Thursday, August 29, 2019

Stock market getting cheaper (relative to bonds)

Several indicators are signaling an increase in the probability of a recession. Most of these indicators are variables that have shown to be statistically leading the recession but they cannot always be seen as the cause of one (for example, an inverted yield curve)

In the search of a cause for a recession we typically look for imbalances. One that has mattered in the past is asset price bubbles. Standard valuation metrics of the stock market suggest that in the last quarters the market has gotten cheaper and moved further away from bubble territory. The Financial Times reports that US companies dividend yield is now larger than the interest rates on a 30 year government bond (see image below). This is not at all a new phenomenon in Europe where the dividend yield has been larger than the interest rate on bonds for years and is now reaching record levels.



A good way to summarize the improvement in the valuation of stocks is to calculate the ex-ante risk premium. The image below shows the risk premium for the US stock market (S&P 500) from 1991 until today (see sources and methodology at the end of this post).


The risk premium has climbed back up to levels (around 4%) that are average for the post-2008 period and significantly higher than in previous decades (high risk premium means that stocks look cheaper relative to bonds - assuming risk attitudes or perceptions are not changing).

What is more significant is that the risk premium is much larger than in the quarters prior to the previous two recessions. In the quarters prior to the 2001Q1 recession the risk premium was negative. In the quarters prior to the 2007Q4 recession the risk premium was falling and below 2% for several quarters. We are now at 4%, far away from any of those magnitudes.

Similar calculations for Europe produce an even "cheaper" stock market (relative to bonds). P/E ratios are as low as 19 for Germany. Combined with a -0.7% yield on a 10 year government bond and assuming inflation around 1.5% means a risk premium of 7.5%. Almost as high as during the panic years of the last crisis (2009) in the US.


Antonio Fatas

____

Methodology: under certain assumptions the expected ex-ante real return of the stock market can be approximated by the inverse of the P/E ratio. The risk premium can then be calculated as the difference between this magnitude and the real yield on a 10-year government bond. See Blanchard and Gagnon for alternative calculations of the risk premium.

Sources of data: P/E ratios and nominal 10-year interest rates from Robert Shiller; forecasts of inflation from the survey of professional forecasters posted at the Philadelphia Fed.


on August 29, 2019 |   Edit

Wednesday, July 3, 2019

Redefining money in a digital age

Economics textbooks use a definition of money as an asset that can be used as a means of payment, that constitutes a unit of account and serves as a store of value. This definition is being used often in debates about new forms of digital money and payments (including cryptocurrencies).

I would like to argue that this characterization of money is
a) not a definition
b) not a very useful one to compare alternative forms of payment
c) fails to understand how digital payments and new technologies have changed the nature of money

Let's deconstruct the "definition" to see what is wrong with it. 

Unit of account and store of value

Prices and wages are denominated in a unit of account that we identify with money or the currency.  This one is quite straightforward but we cannot rule out a world with multiple units of accounts. There are countries where we have several currencies being used in parallel which does not stop people from using different forms of money. At a much smaller scale:
- when you travel abroad, you use your debit card to do a purchase in a different unit of account
- Bitcoin based debit cards (such as bitpay) allow you to use Bitcoin to pay for goods and services priced in US dollars
- Libra (the new currency launched by Facebook and partners) is also likely to follow this model

Aren't all these examples forms of money? They are. Of course, different units of account add risk and volatility. The risks will depend on how the value of these assets correlate with the value of your other assets, your income,... Individuals will have to understand the trade offs between these risks and potential features that these new forms of money provide (more on this later).

In some sense this is linked to the idea of money as a store of value. All assets are a store value. When we say that money is a store of value we typically mean is a "stable store of value". And stability depends on its intended use. It is reasonable to argue that that an asset whose main function is to facilitate payments should be stable in value relative to the price of goods and services (and this can be guaranteed by a stable and low inflation rate). But we can have competition here as well. 

Asset that can be used as a means of payment

Here is where things get much more interesting. Paper money is an asset (owned by the person holding it) that can be used to buy goods and services by exchanging the physical piece of paper. No separation between the asset and payment.

