This paper analyzes the impacts of news shocks on macroeconomic volatility. Whereas in any purely forward-looking model, such as the baseline New Keynesian model, anticipation amplifies volatility, we obtain ambiguous results when including a backward-looking component. In addition to these theoretical findings, we use the estimated model of Smets and Wouters (2003) to provide numerical evidence that news shocks increase the volatility of key macroeconomic variables in the euro area when compared to unanticipated shocks. --Anticipated shocks,business cycles,volatility

Winkler, Roland C.

Wohltmann, Hans-Werner

English

Research Papers in Economics

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Winkler, Roland C.; Wohltmann, Hans-Werner
Working Paper
On the (de)stabilizing effects of news
shocks
Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics,
No. 2009,05
Provided in cooperation with:
Christian-Albrechts-Universität Kiel (CAU)
Suggested citation: Winkler, Roland C.; Wohltmann, Hans-Werner (2009) : On the
(de)stabilizing effects of news shocks, Economics working paper / Christian-Albrechts-
Universität Kiel, Department of Economics, No. 2009,05, http://hdl.handle.net/10419/27740D
on the (de)stabilizing 
effects of news shocks
by Roland Winkler and Hans-Werner Wohltmann
No 2009-05On the (de)stabilizing eﬀects of news shocks
Roland C. Winkler‡
CAU and IfW, Kiel
Hans-Werner Wohltmann§
CAU Kiel
August 20, 2009
Abstract
This paper analyzes the impacts of news shocks on macroeconomic volatility. Whereas
in any purely forward-looking model, such as the baseline New Keynesian model, antic-
ipation ampliﬁes volatility, we obtain ambiguous results when including a backward-
looking component. In addition to these theoretical ﬁndings, we use the estimated
model of Smets and Wouters (2003) to provide numerical evidence that news shocks
increase the volatility of key macroeconomic variables in the euro area when compared
to unanticipated shocks.
JEL classiﬁcation: E32
Keywords: Anticipated Shocks, Business Cycles, Volatility
‡Address: Roland C. Winkler, Kiel Institute for the World Economy, D¨ usternbrooker Weg 120, 24105 Kiel,
Germany, Phone: ++49-431-8814-207, E-mail: roland.winkler@ifw-kiel.de
§Address: Prof. Hans-Werner Wohltmann, Department of Economics, Christian-Albrechts-Universit¨ at
(CAU) Kiel, Olshausenstr. 40, 24098 Kiel, Germany. Phone: ++49-431-880-1449, Fax: ++49-431-880-
2228, E-mail: wohltmann@economics.uni-kiel.de1 Introduction
Does the anticipation of a future shock dampen macroeconomic volatility? In a very
insightful paper, F` eve, Matheron, and Sahuc (2009) (FMS henceforth) argue that exactly
the opposite may be true. They consider a one-dimensional purely forward-looking rational
expectations model with a news shock and demonstrate that the variance (or volatility) of
the endogenous variable is an increasing function of the length of the time period between
the anticipation and the realization of the shock, henceforth denoted as q. Notably, this
implies that the anticipation of a future disturbance destabilizes the model economy when
compared to an unanticipated disturbance of equal magnitude. FMS state robustness for
the hybrid case, where the dynamics of the endogenous variable is described both by a
forward-looking and a backward-looking component.
In the ﬁrst part of this paper we, however, demonstrate that in the hybrid case, the
correlation between anticipation and the variance of the endogenous variable is ambigu-
ous. Then, we seek to get insights about the volatility eﬀects of anticipated shocks in
a realistic model of the business cycle. To do so, we use the estimated Euro area model
developed by Smets and Wouters (2003) (SW henceforth). As suggested by our theoretical
ﬁndings for the one-dimensional hybrid case, the results for the SW model are ambigu-
ous. The variances of all macroeconomic variables are increasing functions of q only in
the case of cost-push shocks. For other disturbances such as shocks to productivity or
to government spending, there always exist some endogenous variables whose volatility is
dampened through anticipation, whereas the volatility of the other variables is ampliﬁed.
The paper is organized as follows. Section 2 considers a multidimensional forward-
looking systems with news shocks. Furthermore, we solve for the variance in the case of
