of public
events and restrictions on gatherings. For instance, 35 days after the
introduction of interventions that cancel public events, the number of new
infections has fallen by 25%, compared to the number of new infections at the
event time, i.e. at the time of introducing the intervention, on average across
the 135 countries in the study. The figure also shows that the effect becomes significant
only after app. 20 days, i.e. it takes time before the interventions work.
On the other hand, international travel controls, public transport
closure, and restrictions on internal (within country) movements seem to have basically
no effect on the number of new cases. In fact, these types of interventions do
not seem to have any statistically significant effect on the number of new
School closures seem to have some effect, although it is barely
Other authors have studied the effectiveness of non-pharmaceutical interventions, even when these studies do not look as carefully as the Askitas et al. study on the effectiveness of different types of interventions. Robert Barro (link), for instance, studies data from the 1918 Spanish flu, exploiting variation in the intensity and duration of interventions across US state. Barro finds only little effect of interventions. Barro also reports, however, that this no-effect result is most likely due to the short duration of the interventions implemented during the Spanish flu. Most of the interventions back then had a duration of around one month. The figure above shows that interventions only start being really effective after one month or so. In this light, it is perhaps not surprising that Barro finds only little effect of non-pharmaceutical interventions.
Other studies, for instance link and link, also report a positive effect of non-pharmaceutical interventions but do not estimate as clearly the effect of different types of interventions.
So, the conclusion, at least to me, is that:
can be effective, in particular if their duration is not too short.
Some interventions are
more useful than others are.
restrictions of large gatherings and public events seem to be rather effective.
travel and public transportation does not seem to be effective.
Hopefully, results like these will be read carefully by politicians. If we will face a second wave of Covid-19, activities that do not influence how the virus spreads should not be shut down. On the other hand, we should be fast in implementing interventions that are effective, and their duration should not be too short. In particular, there is no need to uncritically just shut down “everything”. It is perhaps understandable that this was the approach taken in many European countries in March when evidence on the effectiveness of different types of interventions was missing. Today, we know more. Research indicates that it pays off to restrict gatherings of large groups of people, while restricting international travel and public transportation does not seem to pay off.