But when payments are electronic, the connection between the asset (the value being exchanged) and the payment is not straightforward. Economic textbooks already struggle with this. They think of different assets according to how close they are to "cash", the most liquid asset. They already admit that it is a matter of degree (how liquid an asset is) and not a question on whether an asset is "money" or not. So they label a "checking account" as money because one can write checks (yes, checks!) or use debit/credit cards to access its value for payments. But there are plenty of other bank accounts that can easily be made liquid by transferring its balance to the checking account. Because of this lack of clarity, we produce a set of measures of money depending on how liquid those assets are (M1, M2, M3,...).  

So textbooks already acknowledge that there is no obvious definition of a liquid asset, that it is a matter of degree. But what new technologies have done for us is make that definition even blurrier. 

I would argue that to make sense of these new technologies, we need to deconstruct money and separate the asset, its value and possibly other characteristics of it from the payment technology. Something that was impossible with a bank note but that is required when it comes to electronic payments. 

A payment involves two (possibly distinct) elements:

1. A balance of value (an asset) that is being held in accounts that possibly have features that are unrelated to payments (one might collect an interest rate on the balance, the institution holding the balance offer deposit insurance, or guarantees against cybercrime, fraud, etc.).

2. The payment technology that access the value of the asset and transfers it to someone else. This technology requires an infrastructure that might require many pieces. For example:

    a) a network that connects the institutions holding the money
    b) a protocol, governance rules on how this network operates
    c) a device that allows users to access those accounts (a debit card, a cheque, a smartphone,...)
    d) a way for users to identify themselves in a way that is secure
    e) the ability to do anonymous transactions

Different payment technologies come with different features that might reduce the costs of using them (or their risks) and make them better forms of payment. All of these technologies could potentially be accessing the same asset, the same balance stored in the same institution. But they might not as well. It might be that accessing that balance is impossible for technical, regulatory or other reasons and then the payments technology comes with its own form of money. 

Here are some examples:

- M-Pesa redefined money and payments together by providing balances through mobile telephony operators and accessing those balances via telephones.

- Bitcoin also redefined money and payments together through the creation of a universal repository of money ("we all bank at the same bank") and a technology to execute payments on these assets

- Bitcoin debit cards (such as bitpay) allow you to use your Bitcoin balance to do purchases in any store even if prices are denominated in a different currency. Money (your Bitcoin balance) is separate from the payments technology

- Wechat, Paypal, Apple Cash and many others use a separate payment technology to access assets which might be located in your traditional depository institution, your bank. You can connect your bank account to the payment technology and separate the two (and you can also hold a balance within their own system if you prefer to do so). In some countries (Europe, Singapore,...) regulation is pushing for these type of solutions by forcing banks to open up their systems to external payment technologies.

What all these examples show is that the definition of money and payments has become much more fluid than in the past. Using the three traditional criteria to compare alternative forms of money is not very useful. What we need to do is deconstruct the properties of both money and the payment technologies to understand the features that make one solution better than others. Given current technology trends, it is likely that we end up with a continuum of technologies involving more than one unit of account, assets stored in different types of institutions (from mobile telephone companies to banks to large tech companies to cryptocurrencies), being accessed through a variety of payments technologies that involve different players (your smartphone manufacturer, your social media account, your bank, credit card companies).

Antonio Fatas



on July 03, 2019 |   Edit

Wednesday, June 19, 2019

Libra: not a currency board and (maybe) not a stable currency

Libra, the cryptocurrency backed by Facebook (and the other members of the Libra association) was announced yesterday. The web site and the white paper refer to the new currency as a stable currency:

"Libra is designed to be a currency where any user will know that the value of a Libra today will be close to its value tomorrow and in the future." 

The stability is guaranteed by the intrinsic value of the coin, a result of the assets that back the value of the currency. These assets are called the "Library Reserve".  The white paper refers to the similarities of this mechanism and the currency board that some currencies with fixed exchange rates use:

"...the mechanics of interfacing with our reserve make our approach very similar to the way in which currency boards (e.g., of Hong Kong) have operated. Whereas central banks can print money at their own discretion, currency boards typically only print local currency when there are sufficient foreign exchange assets to fully back a new minting of notes and coins."

