Categories
Financial markets Stock markets

Expected returns

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.

In this 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

The Council 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

We provide 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.

We (The Council) receive inputs from Blackrock, J.P. Morgan, Mercer, and State Street. We are truly thankful for their help.

Blackrock, 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
  • Investment-grade bonds
  • High-yield bonds
  • Emerging market sovereign bonds
  • Global equity (developed markets)
  • Emerging markets equity
  • Private equity
  • Infrastructure
  • Real estate
  • Hedge funds

The horizons are:

  • 1-5 years.
  • 6-10 years.
  • 1-10 years.

Returns are nominal. Estimates are for the arithmetic average annual return per year during the different horizons.

Returns are 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.

In addition, we provide forecasts for Danish inflation based on inputs from a number of Danish forecasters (the Danish central bank, ministry of finance, etc.).

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:

Source: Council for Return Expectations.

Over the 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.

Notice also 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.

Standard deviations

In the 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.

The 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 and returns.

The standard deviations surrounding the estimates of expected returns are here:

Source: Council for Return Expectations.

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):

Source: Council for Return Expectations.

One might 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 returns.

Correlations

We also present correlations between expected returns. Here they are for the first ten years:

Source: Council for Return Expectations

Correlations 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.

The 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.

Inflation

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.

Conclusions

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.

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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.

Categories
Corona crisis Danish economy Financial stability

How stable is the Nordic financial sector?

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.

As an 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.

Even if 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.

The first 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?

If 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.

In the 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:

GDP and hypothetical non-crisis GDP for Denmark.
Data source : St. Louis FRED database.

The blue 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.

I present 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 crises.

In the 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.

Risks assessment

As the next step, I venture into a kind of risk assessment.

Literature 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.

Credit 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.

House 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.

So, house 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.

I conclude 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

Traditional 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.

Capital and 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:

Capital ratios of selected large Nordic banks, before and after the 2008 financial crisis.
Data source: Eikon.

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.

I am concerned by the current low-interest rate environment. Or, rather, I am concerned about the consequences if interest rates at some point start rising.

Today, interest rates are very low in an historical content. I use this graph to illustrate:

Monetary policy rates in the Nordics.
Data source: Datastream via Eikon.

The figure 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).

As 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.

On the 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.

Finally, in 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.

Discussion

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.

The 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.

Webinar

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.

Categories
Corona crisis

Interventions that pay off. And, interventions that do not

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:

Source : Askitas, Tatsiramos, and Verheyden (2020)

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 infections.

School closures seem to have some effect, although it is barely significant.

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:

  1. Non-pharmaceutical interventions can be effective, in particular if their duration is not too short.
  2. Some interventions are more useful than others are.
  3. In particular, restrictions of large gatherings and public events seem to be rather effective.
  4. Restricting international 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.

Categories
Corona crisis Danish economy

The price of a lockdown

Has the lockdown been too strict and have we paid too high a price (in terms of forgone economic activity) as a result? Evidence from Denmark and Sweden helps answering this important question.

The fact that the number of deaths associated with Covid-19 falls in many European countries is unbelievably good news. As a result, European economies are opening up again. This is also good news. What is perhaps somewhat surprising, though, is that the number of deaths and new cases has not started climbing after opening up. This raises the question whether too many sectors were shut down and we, consequently, paid too high a price to contain the virus. Evidence from Denmark and Sweden suggests that the price was probably not as high as one might intuitively have expected.

Covid-19 in Denmark

The Danish prime minister announced the lockdown of Denmark on March 11. Death numbers kept climbing until early April, but has fallen steadily since then, as this figure shows

Number of new deaths in Denmark. Rolling 5-days average. Vertical lines indicate April 15 (primary schools opened), April 20 (dentists etc. opened), May 11 (retail shops opened), and May 18 (restaurants opened).
Data source: Statens Serum Institute. https://en.ssi.dk/

The fact that Covid-19 seems to be under control in Denmark is obviously very good news.

The lockdown has been costly, though. Jobs have been lost, firms have closed down, incomes have fallen, etc. Public debt levels will increase significantly.

Denmark started opening up again in mid-April. On April 14, private corporations that had been asked to encourage people to work from home were now encouraged to let workers return to their offices, subject to suitable safety measures. On April 15, kindergartens and lower primary schools opened up, and on April 20, hairdressers, dentists, etc. were allowed to open. If the lockdown helped contain the virus, one would have expected to see a rise in deaths and new cases after easing the lockdown. This has not happened.

On May 11, retail shops opened. On May 18, restaurants. Again, if the lockdown was the primary reason why the virus stopped spreading, we should have seen the number of new cases increase after opening up. This we have (luckily) not.

Given that the number of deaths and new cases has been constantly falling since opening up, one may wonder whether the lockdown was too strict. It seems that voluntary social distancing, avoiding large crowds, and the washing of hands have been perhaps even more important. Doctors and scientists wonder whether the virus has mutated and turned less infectious, or warmer weather in April and May has caused a slowdown in new cases, but if I understand what they are saying, they believe that hand washing (and related precautions) is a main reason why numbers fall.

