Risk, uncertainty and monetary policy
Working Paper Research
by G. Bekaert, M. Hoerova and M. Lo Duca
October 2012 No 229
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NBB WORKING PAPER No. 229 - OCTOBER 2012
Risk, Uncertainty and Monetary Policy*
Geert Bekaert
Marie Hoerova
Marco Lo Duca
Columbia GSB
ECB
ECB
July 2012
Abstract
The VIX, the stock market option-based implied volatility, strongly co-moves with
measures of the monetary policy stance. When decomposing the VIX into two
components, a proxy for risk aversion and expected stock market volatility
(“uncertainty”), we find that a lax monetary policy decreases both risk aversion and
uncertainty, with the former effect being stronger. The result holds in a structural vector
autoregressive framework, controlling for business cycle movements and using a variety
of identification schemes for the vector autoregression in general and monetary policy
shocks in particular.
JEL Classification: E44; E52; G12; G20; E32
Keywords: Monetary policy; Option implied volatility; Risk aversion; Uncertainty;
Business cycle; Stock market volatility dynamics
* We thank Tobias Adrian, Gianni Amisano, David DeJong, Bartosz Mackowiak, Philippe Mueller, Frank
Smets, José Valentim, Jonathan Wright and seminar participants at the European Central Bank, the FRB
Philadelphia, the European Finance Association Annual Meetings (Stockholm), the Annual IMF Jacques
Polak Research Conference (Washington), the Midwest Macroeconomics Meetings (East Lansing), the
Annual Seminar on Banking, Financial Stability and Risk (Sao Paulo), the Vienna Macroeconomics
Workshop (Rome) and the Financial Management Association Meetings (New York) for helpful comments
and suggestions. Falk Bräuning and Carlos Garcia de Andoain Hidalgo provided excellent research
assistance. The views expressed do not necessarily reflect those of the European Central Bank or the
Eurosystem.
1
Electronic copy available at: http://ssrn.com/abstract=1561171
2
1. Introduction
A popular indicator of risk aversion in financial markets, the VIX index, shows strong
co-movements with measures of the monetary policy stance. Figure 1 considers the crosscorrelogram between the real interest rate (the Fed funds rate minus inflation), a measure
of the monetary policy stance, and the logarithm of end-of-month readings of the VIX
index. The VIX index essentially measures the “risk-neutral” expected stock market
variance for the US S&P500 index. The correlogram reveals a very strong positive
correlation between real interest rates and future VIX levels. While the current VIX is
positively associated with future real rates, the relationship turns negative and significant
after 13 months: high VIX readings are correlated with expansionary monetary policy in
the medium-run future.
The strong interaction between a “fear index” (Whaley (2000)) in the asset markets
and monetary policy indicators may have important implications for a number of
literatures. First, the recent crisis has rekindled the idea that lax monetary policy can be
conducive to financial instability. The Federal Reserve’s pattern of providing liquidity to
financial markets following market tensions, which became known as the “Greenspan
put,” has been cited as one of the contributing factors to the build-up of a speculative
bubble prior to the 2007-09 financial crisis. 1 Whereas some rather informal stories have
linked monetary policy to risk-taking in financial markets (Rajan (2006), Adrian and Shin
1
Investors increasingly believed that when market conditions were to deteriorate, the Fed would step in and
inject liquidity until the outlook improved. Such perception may encourage excessive risk-taking and lead
to higher valuations and narrower credit spreads. See, for example, “Greenspan Put may be Encouraging
Complacency,” Financial Times, December 8, 2000.
3
Electronic copy available at: http://ssrn.com/abstract=1561171
(2008), Borio and Zhu (2008)), it is fair to say that no extant research establishes a firm
empirical link between monetary policy and risk aversion in asset markets. 2
Second, Bloom (2009) and Bloom, Floetotto and Jaimovich (2009) show that
heightened “economic uncertainty” decreases employment and output. It is therefore
conceivable that the monetary authority responds to uncertainty shocks, in order to affect
economic outcomes. However, the VIX index, used by Bloom (2009) to measure
uncertainty, can be decomposed into a component that reflects actual expected stock
market volatility (uncertainty) and a residual, the so-called variance premium (see, for
example, Carr and Wu (2009)), that reflects risk aversion and other non-linear pricing
effects, perhaps even Knightian uncertainty. Establishing which component drives the
strong co-movements between the monetary policy stance and the VIX is therefore
particularly important.
Third, analyzing the relationship between monetary policy and the VIX and its
components may help clarify the relationship between monetary policy and the stock
market, explored in a large number of empirical papers (Thorbecke (1997), Rigobon and
Sack (2004), Bernanke and Kuttner (2005)). The extant studies all find that expansionary
(contractionary) monetary policy affects the stock market positively (negatively).
Interestingly, Bernanke and Kuttner (2005) ascribe the bulk of the effect to easier
monetary policy lowering risk premiums, reflecting both a reduction in economic and
financial volatility and an increase in the capacity of financial investors to bear risk. By
using the VIX and its two components, we test the effect of monetary policy on stock
market risk, but also provide more precise information on the exact channel.
2
For recent empirical evidence that monetary policy affects the riskiness of loans granted by banks see, for
example, Altunbas, Gambacorta and Marquéz-Ibañez (2010), Ioannidou, Ongena and Peydró (2009),
Jiménez, Ongena, Peydró and Saurina (2009), and Maddaloni and Peydró (2010).
