FRBSF Economic Letter
2006-21; August 25, 2006
New Uses for New Macro Derivatives
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Economic forecasters often look to the performance
of futures markets to help predict such economic developments
as movements in the price of oil and other commodities. In addition,
relatively new financial market instruments, like TIPS (Treasury
Inflation Protected Securities) help policymakers get a handle
on the public's inflation expectations.
In the last few years, derivatives markets involving
bets on future economic events have emerged. In October 2002,
Goldman Sachs and Deutsche Bank joined forces to form a market
in what they call "Economic Derivatives." This market
allows investors to purchase instruments whose payoff is linked
to growth in U.S. non-farm payrolls, retail sales, business confidence,
and initial unemployment claims, as well as the Euro-area harmonized
CPI. More recently, other U.S.-based markets have been created
for GDP and the international trade balance, and plans are under
way for instruments on the U.S. CPI.
For investors, these markets help hedge their
portfolios against the uncertainties of economic outcomes. For
forecasters and policymakers, these markets help pull together
the best guesses of market participants, and thereby provide
an informed consensus on how economic developments will unfold.
This Economic Letter summarizes research by Gürkaynak and
Wolfers (2005), which examines how these markets work and how
useful they may be for economic predictions.
How derivatives markets for events work
In the Economic Derivatives market, a trader
might purchase an instrument that pays $1 if the next employment
report shows monthly growth in non-farm payrolls of between 100,000
and 125,000 jobs. The transaction is structured so that the payoff
is binary—either $1 if you are correct, or nothing if you are
not; hence, these are called "binary options." Similarly,
a trader can decide to purchase an instrument that pays $1 if
employment grows by 125,000 to 150,000 jobs. Indeed, around a
dozen such options are typically offered, thereby allowing traders
to take positions on the particular outcomes that they regard
as most likely. Traders also have the option of selling (or going "short")
on any outcomes that they think are particularly unlikely.
These options are traded in an auction that typically
lasts for about an hour and which occurs either on the morning
before the data release, or a few days before. As such, this
market allows traders to hedge their portfolios so that they
are not exposed to the particular risk—typically called "event
risk"—that arises due to unexpected economic announcements
causing sharp changes in the value of stocks and bonds.
A particularly interesting feature of this market
is the mechanism used to match willing buyers with sellers. This
market uses a pari-mutuel system, which is quite uncommon in
financial markets, but much more common in horse racing. In the
racing context, punters bet on their favorite horse, and all
the money bet is put into a central pot; when the race is run,
the house simply divides the money from this central pot among
those who bet on the winning horse (with those who purchased
more tickets receiving a proportionately larger share of the
pot). In the Economic Derivatives context, the mechanism is similar,
but instead of betting on a favorite horse, traders purchase
tickets in their preferred economic outcome. An interesting feature
of this mechanism is that the return to selecting the winning
outcome is not known until all trades have been executed, although
indicative estimates can be shown during the auction process.
 Figure 1 shows the final prices from a specific
auction in which traders took positions on the number of jobs
created in May 2005. We see that traders were willing to pay
11.7 cents for the option paying $1 if payroll growth was
indeed between 100,000 and 125,000 jobs. As such, it seems reasonable
to infer that this particular outcome had about an 11.7% probability
of occurring. Inferring probabilities from the prices of binary
options has some intuitive appeal, and Wolfers and Zitzewitz
(2006) argue that it has also proven to be quite accurate in
many other prediction markets.
Gürkaynak and Wolfers also
explore the question of whether risk-aversion might lead to a
risk-premium, concluding that the evidence so far suggests that
the relevant adjustment is sufficiently small that we can essentially
ignore risk-adjustments.
Thus the prices on each of the outcomes shown
in Figure 1 essentially provide a market-generated estimate
of the full probability distribution of different outcomes. If
the market is reasonably accurate, this distribution provides
a set of forecasts of the likelihood of different outcomes that
may be quite useful to forecasters and policymakers.
Using data from macro derivatives markets to make
economic forecasts
The particular advantage of a market-generated
forecast is that these prices reflect the joint wisdom of the
many traders operating in this market, and not just the idiosyncratic
views of a particular forecaster. Previous research tells us
that aggregating forecasts from many forecasters typically produces
a much more accurate forecast than simply following a preferred
forecaster.