If this is true, we need to ask the question whether it was necessary to keep children and workers at home and close down as much as we did. Whether the economic costs have been too high. This is a sensitive and difficult question, though, because it requires that we evaluate how much economic activity would have fallen had we not shut down as much as we did. Surely, people would have cut consumption anyhow, as people would have been afraid of the virus itself and its consequences. So, what was the extra price we paid because of the lockdown?

Evidence from a clever study

A clever study (Link) by University of Copenhagen researchers Asger Andersen, Emil Hansen, Niels Johannesen, and Adam Sheridan helps us answering this question.

Asger, Emil, Niels, and Adam compare spending by Danes and Swedes. Denmark and Sweden are similar along many dimensions, but have followed different strategies when it comes to Covid-19. Denmark enforced a strict lockdown, as mentioned, whereas Sweden has kept primary schools, restaurants, bars, etc. open (though did close universities and encouraged working from home). If the lockdown was the main cause of the recession, economic activity should have suffered more in Denmark than in Sweden. Andersen et al. use individual bank-account data from a large Danish bank that operates in both Denmark and Sweden (Danske Bank) to address this question. They track spending of 860,000 individuals before and during the lockdown, studying their use of cards, cash withdrawals, mobile wallets, etc. They compare spending of Danes and Sweden from mid-March (when Denmark shut down) through early April. This figure contains their most important result:

Data source: Andersen, Hansen, Johannesen, and Sheridan (2020).

The figure shows that spending of Danes fell 29% compared to a hypothetical spending path without the Covid-19 pandemic. A 29% contraction is enormous. But, and this is the main point, spending by Swedes fell by almost as much, 25%. This means that Danes cut spending by “only” 4%-points more than Swedes. Swedes could continue visiting restaurants, schools, etc., but Swedes cut spending dramatically nevertheless. The causal effect on spending, resulting from the extra lockdown in Denmark, is 4%-points and considerably smaller than the effect of the virus itself.

One may debate whether 4% is a small or a large number. In normal times, we would say that a 4% drop in aggregate spending is large. The main cause of this recession, at least according to this study, was not the lockdown, though, but the virus. In other words, even if economies had not been shut down as much as they were, the contraction in economic activity would still have been very large.

The broader picture

The paper by Andersen et al. weighs in on an important debate about the underlying cause of this recession. Is it due to a reduction in supply or demand? Their results indicate that the demand effect is the important one. Spending drops even when supply is less constrained.

But the strict lockdown in Denmark did cause an extra 4%-point drop in spending. Was it worth it? Nobody knows, but research from the MIT (Link) on the 1918 pandemic indicates that areas in the US that took more drastic measures early on, in terms of social-distancing measures and other public health interventions, had stronger recoveries after the measures were eased. There is debate how strong this result is, though (Link).

If all this is true, i.e. that the lockdown was not the main cause of the recession, but the main cause was the virus itself and the fear it created, and that the recovery is stronger afterwards in countries that imposed stronger interventions, we should see forward-looking measures indicating this. Early data from the Danish and Swedish stock market seem to point in this direction, even if the evidence is admittedly very tentative and a fuller analysis is obviously needed. This figure shows the Danish and the Swedish stock markets surrounding the date of the lockdown.

Danish (OMX Copenhagen 25) and Swedish (OMX Stockholm 30) stock market indices. Vertical line indicates March 12, when Denmark was shut down.
Data source: www.nasdaqomxnordic.com

I have normalized the two stock-market indices to “1” on March 12, the day after the lockdown was announced in Denmark. The Danish and the Swedish stock markets followed more or less similar paths during March, but, since then, the Danish stock market has gained 29% whereas the Swedish market has gained “only” 20%. Again, one needs a more detailed analysis (the Danish stock market is heavily influenced by pharmaceuticals and other defensive stocks that do relatively well in downturns), but this early evidence indicates that stock market investors are more positive on the Danish stock market.

Furthermore, the number of deaths is currently much lower in Denmark than in Sweden (also on a per capita basis), which is of course also something to remember:

Denmark and Sweden vs. other countries

The idea of using credit-card and other payment-type information is great and has been used by a number of economists to obtain real-time information on the economic impact of Covid-19. Microdata indicate that consumer spending in France fell by 50% as a result of the virus (Link), in Spain by 50% (Link), and in the US by something like 50%, too (Link). It thus seems that the contraction in spending is somewhat smaller in Denmark and Sweden compared to other countries. The comments above should be viewed in this light.

In any case, a 30% or 50% drop in spending is a huge drop. But, a 50% drop in spending does not mean that overall economic activity drops by 50%. Overall economic activity includes public spending, exports/imports, etc. So, credit card transactions provide useful information about consumer spending, but cannot stand alone when judging overall economic activity.

Conclusion

At some stage, we need to evaluate carefully whether too much was shut down. Not to blame somebody, but to learn. It is important that we analyze thoroughly whether it is better to be very aggressive early on in a pandemic or whether less aggressive measures are enough. Currently, it seems that we shut down a little too much (and we are now too slow in opening up e.g. borders again), but that the price of doing so was not as high as one might have expected intuitively.