a one-dimensional hybrid system. In Section 3, we present numerical results for the SW
model with news shocks. Section 4 provides concluding remarks.
12 Volatility and news: Some new theoretical results
Consider the following forward-looking model
Yt = AEt Yt+1 + c   xt , (1)
where xt is an exogenous disturbance, described by the stochastic process
xt = ρxt−1 + εt−q . (2)
with the autocorrelation parameter, 0 ≤ ρ < 1. εt is an i.i.d.-normal error term with zero
mean and unit variance. If q > 0, an innovation to xt is anticipated q periods in advance.
If q = 0, we have an unanticipated shock to xt. Yt is a n × 1 vector of endogenous
variables, Et is the expectation operator conditional on information up to date t, and c
is a n × 1 vector of constants. To obtain a unique and stable solution, we assume that
the n eigenvalues of the n × n system matrix, A, lie inside the unit circle. The following
extension of FMS is straightforward:
Result 1. In any purely forward-looking model, the variance of all endogenous variables
is a strictly increasing function of q.
An implication of this result is that in the baseline New Keynesian model, anticipated
shocks lead to higher volatility than unanticipated shocks of equal magnitude. Since in
the New Keynesian literature welfare is often measured in terms of a weighted average of
the variance of inﬂation and the output gap, it is worthwhile to mention that our ﬁnding
implies that agents are better oﬀ when they are faced with unanticipated rather than
anticipated shocks.
Next, consider the hybrid model economy, also discussed in FMS
yt =
b
1 + ab
yt−1 +
a
1 + ab
Et yt+1 +
1
1 + ab
εt−q , (3)
where yt is the only endogenous variable and it is assumed that |a| < 1, |b| < 1. It can be
2shown that the variance of yt is given by
Varq yt =
1
1 − b2
￿
1 − (ab)q+1
1 − ab
￿2
+
q−1 X
n=0
a2(q−n)
￿
1 − (ab)n+1
1 − ab
￿2
. (4)
Result 2. In a hybrid (one-dimensional) model, the variance of yt is an increasing func-
tion of q if sgn(a) = sgn(b). For sgn(a)  = sgn(b), the correlation between the variance of
yt and q is ambiguous.
To demonstrate that Varq yt may not be a strictly increasing function of q if sgn(a)  =
sgn(b), it suﬃces to compare the variance of an unanticipated shock, i.e. q = 0, to the
variance of a one-period anticipated innovation, i.e. q = 1. Then, Varq=1 yt > Varq=0 yt,
if and only if a2 + 2ab > 0. This inequality is violated if, for example, a = −b. Thus, in a
hybrid model, the volatility eﬀect of anticipations crucially depends on the parametrization
of the model. This result carries over when we consider a multidimensional hybrid model
such as the SW model analyzed in the next section.
3 Volatility and news in the Smets-Wouters model for the
Euro area
As pointed out by Woodford (2009), a dynamic stochastic general equilibrium model with
a number of real and nominal frictions such as the SW model reﬂects the current state of
the art in studying business cycle ﬂuctuations. However, the role of anticipated shocks as
a driving force in explaining business cycles is neglected. This paper seeks to close this
gap by assuming that the exogenous disturbances are not solely driven by unanticipated
stochastic innovations but also by q-period anticipated changes in the exogenous processes.
We restrict attention to exogenous shocks to total factor productivity (TFP), government
spending, the monetary policy rule, the inﬂation objective, as well as to price and wage
markups. We then explore the change in the volatility of the endogenous variables caused
by these shocks when changing q.
Technically, we proceed as follows. We calibrate the SW model using the estimated
3posterior modes of the parameters including the variances of the shocks and the autore-
gressive parameters of the exogenous disturbances. We reproduce the results of SW by
simulating the model for the above mentioned set of unanticipated shocks. We then ana-
lyze the same shocks (identical with respect to variance and autocorrelation) but assume
that agents learn about the exogenous disturbances 1, 2, 3, 4, or 8 quarters in advance.
Finally, we normalize the variances of each endogenous variable in the case of a q-period
anticipated shock by the variance in the case of an unanticipated shock.
Table 1 displays these ratios, denoted as relative variances, for output, ˆ Y , consumption,
ˆ C, investment, ˆ I, hours worked, ˆ L, inﬂation, ˆ π, the nominal interest rate, ˆ R, the real price