 We now have data from the first 153 of these
Economic Derivatives auctions and have compared them with an
alternative forecast aggregator: the survey of the expectations
of financial market analysts taken on the Friday prior to the
data release. Figure 2 shows this comparison for the most
highly watched of our data series, monthly growth in non-farm
payrolls. Specifically, we calculated the mean of the price distribution
for each of the auctions in our sample, and the average forecast
across forecasters from the Friday survey, and asked: Which better
predicts the actual outcome? Figure 2 shows how similar
the two competing sets of forecasts were. Even so, the Economic
Derivatives forecasts were slightly (5%-10%) more accurate, although
these differences were not statistically significant.
Another way of analyzing these data is to ask:
how should one weight these two sets of forecasts to arrive at
an optimal prediction? A regression analysis is needed to answer
this question, and here the results were less equivocal: once
one knows the Economic Derivatives forecast, there is no useful
information in the survey-based forecast. This is consistent
with the efficient markets hypothesis, which suggests that market
prices tend to incorporate all publicly available information—including
the published forecasts of other forecasters.
We have also performed a similar comparison of
the predictive ability of market- and survey-based forecasts
for retail sales growth, business confidence, and initial unemployment
claims, and this further analysis confirmed this general pattern:
The Economic Derivatives forecast encompasses all of the information
available in the survey-based forecast.
Another implication of the efficient markets
hypothesis is that the stock market should only respond to unexpected
developments. Thus, even if non-farm payrolls grew strongly in
a particular month, if that growth was expected, its announcement
should not lead to any changes in stock prices. This raises the
question: What movements in non-farm payrolls are expected, and
what are unexpected?
 The comparison of Economic Derivatives and survey-based
forecasts provide two alternative baselines: We can compare actual
outcomes to each of these forecasts, and ask which better predicts
subsequent stock market movements. In order to isolate the specific
stock market movements that were most likely to be driven by
the announcement of economic news, we analyze the change in stock
prices from 5 minutes before the announcement to a mere
25 minutes later. Figure 3 compares this stock market
response to our two alternative measures of the unexpected component
of non-farm payrolls. We find the measure based upon the Economic
Derivatives data does a much better job in explaining the response
of the stock market to economic news.
We have extended this analysis in two further
directions, examining both forecasts of other variables (business
confidence, retail sales and initial unemployment claims), and
the response of bond markets to economic news. In each case,
we confirm our main conclusions: the Economic Derivatives market
better predicts financial market responses to economic data than
does the alternative survey-based measure.
Finally, there is an existing literature that
suggests that economic forecasters tend to make systematic mistakes,
in a manner consistent with some of the insights of behavioral
economics. For instance, there is evidence that forecasters tend
to stick with bad forecasts for too long and take insufficient
account of recent data that should have led them to change their
views. We performed similar tests on both of our forecast measures,
finding some systematic evidence that the average survey-based
forecast shows some of these problems. Interestingly, there is
very little evidence that the market-based forecast displays
these pathologies, although given our limited sample, this evidence
should not be overstated.
Conclusion
Overall our analysis of the Economic Derivatives
markets yielded quite convincing evidence that market-generated
forecasts are very accurate and probably at least as accurate
as any other form of forecast. This finding makes economic forecasting
a very simple exercise for most of us: Rather than work through
a complicated model of the economy, it is more accurate (and
surely quicker!) simply to look to the prices in economic derivatives
markets to assess the likelihood of various outcomes.
The underlying logic of these markets may eventually
prove to be quite persuasive, and other research (Wolfers and
Zitzewitz, 2004) has shown that analogous prediction markets
have similar power in predicting outcomes as diverse as elections,
baseball games, and movie successes. Ongoing research is examining
the extent to which prediction markets may be harnessed to forecast
outcomes of direct interest to both businesses and public policymakers.
The intuition is simply that markets can make the wisdom of many
of us easily accessible to all of us.
Justin Wolfers
Assistant Professor, University of Pennsylvania,
and Visiting Scholar, FRBSF
References
Gürkaynak, Refet, and Justin
Wolfers. 2005. "Macroeconomic Derivatives: An Initial Analysis
of Market-based Macro Forecasts, Uncertainty, and Risk." FRBSF
Working Paper 2005-26, and forthcoming in NBER International
Seminar on Macroeconomics 2005, MIT Press.
Wolfers, Justin, and Eric Zitzewitz. 2004. "Prediction
Markets." Journal of Economic Perspectives 18(2)
pp. 107-126.
Wolfers, Justin, and Eric Zitzewitz. 2006. "Interpreting
Prediction Market Prices as Probabilities." FRBSF Working
Paper 2006-11.
Opinions expressed in this newsletter
do not necessarily reflect the views of the management
of the Federal Reserve Bank of San Francisco or of the
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Questions? Contact
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