4
Electronic copy available at: http://ssrn.com/abstract=1561171
This article characterizes the dynamic links between risk aversion, economic
uncertainty and monetary policy in a simple vector-autoregressive (VAR) system. Such
analysis faces a number of difficulties. First, because risk aversion and the stance of
monetary policy are jointly endogenous variables and display strong contemporaneous
correlation (see Figure 1), a structural interpretation of the dynamic effects requires
identifying restrictions. Monetary policy may indeed affect asset prices through its effect
on risk aversion, as suggested by the literature on monetary policy news and the stock
market, but monetary policy makers may also react to a nervous and uncertain market
place by loosening monetary policy. In fact, Rigobon and Sack (2003) find that the
Federal Reserve does systematically respond to stock prices. 3
Second, the relationship between risk aversion and monetary policy may also reflect
the joint response to an omitted variable, with business cycle variation being a prime
candidate. Recessions may be associated with high risk aversion (see Campbell and
Cochrane (1999) for a model generating counter-cyclical risk aversion) and at the same
time lead to lax monetary policy. Our VARs always include a business cycle indicator.
Third, measuring the monetary policy stance is the subject of a large literature (see,
for example, Bernanke and Mihov (1998a)); and measuring policy shocks correctly is
difficult. Models featuring time-varying risk aversion and/or uncertainty, such as Bekaert,
Engstrom and Xing (2009), imply an equilibrium contemporaneous link between interest
rates and risk aversion and uncertainty, through precautionary savings effects for
example. Such relation should not be associated with a policy shock. However, our
3
The two papers by Rigobon and Sack (2003, 2004) use an identification scheme based on the
heteroskedasticity of stock market returns. Given that we view economic uncertainty as an important
endogenous variable in its own right with links to the real economy and risk premiums, we cannot use such
an identification scheme.
5
results are robust to alternative measures of the monetary policy stance and of monetary
policy shocks. In particular, the results are robust to identifying monetary policy shocks
using a standard structural VAR, high frequency Fed funds futures changes following
Gürkaynak, Sack and Swanson (2005), and monthly surprises based on the daily Fed
funds futures following the approach in Bernanke and Kuttner (2005).
The remainder of the paper is organized as follows. In Section 2, we detail the
measurement of the key variables in the VAR, including monetary policy indicators,
monetary policy shocks and business cycle indicators. First and foremost, we provide
intuition on how the VIX is related to the actual expected variance of stock returns and to
risk preferences. While the literature has proposed a number of risk appetite measures
(see Baker and Wurgler (2007) and Coudert and Gex (2008)), our measure is
monotonically increasing in risk aversion in a variety of economic settings. This
motivates our empirical strategy in which we split the VIX into a pure volatility
component (“uncertainty”) and a residual, which should be more closely associated with
risk aversion. In Section 3, we analyze the dynamic relationship between monetary policy
and risk aversion and uncertainty in standard structural VARs. The results are remarkably
robust to a long list of robustness checks with respect to VAR specification, variable
definitions and alternative identification methods. In Section 4, we use two alternative
methods to identify monetary policy shocks relying on Fed futures data.
Our main findings are as follows. A lax monetary policy decreases risk aversion in
the stock market after about nine months. This effect is persistent, lasting for more than
two years. Moreover, monetary policy shocks account for a significant proportion of the
variance of risk aversion. The effects of monetary policy on uncertainty are similar but
6
somewhat weaker. On the other hand, periods of both high uncertainty and high risk
aversion are followed by a looser monetary policy stance but these results are less robust
and much weaker statistically. Finally, it is the uncertainty component of the VIX that has
the statistically stronger effect on the business cycle, not the risk aversion component.
2. Measurement
This section details the measurement of the key inputs to our analysis: risk aversion
and uncertainty; the monetary policy stance and monetary policy shocks; and finally,
business cycle variation. Our data start in January 1990 (the start of the model-free VIX
series) but we perform our analysis using two different end-points for the sample: July
2007, yielding a sample that excludes recent data on the crisis; and August 2010. The
crisis period presents special challenges as stock market volatilities peaked at
unprecedented levels and the Fed funds target rate reached the zero lower bound. We
detail how we address these challenges below. Table 1 describes the basic variables we
use and assigns them a short-hand label.
2.1 Measuring Risk Aversion and Uncertainty
To measure risk aversion and uncertainty, we use a decomposition of the VIX index.
The VIX represents the option-implied expected volatility on the S&P500 index with a
horizon of 30 calendar days (22 trading days). This volatility concept is often referred to
as “implied volatility” or “risk-neutral volatility,” as opposed to the actual (or “physical”)
expected volatility. Intuitively, in a discrete state economy, the physical volatility would
use the actual state probabilities to arrive at the physical expected variance, whereas the
risk-neutral variance would make use of probabilities that are adjusted for the pricing of
risk.
7
The computation of the actual VIX index relies on theoretical results showing that
option prices can be used to replicate any bounded payoff pattern; in fact, they can be
used to replicate Arrow-Debreu securities (Breeden and Litzenberger (1978), Bakshi and
Madan (2000)). Britten-Jones and Neuberger (2000) and Bakshi, Kapadia and Madan
(2003) show how to infer “risk-neutral” expected volatility for a stock index from option
prices. The VIX index measures implied volatility using a weighted average of Europeanstyle S&P500 call and put option prices that straddle a 30-day maturity and cover a wide
range of strikes (see CBOE (2004) for more details). Importantly, this estimate is modelfree and does not rely on an option pricing model.