This reference to currency boards is confusing and misleading. In fact, it is surprising that given the vast knowledge we have about how fixed exchange rates work, the white paper does not present a more precise description of how the value of Libra will be managed. It also confuses the fact that there are assets backing the currency with the notion of fixed exchange rates and currency boards. And it does so by playing to the myth that traditional fiat currencies are not backed by any assets.

Let me clarify each of these issues.

Assets = Liabilities

Fiat money is backed (one by one) by the value of the assets in the central bank balance sheet. Any central bank that issues a traditional fiat currency has assets which have a value that is identical to the liabilities it has issued (same as with Libra). This does NOT guarantee the stability of the currency. The stability comes from the commitment of the central bank to a certain monetary policy that ensures that the value of the currency remains stable relative to the value of goods and services (i.e. stable and low inflation).

Fixed Exchange Rates

Some central banks go beyond an inflation target and implement monetary policy by anchoring the value of their currency to another currency (that is seen as stable), what we call fixed exchange rates. Fixed exchange rates require:

a) an announcement by the central bank of a parity relative to another currency (or basket of currencies)
b) the commitment to intervene in foreign exchange markets to ensure that the value of the currency is what has been announced.

Simplest example is to announce a fixed price relative to another currency (say 1 to 1 to the US dollar) and then commit to sell or buy unlimited amounts of the local currency against US dollars at the pre-announced price. This ensure that the exchange rate stays fixed.

In the case of Libra, there is no such commitment (at least not yet). There is some loose statement that the value of the currency will remain stable relative to a basket of currencies but no details on whether an explicit commitment will be announced. If such a commitment does not exist then we are in the world of flexible exchange rates where credibility has to come from some sort of inflation target announcement that is delivered over time.

Currency Boards

When a central bank fixes the exchange rate and commits to intervene to defend the currency, there can be concerns on whether it will be able to do so if the currency is under attack. While all central banks have enough assets to buy back their liabilities, many of these assets are domestic assets. While in theory one can control the value of the currency though these domestic assets (and interest rates), having a large pool of foreign assets that a central bank can sell to intervene in the foreign exchange market is seen as an additional guarantee that the commitment to fixed exchange rates will be honored. This is what is known as a currency board. In its extreme form the central banks holds enough foreign assets to buy back its supply of local currency.

But this is not quite what the design of Libra promises. Unless the currency composition of its balance sheet happened to match the basket of currencies that is used to fix the exchange rate. But this would be an unusual and confusing system because as the currency composition changed the basket used as a reference for the fixed exchange rate would also change.

Libra: Not a currency board and not a fixed exchange rate (not yet)

In the absence of a proper fixed exchange rate and a credible mechanism to maintain it, Libra looks more like a standard flexible exchange rate currency. Its stability will depend on its credibility. Referring to the fact that there are enough assets backing its supply is not a good argument (that argument applies to any central bank issuing fiat money).

Antonio Fatás


on June 19, 2019 |   Edit

Thursday, June 6, 2019

This time might not be different

Estimating the probability of a recession over a short horizon has so far proven to be a challenging task for economists. Each cycle looks slightly different from the previous one and trying to come up with precise indicators of crises leads to either overpredicting them or missing their timing as some risks are underestimated. As the US enters its longest expansion ever, we are back to a discussion on whether there are any reliable indicators that can help us forecast the next turning point. 

Without providing an exhaustive list of all candidates, let me highlight the interaction between three statistical patterns and how they inform us (or not) about the risks ahead: 

Three (related) statistical patterns

1. The Yield Curve tends to invert before a recession.


2. The US does not seem to be able to sustain a low unemployment rate. Once we reach "full employment" (or even before), unemployment bounces back as we hit a turning point. I have written about this pattern in my previous post.



3. No US expansion has lasted more than 120 months. Using the NBER business cycle dates, we are about to enter the longest expansion since their data starts in 1857.

