Risk and Return in the Bond Markets

This is the second post to advertise the work of a UCLA graduate student who is looking for a job this year. My first post introduced Sangyup Choi who is working on uncertainty shocks in emerging markets. This post introduces Chan Mang who is working on the implications of term structure models for the foreign exchange market.

Chan Mang Chan Mang graduated from UCLA two years ago. In 2012 he was awarded a post doc position at the prestigious National University of Singapore and last year he worked in the private sector.  Chan's research builds on the  widely cited bond pricing model developed by John Cochrane and Monika Piazzesi.

Finance economists seek to explain the term structure of bond prices. Why do long bonds typically earn a higher yield than short bonds and how do the yields for bonds of different maturities move over time? A graph of these yields as a function of duration  is called the yield curve.

Figure 1 is a graph of the yield curve, taken from the Treasury Website, for December 14th 2014. 

Figure 1:

The graph shows that one month treasuries are currently paying an interest rate of zero. However, longer denomination treasuries have higher yields that increase monotonically with duration and thirty year bonds are currently paying 3%.  This pattern is not time invariant and there have been periods when the yield curve is flat or even downward sloping over some regions.

Figure 2 compares the yield curve from December 2014, with that from February 2006. Back in 2006 the yield on six month treasuries was over 4.6%, but the yield on five year securities was lower at 4.5%. When long yields are lower than short yields, the yield curve is said to be inverted, and historically, an inverted yield curve has been the harbinger of a recession.

Figure 2

Back to the main story.  Finance theorists explain the yield curve with what they call 'factor models'. They look at the evolution of yield curves over time and they seek common components that help to explain how all of the yields move over time.  Cochrane and Piazzesi developed the state of the art factor model to understand these phenomena.

Enter Chan Mang. In Chan's words

In my work, I show that the affine term structure model of Cochrane and Piazzesi (2008) has inconsistent predictions when I compare different financial markets. ... [because] the additional information in the term structure ... generates an implausible amount of predictability in exchange rates and currency excess returns. 

[I find that] ... it is either the case that bond excess returns or currency returns are predictable, but not both at the same time.

To understand these features of the data, Chan is developing a theoretical model that connects the Cochrane-Piazzesi explanation with what monetary policy makers think they are doing.

Repeat After Me: The Quantity of Labor Demanded is Not Always Equal to the Quantity Supplied

I've been teaching a class on intermediate macroeconomics this quarter. Increasingly, over the past twenty years or more, intermediate macro classes at UCLA (and in many other top schools), have focused almost exclusively on economic growth. That reflected a bias in the profession, initiated by Finn Kydland and Ed Prescott, who persuaded macroeconomists to use the Ramsey growth model as a paradigm for business cycle theory. According to this Real Business Cycle view of the world, we should think about consumption, investment and employment 'as if' they were the optimal choices of a single representative agent with super human perception of the probabilities of future events. 

Although there were benefits to thinking more rigorously about inter-temporal choice, the RBC program as a whole led several generations of the brightest minds in the profession to stop thinking about the problem of economic fluctuations and to focus instead on economic growth. Kydland and Prescott assumed that labor is a commodity like any other and that any worker can quickly find a job at the market wage. In my view, the introduction of the shared belief that the labor market clears in every period, was a huge misstep for the science of macroeconomics that will take a long time to correct.

In my intermediate macroeconomics class, I am teaching business cycle theory from the perspective of Keynesian macroeconomics but I am grounding old Keynesian concepts in the theory of labor market search, based on my recent books (2010a, 2010b) and articles (2011, 2012, 2013a, 2013b).  I am going to use this blog to explain some insights that undergraduates can easily absorb that are adapted from my understanding of Keynes' General Theory. Today's post is about measuring employment.  In later posts, I will take up the challenge of constructing a theory to explain unemployment.

Ever since Robert Lucas introduced the idea of continuous labor market clearing, the idea that it may be useful to talk of something called 'involuntary unemployment' has been scoffed at by the academic chattering classes. It's time to fight back. The concept of 'involuntary unemployment' does not describe a loose notion that characterizes the sloppy work of heterodox economists from the dark side. It is a useful category that describes a group of workers who have difficulty finding jobs at existing market prices. 