Categories
Corona crisis Oil Stock markets

Zero-probability events that happened

Some of the events we have seen during this crisis are so unlikely that we need tricks to calculate their probabilities. This is a journey into the world of very small probabilities and very large numbers. I admit it is somewhat surreal. But it is important as these small-probability events can have long-lasting effects on economic activity.

The defining characteristic of this recession is the speed with which markets and economies started freefalling. From one day to the next, economies were shut down. Economic activity came to a complete sudden stop in some sectors. In different blogposts, I have used words such as “unprecedented”, “completely crazy”, “horrifying”, “tremendous”, etc. to describe what happened.

Using words like these is adequate, I think (I admit I was shocked by what happened), but I have come to a point where I want to be more precise. As economists, we want to put numbers on things. I want to calculate the probability of the events. This turned out to be more complicated than I had first imagined.

The probability of a 121-sigma event

On Monday, April 20, the oil price (West Texas Intermediate) closed at USD -38 for a barrel of oil. I describe this here (link). This was a drop of 306% relative to the closing price the previous trading day. Based on daily data since 1983, the average percentage change in the oil price is 0.03% and the standard deviation 2.5%. I called the event “completely crazy”, which I still believe is a fair description. Now, I want to know “how crazy” it was. What was the probability that it would happen?

The happy-go-lucky Professor of Finance, i.e. me, opens his Excel spreadsheet and enters the command used to find the probability that an event of -306 happens when data are normally distributed with mean 0.03 and standard deviation 2.5.

I press “Enter”. Excel returns “0”. The Professor of Finance thinks this look strange and increases the number of decimal places. Excel just shows “0.000….”. According to Excel, this is a zero-probability event. It could not happen.

But it did happen. I start remembering the statistic classes many years ago, and recall the discussions on low-probability events.

Luckily, I have helpful colleagues. I write CBS Professor of Statistics Søren Feodor Nielsen. He tells me that the probability I am looking for is below the “machine precision”. He also tells me that the probability itself might be extreme, but the logarithm to the probability is probably not. We calculate the log-probability (using a different statistical package than Excel). It is -7495.137, i.e. the likelihood is exp(-7495.137). This is, unfortunately, such a low number that a computer cannot calculate this either.

We then calculate the log base 10 probability. Now we have the result. The probability is 0.799 x 10^(-3255).

This is a zero followed by 3255 zeroes, and then 799. Practically zero, but not exactly zero. The event happened, but it is extremely unlikely. We can call it “completely crazy”, but we can also say that the probability is 0.799 x 10^(-3255).

There is another way to illustrate how unlikely this event was. When the probability is 0.799 x 10^(-3255), we should see this event happen every 1.2516 x 10^(3255) days. I.e., a number with 3255 numbers before the decimal place. I simply do not know what such a number is called (link). This is where it starts getting surreal.

How precise is this? We have calculated it, but Søren tells me we should not put too much faith in its precision. Tail-probabilities in the normal distribution are calculated by numerical approximations, and it is questionable how precise they are when we are that far out in tail. Admittedly, the question is of course also how important it is that it is precise that far out in the tail. The probability is unbelievably small. That is the main thing. Exactly how small is perhaps not that important.

S&P 500

This graph shows daily percentage changes in the S&P 500 throughout the last 50 years.

S&P 500. Daily data. May 1970 – May 2020.
Data source: Thomson Reuter Datastream via Eikon.

On March 13, the S&P 500 fell 12%. The average daily percentage change in the S&P 500 (calculated up until March 12) is 0.03% and the standard deviation is 1.05%.

The likelihood that we will see a 12% drop in the S&P 500 on a daily basis, given the behavior of the S&P 500 during the last 50 years, is very small, but not so small that it cannot be calculated in Excel. Excel says it is 1.0827 x 10^(-29).

These are daily data. If we assume 250 trading days per year, we should see this event happen every 3.69 x  10^(27) = 3,694,454,429,465,560,000,000,000,000 year. I.e., every 3.694 octillion year.

This is also somewhat surreal.

There are many caveats. We are again so far out in the tail that we should not put too much emphasis on the exact number of decimals and the exact number after all the zeroes. Also, all these calculations are based on the assumption of normally distributed data. Given that we saw a 21% fall in the S&P 500 on Black Monday, October 19, 1987, we have seen two very unlikely crashes within the last three decades. According to these calculations, they should happen much more seldom. We use the normal distribution, but data might follow a different distribution. For most practical purposes, however, it is not necessary to know the exact distribution of these extreme events. The important thing to know is that the likelihood is very small.

Other numbers

In this post (link), I describe how the initial jobless claims soared, confidence indicators fell, GDP dropped, etc. As an example, the likelihood that we should see almost 7 million people filing for jobless claims on March 28, given the complete previous history of jobless weekly claims, is 0.97 x 10^(-841). This is again such a small probability that Excel cannot even calculate it.

Economic effects

There is a broader point to these discussions. Events like those described above might have long-lasting consequences for economic activity.

When events are unlikely, we assign low probability to their occurrence. One hypothesis is that we keep on assigning low probabilities to their occurrence. In this case, future investment decisions of firms and future consumption decisions of households are unaltered by the events we have been going through. It was a temporary shock. It has very low probability. It will not influence how we form expectations going forward.