of installed capital, ˆ Q, the rental rate on capital, ˆ rk, and the real wage, ˆ w.1 As usual,
the potential (or natural) level of a variable, denoted by superscript p, is deﬁned as its
equilibrium level without nominal rigidities and constant price and wage markups. The
deviation of the actual level of output from its potential level is denoted as output gap.
Table 1 also displays the relative variance of ˆ Y p, ˆ Cp, ˆ Ip, ˆ Lp, and ˆ Y − ˆ Y p.
A value of the relative variance larger than one means that anticipation has a desta-
bilizing eﬀect on the particular variable. A value smaller than one indicates that the
opposite holds.
– Table 1 about here –
Table 1 shows that we obtain unambiguous results for price and wage markup shocks.
The anticipation of these typical cost-push shocks greatly ampliﬁes the volatility of all
variables in question. Moreover, our numerical results indicate that, in these cases, the
variance of all key macroeconomic variables is a strictly increasing function of q.
For all other shocks under consideration, the results are ambiguous. In the case of a
shock to TFP, the volatility of output, consumption, investment, the real wage, and the
rental rate on capital increases when q > 0, whereas the opposite is true for hours worked,
the nominal interest rate, and the output gap. Notice that the variances are mostly
not strictly increasing (or decreasing) functions in q. For inﬂation and the real price of
1The notation follows that of SW. A hat above a variable denotes the log deviation from its steady state.
4capital, a destabilizing eﬀect of anticipation only exists for small (empirically relevant) q.
The destabilizing anticipation eﬀect vanishes at long horizons.
Turning to policy shocks, it is worth emphasizing that an announced change in gov-
ernment spending dampens the volatility of a number of key macroeconomic variables.
Exceptions can be found for consumption, investment, and the rental rate on capital. A
similar ﬁnding is true when we consider an inﬂation objective shock. Except for inﬂation
and the nominal interest rate, the announcement of a (temporary) change in the central
bank’s inﬂation objective has a stabilizing eﬀect. A totally diﬀerent picture emerges when
we explore the eﬀects of a diﬀerent type of monetary policy shock, namely an interest rate
shock. Except for the real price of capital, all variables under consideration are much more
volatile when the change in the nominal interest rate is anticipated in advance. However,
the relative variances of output, consumption, investment, and hours worked behave in a
hump-shaped manner with increasing q.
It is worth mentioning that anticipation can also amplify the volatility of key macroe-
conomic variables, if we consider a version of the model without nominal rigidities. An
anticipated TFP shock increases the volatility of output and investment when compared
to an unanticipated innovation in TFP, whereas an announced government spending shock
increases the variance of investment.
Finally, since macroeconomic volatility plays a major role in the measurement of overall
welfare, it should be pointed out that agents in the estimated model of the Euro area are
potentially better oﬀ when they are faced with unanticipated rather than anticipated
shocks.2
4 Conclusion
To conclude, we relate our work to the recent empirical literature that emphasizes the role
of anticipated shocks as the most important source of macroeconomic ﬂuctuations (e.g.,
Beaudry and Portier, 2006, Schmitt-Groh´ e and Uribe, 2008, Beaudry and Lucke, 2009).
2An analysis of the welfare eﬀects of news shocks within models such as the SW model, in which a micro-
founded welfare measure is not a simple weighted average of the variances of some macroeconomic variables,
would be a fruitful area for future research.
5Our exploration of news shocks within an estimated model of the Euro area oﬀers a novel
insight into these ﬁndings by demonstrating that news shocks by itself may amplify the
volatility of key macroeconomic variables.
In this paper, our objective was not to put forth any explanation, but merely to
demonstrate the potentially destabilizing eﬀects of news shocks. To achieve a deeper
economic understanding, we leave for future research.
References
Beaudry, P., Portier, F., 2006. Stock Prices, News, and Economic Fluctuations. American
Economic Review 96, 1293-1307.