While the VIX obviously reflects stock market uncertainty, it conceptually must also
harbor information about risk and risk aversion. Indeed, financial markets often view the
VIX as a measure of risk aversion and fear in the market place. Because there are wellaccepted techniques to measure the physical expected variance, we can split the VIX into
a measure of stock market or economic uncertainty, and a residual that should be more
closely associated with risk aversion. The difference between the squared VIX and an
estimate of the conditional variance is typically called the variance premium (see, e.g.,
Carr and Wu (2009)). 4 The variance premium is nearly always positive and displays
substantial time-variation. Recent finance models attribute these facts either to nonGaussian components in fundamentals and (stochastic) risk aversion (see, for instance,
Bekaert and Engstrom (2010), Bollerslev, Tauchen and Zhou (2009), Drechsler and
Yaron (2011)) or Knightian uncertainty (see Drechsler (2009)). In the Appendix, we use
a one-period discrete economy with power utility to illustrate the difference between
4
In the technical finance literature, the variance premium is actually the negative of the variable that we
use. By switching the sign, our indicator increases with risk aversion, whereas the variance premium
becomes more negative with risk aversion.
8
“risk neutral” and “physical” expected volatility and demonstrate that the variance
premium is indeed increasing in risk aversion.
To decompose the VIX index into a risk aversion and an uncertainty component, we
first estimate the expected future realized variance. It is customary in the literature to do
so by projecting future realized monthly variances (computed using squared 5-minute
returns) onto a set of current instruments. We follow this approach using daily data on
monthly realized variances, the squared VIX, the dividend yield and the real three-month
T-bill rate. By using daily data, we gain considerable statistical power relative to the
standard methods employing end-of-month data. For example, forecasting models
estimated from daily data easily “beat” models using only end-of-month data, even for
end-of-month samples.
To select a good forecasting model, we conduct a horserace between a total of eight
volatility forecasting models. The first five models use OLS regressions with different
predictors: a one-variable model with either the past realized variance or the squared
VIX; a two-variable model with both the squared VIX and the past realized variance; a
three-variable model adding the past dividend yield; and a four-variable model adding the
past real three-month T-bill rate. We also consider three models that do not require
estimation: half-half weights on the past squared VIX and past realized variance; the past
realized variance; the past squared VIX. We consider two model selection criteria: outof-sample root-mean-squared error and mean absolute errors, and, for the estimated
models, stability (especially through the crisis period).
This procedure leads us to select a two-variable model where the squared VIX and the
past realized variance are used as predictors. The performance of the three and four
9
variable models is very comparable to this model, but the univariate estimated models
and the non-estimated models perform consistently and significantly worse. Moreover,
the model that we selected is the most stable of the well-performing forecasting models
we considered, with the coefficients economically and statistically unaltered during the
crisis period. In the online Appendix, we give a detailed account of the forecasting
horserace. The resulting coefficients from the two-variable projection are as follows: 5
RVARt=-0.00002 + 0.299 VIX2t-22 + 0.442 RVARt-22+et
(0.00012) (0.067)
(0.130)
(1)
The standard errors reported in parentheses are corrected for serial correlation using 30
Newey-West (1987) lags.
The fitted value from the two-variable projection is the estimated physical expected
variance (“uncertainty”). We use the logarithm of this estimate in our analysis and label it
UC. We call the difference between the squared VIX and UC “risk aversion” (the
logarithm of which is labeled as RA). We plot the risk aversion and uncertainty estimates
in Figure 2, along with 90% confidence intervals. 6 To construct the confidence bounds,
we retain the coefficients from the forecasting projection together with their asymptotic
covariance matrix. We then draw 100 alternative parameter coefficients from the
distribution of these estimates, which generates alternative RA and UC estimates. In
Section 3.2.4, we use these bootstrapped series to account for the sampling error in the
risk aversion and uncertainty estimates in our VARs.
2.2 Measuring Monetary Policy
5
This estimation was conducted using a winsorized sample but the estimation results for the nonwinsorized sample are in fact very similar.
6
The estimated uncertainty series is less “jaggedy” than it would be if only the past realized variance would
be used to compute it (as in Bollerslev, Tauchen and Zhou, 2009), which in turn helps smooth the risk
aversion process.
10
To measure the monetary policy stance, we use the real interest rate (RERA), i.e., the
Fed funds end-of-the-month target rate minus the CPI annual inflation rate. In Section
3.2.1, we consider alternative measures of the monetary policy stance for robustness. Our
first such measure is the Taylor rule residual, the difference between the nominal Fed
funds rate and the Taylor rule rate (TR rate). The TR rate is estimated as in Taylor
(1993):
TRt = Inft + NatRatet + 0.5 (Inft - TargInf) + 0.5 OGt
(2)
where Inf is the annual inflation rate, NatRate is the “natural” real Fed funds rate
(consistent with full employment), which Taylor assumed to be 2%, TargInf is a target
inflation rate, also assumed to be 2%, and OG (output gap) is the percentage deviation of
real GDP from potential GDP; with the latter obtained from the Congressional Budget
Office. As other alternative measures of the monetary policy stance, we consider the
nominal Fed funds rate instead of the real rate, and (the growth rate of) the monetary
aggregate M1, which is commonly assumed to be under tight control of the central bank.
We multiply M1 (growth) by minus one so that a positive shock to this variable
corresponds to monetary policy tightening, in line with all other measures of monetary
policy we use.
Measuring the monetary policy stance is challenging since late 2008, as the Fed funds
rate reached the zero lower bound (the Fed funds target was set in the range 0-0.25% as
of December 2008) and the Federal Reserve turned to unconventional monetary policies,
such as large-scale asset purchases. We approximate the “true” nominal Fed funds rate in
the period December 2008 - August 2010 by taking it to be the minimum between
0.125% (i.e., the mid-point of the 0-0.25% range) and the TR rate, estimated using
11
equation (2) above. Rudebusch (2009) has also advocated using the TR rate estimate as a
proxy for the “true” Fed funds rate post-2008.