These three statistical patterns are related. As an expansion continues, we see both a gradual decrease in the unemployment rate and a flattening of the yield curve. This should not be a surprise, as unemployment declines central banks raise short-term rates. But what is interesting is that the US (so far) has not been able to reach a state where the yield curve remains flat for a long period of time or, equivalently, the unemployment rate stays low for a number of years. Both the slope of the yield curve and the unemployment follow clear V-shape paths. And this is likely to be linked to the length of the expansion: when the recovery starts both unemployment rates and the slope of the yield curve come down from high levels and as they reach their lowest possible levels, they bounce back setting a limit for how long expansions last. In the current expansion, and after 10 years, even if we started with a high unemployment level (as in 2009), we must be very close to full employment (and the yield curve is flat or inverted).

But aren't these just statistical patterns without an obvious causal argument? Correct, but the fact that this statistical pattern is to robust and consistent means that if the US were to continue its current expansion for a few more years it would have to be that "this time was different". 

Can this time be different?

Could it be that the risks or imbalances that led to previous recessions are either not present or just better managed today? Maybe. It is true that the stock market does not look as expensive as in the year prior to the 2001 recession. It is true that housing markets do not look as expensive as the year prior to the 2008 recession. But we need to remember that in those years we underestimated the relevance of those risks. In 2007 US Federal Reserve Officials praised the resilience of the US financial system to a possible fall in housing prices. Are we failing to see other relevant risks today?

And let's not forget that, even ex-post, some recessions are not clearly preceded by excessive imbalances. For example, the 1990 recession. That recession seems to be more an accumulation of smaller risks combined with geopolitical events (such as the invasion of Kuwait by Iraq). And while some of these geopolitical events are difficult to predict, it is not hard to produce a list of the potential threats the world faces today (from Brexit, to the trade conflicts initiated by the US administration, to the potential instability of the Euro area,...).

In summary, statistical patterns suggest that a recession is imminent. Can this time be different because large imbalances are not present? Maybe. But let's not forget the previous times when we did not see the size and implications of the ongoing imbalances. And let's not ignore the long list of potential risks that could materialize and produce a global slowdown that could easily tilt the US and possibly other countries into a recession. This time might not be different.

Antonio Fatas

on June 06, 2019 |   Edit

Tuesday, March 12, 2019

The 2020 (US) Recession

Summary: This post is based on a research note I wrote asking whether low unemployment is sustainable. The answer is a clear no for the US. Low level of unemployment are good predictors of the tail risk event of a recession, a sharp increase in unemployment rates. These dynamics are related to the build up of financial and macroeconomic imbalances. If this pattern is to repeated, and given the current level of unemployment rate, a US recession must be around the corner. For details on the analysis, the research note including additional results is available on my web site: Fatas (2019).

A few months away from the longest US expansion

The US economy is a few months short of beating the longest expansion ever, which took place from March 1991 to March 2001. As we approach this milestone, there are increasing concerns about the possibility of a recession in the coming years.

Do expansions die of old age? The empirical evidence suggests that this is not the case. There is no clear correlation between the length of an expansion and the probability of a recession (Rudebusch (2016)).

An alternative definition of age

There is an alternative way of thinking about the age of an expansion, in terms of how much economic slack there is left. Expansions are periods where the economy is returning to full employment. As unemployment becomes low and reaches levels around or below the natural rate of unemployment, is it possible to maintain this state for a number of years? Or does “full employment” automatically lead to imbalances that represent the seed of the next crisis?

In the case of the US, history suggests that “full employment” is not a sustainable state and that once we reach such a level a sudden increase in unemployment is very likely. In the figure below I plot unemployment rates around the peak of each of the last five cycles (where zero represents the month the recession started). I plot 5 years before the recession started and 10 months after the recession.

Unemployment Rate Around Recessions (US)
All cycles display a V-shape evolution for unemployment. Unemployment reaches its lowest point around 12 months before the recession and, in most cases, unemployment is already increasing in the months preceding the recession. What is interesting is the absence of a single episode of stable low unemployment (or full employment). It seems as if reaching a low level of unemployment always leads to dynamics that soon generate a recession. Recessions die of old age if “age” is measured in terms of how much economic slack is left. If this pattern was to be repeated, the US must be today very close to an inflection point, a recession.