An alternative hypothesis is that we have become so scared by the event that it will haunt us for years to come. We update our beliefs disproportionally much.

Which of the two scenarios play out is important for the recovery from this recession. Will we start consuming when economies open up or will we hold back because we have become scared?

Kozlowski, Veldkamp and Venkateswaran (2020) have an important paper on this (link). Their main point is that events like those studied above, i.e. events that are very unlikely but have large impacts, will have persistent effects on beliefs. They write that “tail events trigger larger belief revisions” and that “because it will take many more observations of non-tail events to convince someone that the tail event really is unlikely, changes in tail beliefs are particularly persistent”. This is an important insight. If this is how we form expectations, it will slow down the recovery from this recession. Kozlowski et al. also show, however, how government interventions can reduce the effect, but not eliminate it.

Conclusion

These weeks we are seeing infection rates declining in many countries and economies opening up again. These are very good news. Let us hope we can start returning to some kind of normality. It will take time before we are back to where we came from, though. Some things might even have changed for good. One thing is for sure: we will not get back as fast as economies and markets fell in March. What happened was very unlikely. But it did happen.

Categories
Corona crisis Stock markets

Is the stock market too optimistic? Or, is the market always right?

The rebound in stock markets has been spectacular. One may wonder whether it is sustainable.

What does it actually mean that the stock market is “too optimistic”? Doesn’t the stock price always reflect the average expectation of all investors? True, and, in this sense, the market cannot be “too optimistic”. On the one hand, the market is always right.

On the other hand, the legendary economist Paul Samuelson famously noticed that the “stock market has predicted nine of the last five recessions”. Stock markets also appear “excessively volatile”, as Shiller showed already in 1981. I.e., the stock market might always be right, but perhaps we are going through a period of excessive volatility where markets were overly pessimistic in March and are overly optimistic now.

In any case, if the stock market is always right, so is the futures market. Investors in the futures market expect huge drops in dividends over the next year. If stock prices behave as they usually do during recessions, stock prices should drop even more, that is dropping even more than dividends.

Market developments

Before we start this attempt to explain things and look ahead, let us review the facts. This graph shows developments in the Danish, the US, the world (MSCI All countries), and the emerging stock markets since the start of the year, all MSCI indices (Danish index is in Danish kroner, the rest in USD):

Stock markets since January 1, 2020. MSCI indices.
Data source: Theomson Reuter Datastream via Eikon.

Stock markets did well in the beginning of the year, fell dramatically from mid-February through mid-March, and have rebounded spectacularly since then. Today, the Danish stock market is above its January 1 value, the US market is 10% down, the world market is 15% down, and emerging markets are 20% down.

What could explain current stock prices?

Stock prices are by definition discounted future cash-flows. That is easy to say. The difficult part is to find the expected future cash-flows and the appropriate discount rate.

Let us start with a positive view on markets and see what is needed to support this.

To begin with, let us keep the discount rate fixed. Let us also, as a starter, assume that valuation ratios are constant. For instance, let us assume that stock prices closely follow developments in real economic activity. Under these assumptions, we would, largely, be able to understand recent market movements. These assumptions would also support a rosy view of the future. Afterwards, we discuss if this is a likely scenario.

Forecasts for economic activity in light of the corona crisis start coming in. Late April, we got the WEO from the IMF and forecasts for the US from the CBO, and this week we got forecasts for Europe from the EU-commission. This figure shows the expected path of real GDP in the US and EU:

Expected developments in EU and US real GDP. The CBO does not provide quarterly growth rates for 2021, so I assume linear growth during 2021, accumulating to the CBO forecast of 2.8% for 2021 .
Data source: EU commission and CBO.

This recession is enormous. GDP is expected to fall by 15% in Q2 this year, compared to late 2019. Luckily, economies are expected to rebound sharply after this quarter, too. The EU commission expects a strong recovery in the EU in 2021, with 6.1% growth in 2021. The CBO expects 2.8% growth in 2021 in the US. Whether this difference is realistic, I do not want to discuss here.

The economy drops by 15% in Q2. If stock prices follow GDP, as we assume for now, stock prices should have dropped by 15% in Q2. This is almost spot-on.

This theory would also predict that we have a great year in front of us. Economic activity should improve by something like 15-20% until late 2021. If stock prices follow, stock prices should also increase with something like 15-20% from here.

What about the huge drop in March? This we can also explain, I think. Basically, it had little to do with underlying economic fundamentals. Some leveraged hedge funds got squeezed, they received margin calls, they dumped everything to raise cash, there was panic, spreads widened, and markets feared a replay of 2008 (I comment on it here and John Cochrane has a nice and more detailed explanation here). Stress on markets almost reached 2008 levels, as, for instance, the St. Louis Fed Financial Stress index indicates:

St Louis Fed Financial Stress Index.
Data source: St. Louis Fed.

Central banks intervened and provided liquidity. Markets calmed down. The stress index is still somewhat elevated, but much lower than in March. Markets started looking at economic fundamentals again. With lots of liquidity around, and low yields, investors bought stocks. This was a temporary crash.