In our analysis in Sections 4.1 and 4.2, we use monetary policy surprises derived
from Fed funds futures data. In Section 4.1, we rely on monetary policy surprises
proposed by Gürkaynak, Sack and Swanson (2005), henceforth GSS. 7 GSS compute the
monetary policy surprises as high-frequency changes in the futures rate around the
FOMC announcements. Their “tight” (“wide”) window estimates begin ten (fifteen)
minutes prior to the monetary policy announcement and end twenty (forty-five) minutes
after the policy announcement, respectively. The data span the period from January 1990
through June 2008. In Section 4.2, we use the unexpected change in the Fed funds rate on
a monthly basis, defined as the average Fed funds target rate in month t minus the onemonth futures rate on the last day of the month t-1. This approach follows Kuttner (2001)
and Bernanke and Kuttner (2005) (henceforth BK); see their equation (5). As pointed out
by BK, rate changes that were unanticipated as of the end of the prior month may well
include a systematic response to economic news, such as employment, output and
inflation occurring during the month. To overcome this problem, we calculate “cleansed”
monetary surprises that are orthogonal to a set of economic data releases. They are
calculated as residuals in a regression of the “simple” monetary policy surprise, onto the
unexpected component of the industrial production index, the Institute of Supply
Management Purchasing Managers Index (the ISM index), the payroll survey, and
unemployment (see Section 2.3 below for a description). Finally, in the regression, we
allow for heterogeneous coefficients before and after 1994, to take into account a change
in the reaction of the Fed to economic data releases, as documented in BK.
7
We are very grateful to R. Gürkaynak for sharing the data with us.
12
To extend the sample of monetary policy surprises until August 2010, we proceed in
two steps. First, we collect data on monetary policy surprises at the zero lower bound
from Wright (2011, Table 5). The surprises are based on a structural VAR in financial
variables at the daily frequency, starting in November 2008 (and calculated beyond the
end of our sample in August 2010). The shocks are positive (negative) when monetary
policy is unexpectedly accommodative (restrictive). They also have a standard deviation
equal to one by construction. For comparability with the GSS data, we rescale Wright’s
shocks by multiplying them by minus the standard deviation of the GSS’s shocks, before
appending them to the time series of GSS shocks. Second, to fill the gap between the data
from GSS (June 2008) and Wright (November 2008), we calculate monetary policy
surprises using Federal funds futures, following BK.
2.3 Measuring Business Cycle Variation
We use industrial production as our benchmark indicator of business cycle variation
at the monthly frequency. In a robustness exercise in Section 3.2.2, we also consider nonfarm employment and the ISM index as alternative business cycle indicators.
In Sections 4.1 and 4.2, we use data on economic news surprises following the
methodology in Ehrmann and Fratzscher (2004). 8 In our analysis, we rely on unexpected
components of news about the industrial production index, the ISM index, the payroll
survey, and unemployment. The unexpected component of each news release is
calculated as the difference between the released data and the median expectation
according to surveys. We use the Money Market Survey (MMS) for the period 19902001 and Bloomberg for the period 2002-2010. The shocks are standardized over the
sample period.
8
We are very grateful to M. Ehrmann and M. Fratzscher for sharing their dataset with us.
13
3. Structural Monetary VARs
In this Section, we follow the identified monetary VAR literature and interpret the
shock in the monetary policy equation as the monetary policy shock. Our benchmark
VAR, analyzed in Section 3.1, consists of four-variables: our risk aversion and
uncertainty proxies (rat and uct), the real interest rate as a measure of monetary policy
stance (mpt), and the log-difference of industrial production as a business cycle indicator
(bct). We consider alternative VARs as part of an extensive series of robustness checks
discussed in Section 3.2. The business cycle is the most important control variable as it is
conceivable that, for example, news indicating weaker than expected growth in the
economy may simultaneously make a cut in the Fed funds target rate more likely and
cause people to be effectively more risk averse, because their consumption moves closer
to their “habit stock,” or because they fear a more uncertain future.
3.1 Structural Four-Variable VAR
We collect the four variables of our benchmark VAR in the vector Zt = [bct, mpt, rat
uct]'. Without loss of generality, we ignore constants. Consider the following structural
VAR:
A Zt = Φ Zt-1 + εt
(3)
where A is a 4x4 full-rank matrix and E[εt εt'] = I. Of main interest are the dynamic
responses to the structural shocks εt. Of course, we start by estimating the reduced-form
VAR:
Zt = B Zt-1 + C εt
(4)
where B denotes A-1 Φ and C denotes A-1. Our estimated VARs include 3 lags. In the
Online Appendix, we include a table with some key reduced-form VAR statistics,
14
showing that the Schwarz criterion selects a one-lag VAR, whereas the Akaike criterion
selects three lags. Moreover, residual specification tests (Johansen, 1995) show that the
VAR with 3 lags clearly eliminates all serial correlation in the residuals.
We need 6 restrictions on the VAR to identify the system. Our first set of restrictions
uses a standard Cholesky decomposition of the estimate of the variance-covariance
matrix. We order the business cycle variable first, followed by the real interest rate, with
risk aversion and uncertainty ordered last. This captures the fact that risk aversion and
uncertainty, stock market based variables, respond instantly to monetary policy shocks,
while the business cycle variable is relatively more slow-moving. Effectively, this
imposes six exclusion restrictions on the contemporaneous matrix A, making it lowertriangular.
Our second set of restrictions combines five contemporaneous restrictions (also
imposed under the Cholesky decomposition above) with the assumption that monetary
policy has no long-run effect on the level of industrial production. This long-run
restriction is inspired by the literature on long-run money neutrality: money should not
have a long run effect on real variables. 9 Following Blanchard and Quah (1989), the
model with a long-run restriction (LR) involves a long-run response matrix, denoted by
D:
D  (I - B)-1 C.