The result might sound obvious and mechanical: once unemployment rate is low, there is only one way for unemployment to go: up. This is true but what matters is whether a persistent period of low and stable unemployment is possible. In the case of the US the answer is no. One way to visualize this possibility is to look at other countries. A good example is Australia that has recently sustained a low unemployment rate for decades. After a recession in the early 1990s, unemployment increased and then started a decline through a path similar to any US expansions. By the year 2000 unemployment reached a low level that has remained mostly flat for years. In other words, the unemployment rate does not display V-shape dynamics but looks more like an open L-shape.

Unemployment Rate (Australia)

Growth at risk and quantile regressions

We can quantify this intuition by relating this result to an academic literature that analyzes the determinants of the tail risk of unemployment (or GDP) changes. This literature looks at the determinants of worst potential outcomes over a specific time window. Some examples: Cecchetti (2008), Kiley (2018) Adrian, Boyarchenko, and Giannone (Forthcoming). 

Empirically this is done with the use of quantile regressions. In this case we are interested in the tail risk of sharp unemployment increases, which are associated with recessions, and I will capture that by coefficient on the 90th percentile of the distribution in a quantile regression (Fatas (2019)).

The results of such a regression are displayed in the table below. All three coefficients are negative (which is what one would expect as there is reversion to the mean in unemployment rates). But the interesting part is that the size of the coefficient increases as we move from small changes in unemployment to large changes (from q10 to q90). This means that low unemployment rates are particularly good at predicting the tail risk of large increases in unemployment (recessions)

Interestingly, the same phenomenon is not present in other countries (such as Australia). See Fatas (2019) for those results.

Why is full employment unsustainable?

The pattern of US unemployment recessions suggests that low levels of unemployment are a strong predictor of sudden increases in unemployment, associated to crises. We do not observe in the data any sustained periods of low unemployment. But why is low unemployment unsustainable? What leads to a recession? 

The academic literature tends to emphasize two set of variables: those associated to macroeconomic imbalances (such as inflation) and those associated to financial imbalances. Interestingly, the introduction of these variables in the quantile regressions above makes the above effect go away (see Fatas (2019)). In particular, once we control for credit growth, it is not any longer the case that low unemployment is a good predictor of the tail risk associated to recessions (we still observe a reversion to the mean but we do not obtain a larger coefficient for the p90 quantile). 

This result suggests that recessions follow periods of low unemployment because imbalances are built during those years. What is interesting is that the evidence shows that this is always the case, that the US economy has never managed to sustain a low rate of unemployment without generating the imbalances that lead to a recession. If history is an indicator of future crisis, and given the current low level of unemployment, a recession is likely to be around the corner. 

References

Adrian, Tobias, Nina Boyarchenko, and Domenico Giannone, Forthcoming, Vulnerable Growth, American Economic Review.

Cecchetti, Stephen G., 2008, Measuring the macroeconomic risks posed by asset price booms, Asset prices and monetary policy (University of Chicago Press).

Fatas, Antonio, 2019, Is Full Employment Sustainable?, Manuscript.

Kiley, Michael T., 2018, Unemployment Risk. Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (US).

Rudebusch, Glenn D., 2016, Will the Economic Recovery Die of Old Age? Federal Reserve Bank of San Francisco Economic Letters.




on March 12, 2019 |   Edit

Monday, December 17, 2018

How low is low for Chinese GDP growth?

The deceleration in the Chinese economy over the last decade has raised concerns about the sustainability of the Chinese economic "miracle". But is that deceleration unusual when compared to other countries? What is to be expected in the coming years?

Economists like to look at emerging markets through the lens of the convergence model (based on the work of Robert Solow). Successful emerging economies are supposed to grow faster than advanced economies and catch up. But as the process of catching up materializes, growth will slow down and over time will approach that of the most advanced economies. How does China compares to other successful emerging economies? It is not easy to find a perfect historical example for China but South Korea comes the closest. It is a successful converging economy in Asia and it is a fairly large economy (unlike Singapore or Hong Kong, two other successful converging economies).

We start by focusing on the period 1980-2018 and use GDP per hour as an indicator of productivity. For each year we compare the initial level of GDP per hour with the growth of GDP per hour over the 5 years that followed. The initial level of GDP per hour is measured relative to the US (as an example of a country close to the technology frontier).