The intermediate conclusion is that if stock prices follow GDP, stock prices should drop by something like 15% during Q2, only to rebound afterwards. And, as of today, stock prices have in fact fallen by something like 15% since the start of the year, with a bumper on the road in March. If we stop here, everything would be fine and we should expect substantial stock market gains going forward. Under these assumptions, markets are expected to rebound by something like 15-20% until late 2021.

Cash-flows do not always follow economic activity

Unfortunately, we cannot stop here. We need to go back to the definition of stock prices: Discounted cash-flows. I truly believe that cash-flows relate to economic activity in the long run (I have research demonstrating this), but I also believe that there are temporary business-cycle deviations between cash-flows and economic activity. And these deviations can be substantial.

We have expectations to economic activity from IMF, CBO, EU, and so on, but how do we find expectations to cash-flows? Niels Joachim Gormsen from the University of Chicago, a smart former Ph.D. student at Copenhagen Business School, has, together with Chicago Professor Ralph Koijen, developed a method that can be used to back out expected changes in dividends from dividend futures. They also estimate a relation between dividend growth and GDP growth, such that they can back out expected GDP growth, too.

Their latest estimates are from April 20. Compared to January 1, 2020, Gormsen and Koijen estimate that US GDP will be 3.8% lower over the next year (precisely, Niels tells me, they compare expected growth from April 1, 2020 through April 1, 2021, to expected growth from January 1, 2020 through January 1, 2021). This is not far from what the CBO expects (the figure above shows that GDP will be something like 5% lower ultimo 2020 compared to primo 2020).

The scary thing is that Gormsen and Koijen show that markets expect dividends to be 18% lower over the next 12 months, i.e. drop by 13%-points more than GDP. An 18% drop in dividends is a very large drop in historical terms.

This graph shows how investors update their expectations to future dividend growth rates during the corona crisis:

Data source: Gormsen and Koijen (2020).

For the EU, Gormsen and Koijen expect GDP, respectively dividends, to drop by 6.3%, respectively 28%. The market is always right, right?

Unfortunately, we cannot stop here either.

Stock prices react excessively to cash-flows during recessions

We need to talk about the last part of the definition of stock prices: discount rates. Research shows that discount rates move counter-cyclically, increasing in bad times and dropping in good. From our definition of stock prices, this means that stock prices should fall by more than dividends in bad times, if discount rates increase in bad times.

Tim Kroencke from the Univeristy of Neuchatel has an interesting paper that studies how stock prices move in relation to dividends during recessions. The central graph in the paper is this one:

Data source: Kroencke (2020)

The graphs shows how dividends (and earnings) fall during recessions and how stock prices fall even more (there are two stock price indices. Read Tim’s paper to get the explanation for the difference. The point is that stock prices fall more than dividends.)

The figure shows that US dividends drop by 10% and US stock prices drop by ten percentage-points more, i.e. 20%, on average during US recessions. It also shows that stock prices do not drop ahead of the drop in dividends, but alongside. This is not good news.

Gormsen and Koijen’s estimates say that we should expect dividends to drop by 18% for the US and 28% for the EU over the next year. Kroencke says that on average stock prices drop by 10%-points more. This means that stock prices should drop by close to 30% in the US. If we use the 10% drop in the price-dividend ratio for EU data, EU stock prices should drop by close to 40%. Today, stock prices are down 15% globally.

Conclusion

We might hope that this recession plays out in a different way than recessions usually do. We might also hope that stock markets react in a different way than they usually do. We might hope that everybody starts spending when economies open up, companies start producing, and earnings and dividends do not suffer. In this case, the drop in dividends might not be that large. And, we might hope that stock markets do not fall by more than dividends, in contrast to what they normally do. This could for instance be because central banks are marginal investors in many markets now, driving asset prices away from what they would have been if prices were solely determined by private market participants. This is what markets hope. And, of course, this might turn out to be the case.

We might also fear, however, that companies will start facing problems given the severity of the crisis, i.e. this turns from a liquidity crisis to a solvency crisis. Should this happen, earnings and dividends will drop. The market is always right. The futures market expects dividends to drop by something like 20% for the US and 30% for Europe. If stock prices react as they normally do during recessions, stock prices should drop even more. The 15% drop since early 2020 seems small in this light.

———————————————————————————-

PS. Did you by the way notice the German Constitutional Court ruling this week? Given last week’s post on the Italian situation, I might just inform you that the Italian yield spread to Germany widened as a consequence. Not a lot – 15 basis points or so – but in the wrong direction.

Categories
Corona crisis Eurozone

Italy vs. Greece

There are similarities between Greece in 2010 and Italy today that make me nervous.

Do you remember the Greek drama in 2010-2012? Following the financial crisis in 2008-2009, Greece saw it public finances deteriorate, as did most other countries. The special thing about Greece was that they had fudged the numbers. In autumn 2009, the new Greek Prime Minister, George A. Papandreou revealed that the former government had lied about the deficit. This, and the recession, brought public debt to 146% of GDP in 2010. It later turned out that Greece had also falsified the numbers – via different swap contracts, willingly helped by US banks – prior to the introduction of the euro, making the whole thing even worse. With debt running at close to 150% of GDP in 2010, investors lost faith. Greek yields skyrocketed (see below). Greece received a number of rescue packages from the EU commission, IMF, and the ECB (the so-called “Troika”). Eventually, in 2012, Greece restructured its debt in the largest sovereign default in history. Investors faced a loss to the tune of EUR 100bn.