(5)
The system with five contemporaneous restrictions and one long-run exclusion restriction
corresponds to the following contemporaneous matrix A and long-run matrix D: 10
9
Bernanke and Mihov (1998b) and King and Watson (1992) marshal empirical evidence in favor of money
neutrality using data on money growth and output growth.
10
Both identification schemes satisfy necessary and sufficient conditions for global identification of
structural vector autoregressive systems (see Rubio-Ramírez, Waggoner and Zha (2010)).
15
 a11
a
A =  21
a31

a 41
a12
a 22
0
0
a32
a 42
a33
a 43
0 
0 
and D =
0 

a 44 
 d11
d
 21
 d 31

d 41
0
d 22
d 32
d 42
d13
d 23
d 33
d 43
d14 
d 24 
d 34 

d 44 
(6)
We couch our main results in the form of impulse-response functions (IRFs
henceforth), estimated in the usual way, and focus our discussion on significant
responses. We compute 90% bootstrapped confidence intervals based on 1000
replications. Figure 3 graphs the complete results for the pre-crisis sample but in our
discussion we mention the corresponding full sample (till August 2010) results in
parentheses. A complete graph for the full sample, mimicking Figure 3, is reproduced in
the Online Appendix (Figure OA1).
Panels A and B show the interactions between the real rate (RERA) and risk aversion
(RA). A one standard deviation negative shock to the real rate, a 34 (42) basis points
decrease under both identification schemes, lowers risk aversion by 0.032 (0.019) in the
model with contemporaneous restrictions and by 0.035 (0.019) in the model with
contemporaneous/long-run restrictions after 9 (19) months. The impact reaches a
maximum of 0.056 (0.020) after 20 (23) months and remains significant up and till lag 40
(40) in both models. So, laxer monetary policy lowers risk aversion under both
identification schemes and in both the pre-crisis and full samples. The impact in the full
sample is quantitatively weaker, and is only statistically significant at the 68% confidence
level. However, such tighter confidence bounds are common in the VAR literature (see
Christiano, Eichenbaum, and Evans (1996), Sims and Zha (1999)). The impact of a one
standard deviation positive shock to risk aversion, equivalent to 0.347 (0.363) on the real
rate is mostly negative but not statistically significant in both models,
16
As Panel C shows, a positive shock to the real rate increases uncertainty (UC) in the
medium-run (after a short-lived negative impact), between lags 11 and 38 in the model
with contemporaneous restrictions and between lags 11 - 40 in the model with
contemporaneous/long-run restrictions. The maximum positive impact is 0.060 and 0.063
at lag 21 in the models with contemporaneous and contemporaneous/long-run
restrictions, respectively (in the full sample, the max impact is 0.018 and it is borderline
statistically insignificant even at the 68% confidence level). In the other direction,
reported in Panel D, the real rate decreases in the short-run following a positive one
standard deviation shock to uncertainty, equivalent to 0.244 (0.274). In both models, the
impact is (borderline) statistically insignificant in the pre-crisis sample (in the full
sample, the impact is significant at the 90% confidence level between lags 7 and 47,
reaching a maximum of 19 basis points at lag 18).
As for interactions with the business cycle variable (Panels E through J), a
contractionary monetary policy shock leads to a decline in industrial production growth
(DIPI) in the medium-run, but the impact is statistically insignificant in all specifications.
In the other direction, monetary policy reacts as expected to business cycle fluctuations: a
one standard deviation positive shock to industrial production growth, equivalent to 0.005
(0.006), leads to a higher real rate. Specifically, in the model with contemporaneous
restrictions, the real rate increases by a maximum of 14 (15) basis points after 6 (11)
months, with the impact being significant between lags 1 and 20 (at lag 1, and between
lags 3-31). The impact is also positive in the model with contemporaneous/long-run
restrictions but it is not statistically significant. Interactions between risk aversion and
industrial production growth are mostly statistically insignificant. Positive uncertainty
17
shocks lower industrial production growth between lags 6-15 (2-18), while the impact in
the opposite direction is statistically insignificant. This is consistent with the analysis in
Bloom (2009), who found that uncertainty shocks generate significant business cycle
effects, using the VIX as a measure of uncertainty. 11
Finally, increases in risk aversion predict future increases in uncertainty under both
identification schemes (Panel L). Uncertainty has a positive, albeit short-lived effect on
risk aversion (Panel K).
Our main result for the pre-crisis sample is that monetary policy has a medium-run
statistically significant effect on risk aversion. This effect is also economically
significant. In Figure 4, we show what fraction of the structural variance of the four
variables in the VAR is due to monetary policy shocks. They account for over 20% of the
variance of risk aversion at horizons longer than 37 and 29 months in the models with
contemporaneous and contemporaneous/long-run restrictions, respectively. Monetary
policy shocks also increase uncertainty and Figure 4 shows that they are only marginally
less important drivers of the uncertainty variance than they are of the risk aversion
variance. Finally, while monetary policy appears to relax policy in response to both risk
aversion and uncertainty shocks, these effects are statistically weaker.
The results for the full sample including the crisis period overall confirm our results
for the pre-crisis sample but are less statistically significant. Given the measurement
problems mentioned before, and the rather extreme volatility the VIX experienced, this is
not entirely surprising.