The idea that the labor market is well described by a model in which a market wage adjusts to equate the quantity of labor demanded with the quantity supplied bears little resemblance to anything we see in the real world. What makes me so confident of that claim?

Figure 1: Average Weekly Hours and the Unemployment Rate  (c) Roger E. A. Farmer

Employment varies over time for three reasons. First, the average number of hours fluctuates. Second people enter and leave the labor force and third, those people who are in the labor force flow into and out of unemployment. Figure 1 (taken from my 2013 Bank of England article) plots data from 1964 through 2012 on average weekly hours and the unemployment rate.  The blue line, measured on the right scale, is average weekly hours. The pink line, on the left scale, is the unemployment rate. The grey shaded areas are NBER recessions.

The facts are clear. Although hours do fall during recessions, the movements in hours are swamped by movements in the unemployment rate. Consider, for example, the 2008 recession. Average weekly hours fell from 34 to 33. The unemployment rate, in contrast, increased from 4% to 10%.  

The main story in the data on average weekly hours is that they declined from 39 hours per week in 1964 to 34 hours per week in 2012. As American workers got richer they collectively chose to take a larger share of their wages in the form of leisure.  These movements are important if our goal is to understand long term trends: they do not tell us much about recessions.

What about the participation rate? Recently, there has been a great deal of angst amongst policy makers  who are asking if the fall in the participation rate that occurred during the 2008 recession was cyclical or structural. Figure 2 sheds some light on that question. The graph demonstrates that there is no clear tendency for participation rates to drop in recessions. For example, participation was higher at the end of the 1973 recession than at the beginning and in a number of other post-war recessions it has remained flat. As with average weekly hours, this figure shows that movements in hours during recessions are almost entirely caused by movements in the unemployment rate.

Figure 2: Participation and the Unemployment Rate (c) Roger E. A. Farmer

So what does cause the participation rate to vary over time? I look at Figure 2 and I see a parabola. Participation went up from 1960 to 2000 as women entered the labor force. It started to fall again in 2000 as the baby boomer generation  began to retire. These secular trends swamp business cycle movements in the participation rate  and they are largely explained  by sociology and by demographics. 

What do I take away from these data? There are three reasons why employment fluctuates over time. People vary the average number of hours worked per week. Households send more or less members to look for a job. And those people looking for jobs find it more or less difficult to find one. The first two reasons for fluctuating employment could perhaps be modeled as the smooth functioning of a market in which the demand and supply of labor respond to changes in market prices.  I cannot see any simple way to model unemployment fluctuations as the operation of a competitive market for labor in the usual sense in which economists use that term. 

Repeat after me: the quantity of labor demanded is not always equal to the quantity supplied.

The Impact of Financial Market Volatility on Emerging Market Economies

Early in the New Year, economists from all over the world will congregate in Boston for the 2015 annual meetings of the American Economics Association. The main purpose of these meetings is to interview new Ph.D. candidates for potential jobs as academics and in the public and private sectors as research and/or policy economists.  

Sangyup Choi

As an academic economist at UCLA, my job includes teaching undergraduates, carrying out economic research for publication in books and journals and, (my favorite part), training new Ph.D. economists. Teaching graduate students is a rewarding experience for an academic as we get to watch our students progress from undergraduates to colleagues. What begins as a teaching experience in year 1 ends up as a learning experience in year 5. 

Today's blog features my student, Sangyup (Sam) Choi, who is working on  the impact of financial market volatility on emerging market economies.  My colleague Aaron Tornell and I are Sam's principal advisors.

Sam is studying the VIX and its impact on economic activity. This is a hot topic amongst macroeconomists ever since Nick Bloom showed, in a paper published in Econometrica,  that shocks to uncertainty are a causal factor in US. recessions. What, you ask is the VIX?


The VIX is an index of volatility that goes up when traders are less certain about the future. In his Econometrica paper, Nick showed that shocks to the VIX are an independent causal factor that helps to predict future U.S. output. Here is a graph of the VIX for the period 2000 to 2014.

Figure 1: The VIX from 2000 to 2014

In a paper published last year in Economics Letters, Sam showed that Nick’s results are sensitive to the period of study. The VIX does predict future output in data from 1950 through 1982, but that result goes away after 1983. The largest recession in post war history in which the VIX jumped by a factor of four, (see Figure 1), did not have a significant independent impact on the U.S. economy, once other explanatory variables have been accounted for. That in itself is surprising. But it gets better.