Why do I repeat this sad story? Because the IMF Fiscal Monitor Report contained a forecast for Italy that has, in my opinion, not received enough attention. The IMF predicts that Italy will reach a public debt level of 150% of GDP this year, exactly like Greece in 2010. Will this lead to a replay of the European debt drama?

First, the data. We are in the middle of a terrible recession. Italy has been severely affected by the corona virus, and has as a consequence enforced a strict lockdown. IMF expects that Italy will be one of the worst hit countries. Italian GDP is expected to fall by 9.1% during 2020.

IMF also expects that the fiscal deficit will be 8.3% of GDP in 2020. This is a large deficit, but other countries face even larger ones. The US, for instance, 15.4% of GDP (this probably requires another blog post…..), China 11.2%, Spain 9.5%, and so on.

The problem in Italy is that public debt levels are already very high, at 130% of GDP. Few countries match this. In fact, only Japan and Greece. What is noteworthy, in my opinion, is that the combination of the already high debt level, the severe recession, and the fiscal deficit will most likely take the debt level to more than 150% in 2020:

Greek and Italian public debt as a percentage of GDP. Data for 2020 and 2021 are forecasts from the IMF.
Data source: EU Open Data Portal and IMF Fiscal Monitor Report.

The figure superimposes a dotted line indicating the 146% debt-to-GDP level Greece reached in 2010, which sparked the Greek problems. Italy is expected to cross this line in 2020.

Contributing to my concern is the fact that financial markets seem to treat Italy and Greece somewhat similar this time around, in contrast to 2010. This time, movements in the Italian yield spread to Germany largely mirror movements in the Greek yield spread:

Greek and Italian yield spread towards Germany. Ten-year government bonds. Daily data: January 2, 2020 – April 30, 2020.
Data source: Thomos Reuter Datastream via Eikon.

The spread between Greek and German ten-year government bonds is a little higher than the spread between Italian and German government bonds, but movements are very similar. Only between the ECB/Christine Lagarde blunder on March 12 and the introduction of the PEPP (see below) on March 18, there was some divergence between Italian and Greek yields. Otherwise, they have moved in tandem.

This is different from 2010. In 2010, Greek yields spiraled out of control, reaching close to 50% on the worst days, but something like 25% for the worst month on average:

Greek and Italian yield spread towards Germany. Ten-year government bonds. Monthly data: 2000-2020.
Data source: St. Louis Fed.

The Italian spread also increased in 2010, but, back then, markets clearly distinguished between Greece and Italy. Today, markets view Italy and Greece as being in somewhat similar situations. Rating agencies have started to act (Link).

Will we see a repetition of the Greek drama?

The Greek yield spread was close to 50% on the worst days in 2010, and currently we are talking something like 2.5% for Italy, i.e. very far from 50%, though up from 1.5% in the beginning of the year. This is clearly not as bad as in 2010. I thus hope that we will not see a repetition of the Greek story in Italy, but I dare not rule it out.

Let us start with the good news. Yields are lower today than in 2010. In 2010, Italian yields were around 4%. Today, they are around 2%. Hence, if yields stay low, Italy can afford a larger public debt compared to the situation in 2010. Also, everybody is aware that problems in Italy will mean problems for Europe, as I describe below, i.e. everybody wants to avoid such a situation.

On the other hand, the situation could turn to the worse if the recession in Italy will be deeper than the IMF expects, if the Italian political situation takes another turn to the extreme, if political negotiations at the EU level about a recovering plan do not bear fruit, or if some other negative shock occurs. Should some of this happen, Italy might find itself in something like the Greek situation at some point. The situation is fragile, as shown by the widening of spreads and the downgrade of Italy.

Italy is important for the European project. It is a G7 country. It is one of the founders of the EU. The foundation of the euro would be threatened if Italy runs into trouble. It would have global repercussions. At the same time, Italy is probably too big to bail out for the rest of the Eurozone/EU.

Some say that the ECB will make sure that this will not happen. Perhaps, but this is not as obvious as some claim. We get into technical nitty-gritties here, unfortunately, but it is important to understand.

ECB is buying bonds like crazy at the moment, keeping a lid on yields, including Italian. The ECB’s PEPP (Pandemic emergency purchase programme) implies that the ECB will buy public and private securities to the tune of EUR 750bn during 2020. I.e., the PEPP will help keeping Italian yields down.

Furthermore, at the ECB Governing Council meeting Thursday, April 30, it was decided that “The Governing Council will conduct net asset purchases under the PEPP until it judges that the coronavirus crisis phase is over, but in any case until the end of this year” and “The Governing Council is fully prepared to increase the size of the PEPP and adjust its composition, by as much as necessary and for as long as needed”. Hence, the PEPP might last longer than throughout 2020 and accumulate to more than EUR 750 bn.