11
Popescu and Smets (2009) analyze the business cycle behavior of measures of perceived uncertainty and
financial risk premia in Germany. They find that financial risk aversion shocks are more important in
driving business cycles than uncertainty shocks. Gilchrist and Zakrajšek (2011) document that innovations
to the excess corporate bond premium, a proxy for the time-varying price of default risk, cause large and
persistent contractions in economic activity.
18
3.2 Robustness
In this subsection, we consider five types of robustness checks: 1) measurement of the
monetary policy stance; 2) measurement of the business cycle variable; 3) alternative
orderings of variables; 4) accounting for the sampling error in RA and UC estimates; and
5) conducting the analysis using a six variable monetary VAR with the Fed funds rate
and price level measures CPI and PPI entering as separate variables. We also verified that
our results remain robust to the use of both shorter and longer VAR lag-lengths. We
estimated a VAR with 1 lag, as selected by the Schwarz criterion, as well as a VAR with
4 lags (we did not go beyond four lags as otherwise the saturation ratio, the ratio of data
points to parameters, drops below 10). Our results were unaltered.
3.2.1 Measuring Monetary Policy
Table 2 reports summary statistics on the interaction of alternative measures of the
monetary policy stance with risk aversion (Panel A) and with uncertainty (Panel B). The
results confirm that a looser monetary policy stance lowers risk aversion in the short to
medium run. This effect is persistent, lasting for about two years. In some cases, the
immediate effect has the reverse sign however. In the other direction, monetary policy
becomes laxer in response to positive risk aversion shocks but the effect is statistically
significant in less than half the cases. As for the effect of monetary policy on uncertainty,
monetary tightening increases uncertainty in the medium run but this effect is not
significant when using the Fed fund rate. In the other direction, higher uncertainty leads
to laxer monetary policy in all specifications but the effect is only significant when using
the Fed fund rate under contemporaneous identifying restrictions.
3.2.2 Measuring Business Cycle Variation
19
We consider the log-difference of employment and the log of the ISM index as
alternative business cycle indicators. Unlike industrial production and employment, the
ISM index is a stationary variable, implying that VAR shocks do not have a long run
effect on it. Our long-run restriction on the effect of monetary policy is thus stronger
when applied to the ISM: it restricts the total effect of monetary policy on the ISM to be
zero. Nevertheless, our main results from Section 3.1 are confirmed for each specification
with an alternative business cycle variable. We present a full set of IRFs (the equivalent
of Figure 3) for the VARs with the log-difference of employment and the log of the ISM
index as business cycle measures in the Online Appendix (Figures OA4 and OA5,
respectively).
3.2.3 Alternative Orderings of Variables
In one alternative ordering, we reverse the order of risk aversion and uncertainty in
our benchmark VAR. In another robustness check, we order the real interest rate last,
thus allowing it to respond instantaneously to RA and UC shocks. We consistently find
that looser monetary policy lowers risk aversion and uncertainty in a statistically
significant fashion in the medium-run. In the other direction, the effects are less robust. In
the specification with RA and UC reversed, monetary policy mostly responds to UC
shocks, but the response to RA shocks is statistically insignificant. In the specification
with RERA ordered last, monetary policy responds to both positive RA and UC shocks
by loosening its stance, and the effect is statistically significantly different from zero. We
present a full set of IRFs for the reversed ordering of RA and UC and for the
specification with RERA ordered last in the Online Appendix (Figures OA6 and OA7,
respectively).
20
3.2.4 Sampling Error in RA and UC
We check that our VAR results are robust to accounting for the sampling error in the
RA and UC estimation. We draw 100 alternative RA and UC series from the distribution
of RA and UC estimates (as described in section 2.1), and feed those into our
bootstrapped VAR. We estimate 100 VAR replications per set of alternative RA and UC
series. We then construct the usual 90% confidence bounds. The results are very similar
to those obtained without taking uncertainty surrounding RA and UC estimates into
account, and are presented in the Online Appendix (Figure OA8).
3.2.5 Six-variable Monetary VAR
We also estimate a six-variable monetary VAR following Christiano, Eichenbaum
and Evans (1999) and featuring the nominal Fed funds rate as the measure of monetary
policy stance and price level measures CPI and PPI as additional variables. 12 To identify
monetary policy shocks, we use a Cholesky ordering with CPI and industrial production
ordered first, followed by the Fed funds rate and PPI, and risk aversion and uncertainty
ordered last.
We present impulse-responses to monetary policy shocks in Figure 5. Again, we
discuss results for the pre-crisis sample, but summarize the full sample results in
parentheses. A positive monetary policy shock corresponds to a 15 basis points (30 in the
full sample) increase in the Fed funds rate. A contractionary monetary shock leads to a
statistically significant decrease in the CPI between lags 3 and 23 (2 and 8) and in the PPI
between lags 23 and 50 (effect insignificant in the full sample). Furthermore, in the precrisis sample, industrial production declines following a monetary contraction after about
12
We estimate the model with four lags, as suggested by the Akaike criterion. All variables are in
logarithms except for the Fed funds rate. Note that industrial production now enters the VAR in levels.
21
10 months, though the effect is not statistically significant (similarly, the effect is
insignificant in the full sample). Importantly, the reactions of both risk aversion and
uncertainty are remarkably similar to those uncovered in our benchmark four-variable
VARs. Looser monetary policy decreases risk aversion by 0.024 (0.023) after 12 (19)
months. The effect reaches a maximum of 0.040 (0.025) at lag 23 (24), and remains
statistically significant till lag 35 (till lag 37, significant under 68% confidence bounds).
The effects remain economically important as monetary policy shocks account for over
12% (3%) of the variance of risk aversion at horizons longer than 40 months (see Panel F
of Figure 5) but these percentages are nonetheless lower than in our four-variable VAR.