Beaudry, P., Lucke, B., 2009. Letting Diﬀerent Views about Business Cycles Compete.
NBER Working Papers 14950, NBER.
F` eve, P., Matheron, J., Sahuc, J.-G., 2009. On the dynamic implications of news shocks.
Economic Letters 102, 96-98.
Smets, F., Wouters, R., 2003. An Estimated Dynamic Stochastic General Equilibrium
Model for the Euro Area. Journal of the European Economic Association 1, 1123-1175.
Schmitt-Groh´ e, S., Uribe, M., 2008. What’s News in Business Cycles. NBER Working
Paper 14215, NBER.
Woodford, M., 2009. Convergence in Macroeconomics: Elements of the New Synthesis.
American Economic Journal: Macroeconomics 1, 267-279.
6Table 1: Relative variances in the Smets-Wouters Euro area model for diﬀerent q-period ahead anticipated shocks
q ˆ Y ˆ C ˆ I ˆ L ˆ π ˆ R ˆ Q ˆ r
k ˆ w ˆ Y − ˆ Y
p ˆ Y
p ˆ C
p ˆ I
p ˆ L
p
Productivity Shock
1 quarter 1.4227 1.3157 1.4632 0.7860 1.1818 0.9267 1.1813 1.2188 1.6134 0.8593 1.1156 0.8444 1.3610 0.8475
2 quarters 1.8623 1.5682 1.9789 0.6792 1.2955 0.8425 1.2542 1.5289 2.3881 0.7382 1.1970 0.7499 1.6616 0.7895
3 quarters 2.2919 1.7491 2.5174 0.6394 1.3182 0.7692 1.2483 1.8967 3.3105 0.6505 1.2540 0.6960 1.8946 0.7720
1 year 2.6901 1.8659 3.0483 0.6385 1.2727 0.7143 1.1941 2.3009 4.3455 0.5974 1.2933 0.6667 2.0656 0.7713
2 years 3.7539 1.9529 4.6555 0.7350 0.9091 0.6447 0.9384 3.7538 8.6210 0.6090 1.3543 0.6411 2.3417 0.7885
Government Spending Shock
1 quarter 0.8429 1.0938 1.1142 0.8494 0.9655 0.8766 0.9867 1.0759 0.9735 0.7933 0.8860 0.9197 1.0908 0.9120
2 quarters 0.7468 1.1683 1.2199 0.7677 0.8966 0.7857 0.9097 1.1521 0.8959 0.6655 0.8264 0.8767 1.1538 0.8689
3 quarters 0.6904 1.2245 1.3134 0.7271 0.7931 0.7338 0.8098 1.2252 0.8079 0.6083 0.7955 0.8558 1.1934 0.8484
1 year 0.6598 1.2652 1.3920 0.7106 0.7241 0.7143 0.7172 1.2919 0.7398 0.6071 0.7803 0.8471 1.2150 0.8395
2 years 0.6465 1.3320 1.5456 0.7226 0.4483 0.8182 0.6943 1.4571 0.8231 0.8399 0.7804 0.8448 1.2170 0.8383
Interest Rate Shock
1 quarter 1.0690 1.0433 1.0764 1.0642 1.0793 0.9910 0.9977 1.0735 1.0762 1.0690 – – – –
2 quarters 1.1254 1.0521 1.1475 1.1109 1.1585 1.2703 0.9444 1.1403 1.1484 1.1254 – – – –
3 quarters 1.1663 1.0316 1.2090 1.1376 1.2378 1.7117 0.8612 1.1957 1.2135 1.1663 – – – –
1 year 1.1905 0.9897 1.2572 1.1443 1.3170 2.2252 0.7639 1.2387 1.2688 1.1905 – – – –
2 years 1.1348 0.7403 1.2895 1.0130 1.5851 4.2432 0.4053 1.2590 1.3646 1.1348 – – – –
Inﬂation Objective Shock
1 quarter 0.9791 0.9562 0.9856 0.9659 1.0283 1.0956 0.8952 0.9870 0.9987 0.9791 – – – –
2 quarters 0.9514 0.9064 0.9644 0.9244 1.0543 1.1932 0.7887 0.9675 0.9917 0.9514 – – – –
3 quarters 0.9178 0.8536 0.9366 0.8771 1.0777 1.2872 0.6874 0.9419 0.9792 0.9178 – – – –
1 year 0.8794 0.8004 0.9031 0.8257 1.0986 1.3745 0.5956 0.9109 0.9615 0.8794 – – – –
2 years 0.7031 0.6139 0.7323 0.6107 1.1566 1.6369 0.3480 0.7536 0.8516 0.7031 – – – –
Wage Markup Shock
1 quarter 3.9260 3.9626 3.9197 3.3853 3.8873 3.9574 4.1001 3.2112 3.1220 3.9260 – – – –
2 quarters 8.6200 8.8089 8.5861 6.6228 8.3699 8.8723 9.4069 5.9217 5.6095 8.6200 – – – –
3 quarters 14.8714 15.4008 14.7715 10.3786 14.0694 15.6809 16.8690 8.7127 8.0409 14.8714 – – – –
1 year 22.4365 23.5244 22.2174 14.4269 20.5780 24.3404 26.2539 11.3541 10.2015 22.4365 – – – –
2 years 60.7923 65.1859 59.5807 30.9596 48.3006 76.1489 73.8727 19.0154 15.2695 60.7923 – – – –
Price Markup Shock
1 quarter 3.6929 3.3257 3.8280 3.6455 2.6108 3.4444 4.0020 3.6574 3.5167 3.6929 – – – –
2 quarters 7.5952 6.4627 8.0930 7.4471 4.2200 6.8333 8.9432 7.5278 7.0671 7.5952 – – – –
3 quarters 12.2580 10.2116 13.3198 11.9788 5.6210 10.9444 15.5479 12.1389 11.2435 12.2580 – – – –
1 year 17.3060 14.4544 19.0465 16.9180 6.7733 15.5000 23.4266 17.1111 15.7114 17.3060 – – – –
2 years 37.1295 33.5871 40.9631 37.5291 9.5008 38.2222 58.3542 36.0463 32.3171 37.1295 – – – –
Note: The relative variance is deﬁned as the ratio of the variance of a variable x in the case of a q− period anticipated shock to the variance of x in the case
of an unanticipated shock of equal magnitude.
7

An Estimated Dynamic Stochastic General Equilibrium Model for the Euro Area.

Convergence in Macroeconomics: Elements of the New Synthesis.

Letting Diﬀerent Views about Business Cycles Compete.

On the dynamic implications of news shocks.

Stock Prices, News, and Economic Fluctuations.

What’s News in Business Cycles.

On the (de)stabilizing effects of news shocks

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