BUT, the important sentence in the PEPP programme is this one: “For the purchases of public sector securities under the PEPP, the benchmark allocation across jurisdictions will be the capital key of the national central banks. At the same time, purchases will be conducted in a flexible manner. This allows for fluctuations in the distribution of purchase flows over time, across asset classes and among jurisdictions.” (Link).

This point here is that that ECB cannot – and I repeat cannot – buy unlimited amounts of Italian debt at the moment. The proportion of Italian bonds the ECB can buy (as a proportion of all Eurozone sovereign debt ECB buys) is restricted by the Italian fraction of ECB capital. At the moment, this is, to be very precise, 13.9165%.

It also says that this will be interpreted in a “flexible manner” – and I am sure the ECB will be flexible – but they cannot be too flexible. ECB just cannot buy unlimited amounts of Italian debt under current programmes.

There is an escape clause: OMT (sorry for this soup of abbreviations; ECB, PEPP, OMT,…). OMT is “Outright Monetary Transactions”. OMT allows ECB to buy unlimited amounts of Italian (and other Eurozone) bonds, should this be necessary. But, and this is a big but, only when the relevant country has entered into a program with the European Stability Mechanism (ESM; one more abbreviation…). An ESM program means that the country will face demands with respect to its conduct of fiscal policy; think about the discussions about the role of the Troika in the Greek drama. To some extent, this is a loss of sovereignty. Entering into a program is also a signal that the country cannot handle the situation on its own. This will make it even more difficult to sell bonds on the market. At the moment, Italy very clearly opposes an ESM program. At the end of the day, this however means that ECB cannot buy unlimited amounts of Italian debt should this become necessary. ECB cannot save Italy.

Other people say “look at Japan”. Japan has a public debt of more than 200% of GDP. IMF expects it to reach 252% of GDP in 2020. Yields in Japan are very low, in spite of this massive debt mountain. I.e., Japan shows that you can accumulate lots of public debt without causing yields to rise, some claim. True, but remember that Japan has its own currency and its own central bank. The Japanese central bank can buy all the public debt they want, keeping Japanese yields down. Italy does not have its own central bank and its own currency. The ECB cannot buy unlimited amounts of Italian bonds. Greece is the example that shows that the situation of a Eurozone country is different from the situation of a country with its own currency and central bank.

(Here, I do not want to get into a discussion whether a country with its own currency and central bank faces no limits on the amounts of public debt it can raise, as argued by proponents of the “Modern Monetary Theory”.  Basically, I disagree with that policy implication and think there is a limit, but this is for another day).

So, in the end, if worst comes to worst, ECB cannot save Italy, unless Italy enters into an ESM program. Some kind of political solution at the EU level would be needed. I hope we will not get there. At the moment, we are not, but I dare not rule that we might get there. I will follow the Italian situation.

Categories
Corona crisis Stock markets

The stock market and more horrifying figures

A couple of weeks ago, I collected some graphs revealing how deep this recession is (Link). Two weeks have passed. New data have arrived. They are still horrifying.

We now have aggregate numbers for Europe. In the first quarter of 2020, Eurozone GDP dropped more than it did during the worst quarter of the financial crisis of 2008:

Quarterly growth rate of Eurozone real GDP.
Data source: ECB.

GDP figures are aggregate data. They show how total economic activity has developed. That’s why they are important. They are, however, also a snapshot of the level of economic activity, collected with a lag. The latest figures deal with the first quarter of 2020. So what about asking firms and households how they feel and how confident they are? If people and firms think this will be a short-lived recession, they should be confident. The EU commission publishes confidence indices for firms in the industrial sector, the services sector, and for households in the EU. Confidence indices drop, as we have never seen before.

Monthly changes in confidence indices.
Data source: EU commission.

The industry, the service sector, and households all have no confidence in the economy.

Next, I collect trailing averages of initial jobless claims in the US during the preceding six weeks. During the past six weeks, more than thirty million Americans have lost their jobs. It is not just a historical record. It is very sad.

Initial jobless claims in the US. Rolling six-week averages.
Data source: St. Louis Fed.

Most likely the unemployment rate will come in at close to 20%.

Behind all these numbers and figures, there are real firms and real people. Firms will be forced to close. Individuals will lose their jobs. Households will struggle.

So, how does the stock market do? Given the scale of the recession, you would expect it to suffer tremendously. It does, however, great.

From its temporary trough on March 23, the SP 500 has gained 32%. It tanked heavily in late February and early March, but it is today (May 1) “only” 13% below its peak on February 19. I write “only” as the stock market often losses something like 50% in a recession (Link). This recession is unprecedented, as shown above, so you would expect unprecedented losses on the stock market. This is not what we see.

It is not unusual that the stock market starts rising before the end of the recession . And the stock market did fall very fast early in the recession. In the US, it was the fastest bear market ever (Link). But given the depths of the recession, stock markets have rebounded surprisingly fast.

True, not all stock markets have rebounded. The Spanish stock market (MSCI Spain) is 30% below its peak on February 19. So is the Italian. And, the Argentine is down 31%.

So, some stock markets have rebounded strongly, but others have not. Let us try to get an overall picture.