As for uncertainty, a higher Fed funds rate increases uncertainty between lags 12 and 31
(16 and 36), with the maximum impact of 0.040 (0.033) at lag 23 (22), which is also
consistent with our previous findings. In non-reported results, monetary policy responds
to both positive RA and UC shocks by loosening its stance. The effect is statistically
significant under 90% confidence bounds between lags 2 and 7 (6 and 15) for RA and
between lags 5 and 26 (3 and 20) for UC.
4. Alternative Identification of Monetary Policy Shocks
In this Section, we employ two alternative methodologies to identify monetary policy
shocks: 1) monetary surprises based on high-frequency Fed funds futures and 2) monthly
surprises calculated using daily Fed funds futures.
4.1 Identification using High-Frequency Fed Funds Futures
Our VAR set-up to identify monetary policy shocks and their structural relationship
with risk aversion and uncertainty follows the Sims (1980, 1998) identification tradition.
With financial market values changing continuously during the month, the use of
22
monthly data for this purpose certainly may cast some doubt on this identification
scheme. We therefore use an alternative identification methodology that makes use of
high frequency data to infer restrictions on the monthly VAR. The approach, inspired by
and building on the procedure described in D’Amico and Farka (2011), consists of three
steps.
In the first step, we measure the structural monetary policy and business cycle shocks
directly. For monetary policy, we rely on a well-established literature that uses high
frequency changes in Fed funds futures rates (see, for example, Faust, Swanson and
Wright, 2004) to measure monetary policy shocks, and we detailed their measurement in
Section 2. Likewise, for business cycle shocks, we use news announcements. Under
certain assumptions, these shocks can be viewed as measuring the structural shocks εt in
the VAR. For monetary policy shocks, this is plausible because usually only one shock
occurs per month, and the use of high frequency futures data helps ensure that the
identified shock is plausibly orthogonal to other shocks. As to the business cycle shocks,
there are a number of potentially important complicating issues, such as the correlation
between the different news announcements and the structural shock to the actual business
cycle variable used in the VAR, and the scale of the shocks when more than one occurs
within a particular month. However, these issues become moot when business cycle
shocks do not generate significant contemporaneous effects on our financial variables,
which ends up being the case.
In the second step, we measure the high frequency effects of monetary policy and
economic news surprises on risk aversion and uncertainty. We regress daily changes in
risk aversion and uncertainty (as proxies for unexpected changes to these variables),
23
respectively, on the monetary policy surprises based on high-frequency futures (using the
“tight” window shocks) 13 and the four monthly economic news surprises concerning
industrial production (ΔIP), the ISM index (ΔISM), non-farm payroll and employment
(ΔEMP), as described in Section 2.3. 14 The resulting coefficients for the pre-crisis sample
(with heteroskedasticity-robust standard errors in parentheses) are as follows:
ΔRAt = -0.039 + 0.047 ΔMPt – 0.005 ΔIPt – 0.004 ΔISMt – 0.004 ΔEMPt
(0.007) (0.020)
(0.014)
(0.016)
(0.017)
(7)
ΔUCt = -0.009 + 0.013 ΔMPt + 0.002 ΔIPt – 0.002 ΔISMt – 0.008 ΔEMPt
(0.003) (0.010)
(0.005)
(0.005)
(0.011)
(8)
The coefficients on the business cycle news surprises are not statistically different
from zero and economically small. However, the responses to the monetary policy
surprises are quantitatively larger and statistically significant at the 5% level for RA and
at the 16% level for UC. The coefficients on ΔMP give us direct evidence on the
contemporaneous responses of RA and UC to structural disturbances in MP. We already
note that these responses confirm that risk aversion reacts positively to monetary policy
shocks and does so more strongly than uncertainty. By the same token, we conclude that
the contemporaneous responses of RA and UC to a business cycle shock in our VARs are
equal to zero.
In the third step, we use the estimates of structural responses of RA and UC to
monetary policy and business cycle shocks in our VAR analysis. This requires a number
of additional assumptions. In particular, we assume that there are no further policy or
business cycle shocks during the month and thus that the monthly shock equals the daily
13
Results for the monetary policy surprises calculated using the “wide” window are very similar.
We treat both the non-farm payroll and the negative of the unemployment surprises as news about
employment (ΔEMP) as they have similar information content. Whenever then come out on the same day
(which is mostly the case), we sum them up.
14
24
shock identified from high frequency data. Furthermore, we assume that the
contemporaneous daily change in risk aversion and uncertainty identifies the monthly
change in unexpected risk aversion and uncertainty due to these policy and business cycle
shocks. In other words, we assume that the high-frequency regressions effectively yield
four coefficients in the A-1 matrix of our structural VAR. Because we need 6 restrictions
in total, we impose two more restrictions from a Cholesky ordering to achieve
identification. In one identification scheme (Model 1), we impose that both industrial
production and monetary policy do not instantaneously respond to RA; in another
scheme, we impose the same restrictions on the reaction to UC (Model 2). 15 Because the
identifying assumptions on monetary policy shocks have more support in the extant
literature than the assumptions we made regarding the business cycle shocks, we also
consider a robustness check where we only impose the high-frequency responses to
monetary policy surprises in the monthly VAR. We then need four additional restrictions
from a Cholesky ordering to complete identification and use the three contemporaneous
restrictions in the BC equation (the usual assumption on sluggish adjustment of macro to
financial data) and a zero response by monetary policy to either RA (Model 3) or UC
(Model 4).