In the context of this period, the deceleration of China makes its growth rate land right at the sample place as the growth rates that Korea had at similar levels of development. The last observation corresponds to the period 2013-2018. Today (2018), China is abut 20% of the US level and if it were to follow the Korean benchmark it would be growing at rates around 6%, very close to current Chinese growth rates. China reached this position after a volatile early decades. Possibly underperforming in the 1980s and over performing in the decade of 2000s when growth passed 10%. 

If we add early decades the comparison becomes much noisier as both South Korea and China had much more volatile, and lower overall, growth rates.



In summary, the deceleration of GDP growth rates in China can be seen as a natural evolution of the economy as it follows its convergence path, in particular if we use recent decades in South Korea as a benchmark. Let's not forget that South Korea is one of the best performer for countries in the range below 50% of the US GDP per capita. So using South Korea as a benchmark we might be providing an optimistic benchmark for Chinese growth.

Antonio Fatás

[Data Source: Total Economy Database, The Conference Board]
on December 17, 2018 |   Edit

Tuesday, November 27, 2018

Global Rebalancing

Prior to the Global Financial Crisis the world economy experienced a period of increasing global imbalances where a group of countries saw their surpluses increase rapidly while, on the other side, a group of countries increased their deficits. These patterns were partly related to the "saving glut" hypothesis put forward by Ben Bernanke to explain the decline in global long-term real interest rates. It was also the case that some of the deficit countries (in particular in the Euro periphery) found themselves in a large crisis after 2008.

This post is an update of the last ten years. Today the world displays smaller imbalances than at the peak of 2008 but what it was more interesting is the extent to which rebalancing had happened between different country groups.

Let's start with the global view. The Figure below shows current account balances as % of world GDP for some regions or groups of countries. Data goes all the way back to 1980 although data is missing for some countries before 1995 (see footnote on data sources).

In the period 1998-2008 (Global Imbalances period) we see the increasing surpluses of oil producer countries, China, advanced Asia (this includes Japan, South Korea, Singapore and Taiwan) and the rest of the world (ROW, many of these countries are emerging markets). The Euro area remained quite balanced (more on this below) and the only deficit country in this figure is the US, absorbing all the surpluses generated by the other countries [of course, the US was not the only deficit country. Some of the Euro area countries had a deficit as well as some of the countries in the rest of the world].

Since 2008 we have witnessed:

  • China is moving fast towards a balanced current account (IMF forecasts suggest that China's current account will be balanced within the next 2 years). 
  • Oil producers have moved towards a balanced CA with small deficits in 2015-2016 as a result of the decline in the price of oil.
  • The Euro area has massively shifted towards a large surplus (the largest among the surplus regions)
  • Advanced Asia has maintained or increased its surplus relative to previous years.
  • The US continues to be the country that absorbs most of the surpluses. The US deficit is smaller than in 2008 but remember this is measured as a ratio of World GDP not US GDP (relative to US GDP the decline would be less pronounced).
  • While in 2008 many emerging markets were savers, in 2018 all the surplus countries are advanced economies (Euro and Asia). Some emerging countries appear under rest of the world as absorbing some of these capital flows.



Some details on the two largest sources of surpluses today. What happened among advanced countries in Asia? The figure below shows that Japan was dominant in this group in the early years. But in recent years the increase in surpluses in Korea, Taiwan and Singapore means the three of them together have a larger surplus than Japan. Overall size is similar to before when measured against world GDP. If we were to measure it against their own GDP we would see an increase in surpluses.

And finally the Euro area. I divide the Euro area into three groups: Germany and Euro Deficit and Euro Surplus. The last two groups are decided by looking at the pattern of the current account of Euro members (excluding Germany) during the 2000-2008 period. Countries that showed consistent deficits are in the first group while the others are in the second one. We can see the large increase in surpluses in Germany in the decade of the 2000s while the group of deficit countries massively increased their deficits. After 2008 we see a fast rebalancing from the deficit countries towards a surplus while Germany maintains (measured as % of world GDP) or increases (relative to its own GDP) the current account surplus.

Antonio Fatás

Data source: Current Account balances (in USD) and World GDP (in USD) from IMF World Economic Outlook database. Data from 1980-1995 is missing for some countries (in particular emerging Asia, including China). During those years they appear as rest of the world.
on November 27, 2018 |   Edit