This is a global recession. The aggregate value of global stocks markets is USD 44 trillion on April 29 (FTSE All World Index). On February 19, it was USD 52 trillion. At the bottom on March 23, it was USD 35 trillion.

World stock market capitalization. FT All World Index. USD bn.
Data source: Thomson Reuter Datastream via Eikon

This means that 20,000 billion USD(!) was lost on the stock market from February 19 through March 23, globally. It also means, however, that almost 10,000 billion USD have been gained during the last month or so.

Given the severity of this recession, it is – frankly speaking – somewhat surprising that global stock markets have not lost more than something like 15% since mid-February. Stock markets seem disconnected from the real economy. Why?

Of course, stock markets are forward looking and all that. And, yes, luckily, it seems that the corona virus is on retreat. These are very good news and give some hope. But, given the depth of the recession, I think it will leave scars. It will take time before unemployment in the US reaches 4% again, as an example.

The reason why stock markets in many countries have done great during the last month, when economy activity has suffered like never before, is actually rather simple. Central banks exercised the “central bank put”.

Central banks were every fast to enact even very large rescue packages. They started doing so in mid-March. This helped stabilize financial markets, depress yields, and flooded markets with liquidity. The goal, of course, was to support the real economy. It saved stock markets, too. Shortly after the announcement of all these comprehensive measures, the stock market started rising.

With lots of liquidity around, and low yields everywhere, markets convinced themselves that central banks would save the world. Markets bought stocks, and stock markets surged.

This is what I believe explains it. Otherwise, I cannot make sense of the fast rebound in the stock market. But I do believe markets have thereby become somewhat disconnected from the real economy. And this is strange to witness.

Categories
Corona crisis Danish economy

Live appearances

I have participated in a number of live appearances during the last couple of weeks, most of them in Danish, though.

I gave a webinar (in English) on the economic impact of the corona virus: Link.

I participated in a program (in Danish) on the consequences of the crisis on real estate prices: Link. I am on at 2:20, 14:05, 18:40, 21:20, 24:45. This is also described here: Link.

I participated in a program (in Danish) on the consequences of the crisis on savings: Link. I am on at 2.20, 6.30, 17.40, 26.30. It was a popular program. Around a third of Danes watching TV that evening tuned in.

I participated in a radio program (in Danish) on the global economic outlook: Link.

Categories
Corona crisis Oil

A 121 x sigma event

What happened on the oil market this week was completely crazy.

I believe everybody noticed the headlines, but it is worthwhile to pause and reiterate, as this was another sign of the very unusual and severe recession we are going through: For the first time in history, oil prices were negative at minus USD 38 for a barrel of oil. I think this graph nicely illustrates how unusual this was.

Percentage daily changes in the WTI oil price.
Data source: Thomson Reuter Datastream via Eikon.

The graph shows daily changes in oil prices since 1983. Until April 16, the average daily price change is 0.03%. The standard deviation of daily price changes is 2.5%. On April 20, Monday, the price of oil fell by 306% compared to its price Friday. This is a 121-sigma fall. Almost surreal, but true.

The price of oil fell because demand for oil has tanked as a result of the recession. When economic activity falls, as it does right now, demand for oil falls. When we do not travel, when we do not drive our cars to work, when production facilities are closed down, and so on, we do not use oil. When supply of oil is not reduced, or at least not reduced as much as demand for oil falls, prices adjust.

If oil is produced but not used, it needs to be stored somewhere. Traders panicked on Monday, as they feared that there is simply not enough physical space to store oil. If you possess thousands of barrels of oil and have nowhere to store them, you are willing to pay somebody to take care of it. FT explains it nicely: “Analysts believe a lack of available storage capacity at the WTI contract’s delivery point of Cushing, Oklahoma — known as the Pipeline Crossroads of the World — set off panic among traders holding derivative contracts, who found themselves with nowhere to put the oil.”

The negative price of oil relates to West Texas Intermediate, the US benchmark oil contract. The price of Brent oil, the international benchmark, has been positive, even on Monday at USD 17 per barrel. This indicates that Monday’s event was more of an issue with storage of oil in the US, and the expiration of futures contracts, than a general issue with storage of oil. E.g., Brent oil can more easily be stored at sea, at least as long as tankers are available. One should also notice that the price of WTI was back in positive territory already Tuesday and stayed positive during the rest of the week. This was, even if very dramatic, a one-day shock.

Prices of both WTI and Brent have been falling since the outbreak of the virus in China in early 2020, however, particularly in early March when Europe and the US started shutting down. Except from the astonishing event on Monday, the price of both Brent and WTI have followed the same path. The agreement from April 10 limiting oil production has thus failed to slow the fall in oil prices.

Prices of WTI and Brent oil since January 1, 2020. Daily prices.
Data source: Thomson Reuter Datastream via Eikon.

The behavior of oil prices reflects the severity of this crisis. The drop in oil prices since the start of the year tells a different story about the recession than does the stock market. The stock market has sprinted ahead since mid-March, indicating that stock market investors believe the recession will soon be over (the stock market did fall on Monday and Tuesday, though, due to the turmoil on oil markets). Oil markets, in contrast, indicate that the recession is very much still ongoing. What signal do you believe – the one from the oil market or the one from the stock market?