In his most recent work, Sam has looked at the impact of the VIX on emerging market economies. He finds that although shocks to the VIX do not have much impact on US output, they do have a noticeable impact on the output of emerging market economies. Figure 2 presents the evidence for that claim.

Figure 2: The Impact of a VIX shock on the U.S. & Korea (c) Sangyup Choi

In Sam’s own words.

My job market paper, entitled “The real impact of VIX shocks on emerging market economies: flight to quality mechanism,” starts from an observation that fluctuations in the VIX have had a much larger impact on emerging market economies than they have had on the US economy for the last two decades.  This finding is puzzling as the VIX measures U.S. stock market volatility. 

To understand why an increase in the VIX has a much larger impact on output fluctuations in emerging market economies than on the U.S. economy, I build a small open economy model with credit market imperfections. The model incorporates a portfolio decision by international investors and an increase in the VIX makes these investors withdraw their funds from emerging markets. 

In Sam’s theory, VIX shocks, in a world of integrated capital markets, cause investors to pull their money from emerging markets. Because emerging market economies have poorly developed credit markets, the sudden outflow of cash causes domestic firms to cut back on production and lay off workers.

Back to Sam…

[In my dissertation] … I build a theoretical model that helps understand my empirical findings.

… I confirm the prediction of my model by estimating structural Vector Autoregressions using data from 18 emerging market economies between 1994 and 2013. … I find that VIX shocks are followed by a statistically significant increase in the real interest rate, a fall in domestic credit, and a real currency depreciation. In contrast, in the U.S. economy VIX shocks are followed by a (statistically insignificant) fall in the real interest rate, an increase in domestic credit, and a real currency appreciation.

…the new empirical findings from my two papers expand our understanding of the importance of uncertainty shocks. When combined with credit market imperfections, the VIX serves as a real-time indicator of risk to emerging market economies.

Here is a link to the online appendix to Sam's paper, which has a dazzling array of evidence to support his claim.

Sam is a terrific economist and will make a great colleague. Hire him! You won't be disappointed.

Will Americans Ever Vote for a Far-Reaching Wealth Tax?

 Here is a link to my piece on inequality that was published today on the Guardian Economics Blog

What Thomas Piketty has shown us, is that since 1980, it is only the rich and the very rich who have benefited from growth, writes Roger Farmer

But there will come a time when the average American realises that the dream that his parents aspired to is no longer within his reach. Photograph: Peter Hundert/ Peter Hundert/cultura/Corbis

Don't Panic --- Yet!

Volatility has returned to the stock market and most of the gains of 2014 were wiped out in the last week. Is it time to panic? Not yet!

There is a close relationship between changes in the value of the stock market and changes in the unemployment rate one quarter later. My research here, and here shows that a persistent 10% drop in the real value of the stock market is followed by a persistent 3% increase in the unemployment rate. The important word here is persistent. If the market drops 10% on Tuesday and recovers again a week later, (not an unusual movement in a volatile market), there will be no impact on the real economy. For a market panic to have real effects on Main Street it must be sustained for at least three months.  And there is no sign that that is happening: Yet.

Figure 1: Wall Street and Main Street (c) Roger E. A. Farmer

Figure 1 plots a simple transformation of the value of the unemployment rate, measured on the left axis, and the real value of the S&P, measured on the right axis, in log units. This graph shows a clear correlation between these series and a more careful investigation reveals that this correlation is causal in the sense in which Clive Granger defined that term: there is information in the stock market that helps to predict the future unemployment rate.

It is of course, possible, that movements in the stock market are only apparently causal. In reality, the clever people who trade in the markets are prescient in their ability to foresee the very bad fundamentals that are driving the real economy. It is also possible that sometimes, market participants panic and that panic has real consequences when the rest of us find that our houses and pension plans are suddenly worthless. My own theoretical work supports the latter hypothesis but reasonable people can disagree.

So: should you be worried that we are about to enter a double dip recession? In my view, not yet, because, as of right now, the market shows no signs of a persistent drop when measured in real terms.    When (and if) the Yellen Fed follows through with its withdrawal of QE; we may be looking at a very different situation. Hang on to your hats!