For the full sample, all the estimated coefficients in the second step regressions are
not statistically different from zero, but the effect of monetary policy shocks on risk
aversion is again positive with a t-stat of close to 1. If we were to impose that the
contemporaneous responses of RA and UC to monetary policy and business cycle shocks
are all equal to zero, models 1 and 2 would be under-identified. We thus estimate only
15
Imposing zero-response restrictions to RA and UC in the BC equation would lead to an under-identified
model.
25
models 3 and 4 for the full sample, i.e., imposing the zero-response to monetary policy
surprises from the second step regression, plus three contemporaneous restrictions in the
BC equation and a zero response by monetary policy to either RA or UC. As before, we
report results for the full sample in parentheses (and present IRFs in the Online
Appendix, Figure OA2).
For the two models imposing four restrictions from the first step, we present impulseresponses to monetary policy shocks in Figure 6. Looser monetary policy (corresponding
to a 29 basis points decrease in the real rate) lowers risk aversion on impact and between
lags 8 and 12, with a maximum impact of 0.055 in the model with no contemporaneous
response of business cycle and monetary policy to RA. The maximum impact is 0.061
and the effect is significant between lags 7 and 17 in the model with no contemporaneous
response of business cycle and monetary policy to UC.
As Panel B shows, a positive shock to the real rate increases uncertainty on impact in
the model with no contemporaneous response of the business cycle and monetary policy
to RA. The effect is positive but not statistically significant in the medium run. In the
model with no contemporaneous response of business cycle and monetary policy to UC,
the positive effect of the real rate shock on uncertainty is statistically significant on
impact and between lags 10-14, with a maximum impact of 0.059 at lag 14.
Lastly, the impact of monetary policy on industrial production growth is not
statistically significant (Panel C). Note that with different measures for the business
cycle, such as employment, the VAR does produce the expected and statistically
significant response to monetary policy.
26
For the two models imposing two restrictions (for the monetary policy shocks only)
from the first step, we present impulse-responses to monetary policy shocks in Figure 7.
Looser monetary policy, corresponding to a 33 (42) basis points decrease in the real rate,
lowers risk aversion on impact and between lags 4-36 (14-37, significant at 68%
confidence bounds), with a maximum impact of 0.055 at lag 15 (0.023 at lag 17) both in
the model with no contemporaneous response of monetary policy to RA and in the model
with no contemporaneous response of monetary policy to UC (and the three zero
restrictions in the BC equation).
As Panel B shows, a positive shock to the real rate increases uncertainty on impact
and between lags 4-36, with a maximum impact of 0.058 at lag 16 both in the model with
no contemporaneous response of monetary policy to RA and in the model with no
contemporaneous response of monetary policy to UC (and the three zero restrictions in
the BC equation). (The impact of the monetary policy shock on uncertainty is positive but
not statistically significant at 68% confidence bounds for the full sample.)
Lastly, the impact of monetary policy on industrial production growth is again not
statistically significant (Panel C).
4.2 Identification using Daily Fed Funds Futures
In this section, we adopt the approach of Bernanke and Kuttner (2005) to study the
dynamic response of risk aversion and uncertainty to monetary policy. The key feature of
their approach is the calculation of a monthly monetary policy surprise using Federal
funds futures contracts. This variable identifies the monetary policy shock and is included
in the VAR as an exogenous variable. The endogenous variables in the VAR are RA, UC
and the log difference of industrial production (DIPI).
27
We present impulse-responses to “cleansed” monetary policy shocks 16 in Figure 7 for
the pre-crisis sample and in the Online Appendix for the full sample (Figure OA3). As
before, below we discuss results for the full sample in parentheses. The results generally
confirm that monetary policy surprises have a positive impact on both RA and UC, and
have the expected negative effect on industrial production. However, the results are less
strong statistically than under our other identification schemes.
A one standard deviation negative shock to the “cleansed” surprise, equivalent to 8.6
basis points (9 basis points), decreases RA on impact by 0.061 and UC by 0.054
(decreases RA by 0.053 and UC by 0.026). The IRFs are significant on impact at the 80%
confidence level for RA and at the 70% level for UC (at the 80% level for RA; not
statistically significant for UC). These results are robust to the use of alternative business
cycle indicators (non-farm employment and the ISM index).
5. Conclusions
A number of recent studies point at a potential link between loose monetary policy
and excessive risk-taking in financial markets. Rajan (2006) conjectures that in times of
ample liquidity supplied by the central bank, investment managers have a tendency to
engage in risky, correlated investments. To earn excess returns in a low interest rate
environment, their investment strategies may entail risky, tail-risk sensitive and illiquid
securities (“search for yield”). Moreover, a tendency for herding behavior emerges due to
the particular structure of managerial compensation contracts. Managers are evaluated
vis-à-vis their peers and by pursuing strategies similar to others, they can ensure that they
do not under perform. This “behavioral” channel of monetary policy transmission can
16
The monetary policy surprise is standardized by subtracting the mean and dividing by the standard
deviation.
28
lead to the formation of asset prices bubbles and can threaten financial stability. Yet,
there is no empirical evidence on the links between risk aversion in financial markets and
monetary policy.
This article has attempted to provide a first characterization of the dynamic links
between risk, uncertainty and monetary policy, using a simple vector-autoregressive
framework. We decompose implied volatility into two components, risk aversion and
uncertainty, and study the interactions between each of the components and monetary
policy under a variety of identification schemes for monetary policy shocks. We
consistently find that lax monetary policy increases risk appetite (decreases risk aversion)
in the future, with the effect lasting for more than two years and starting to be significant
after nine months. The effect on uncertainty is similar but the immediate response of
uncertainty to monetary policy shocks in high frequency regressions is weaker than that
of risk aversion. Conversely, high uncertainty and high risk aversion lead to laxer
monetary policy in the near-term future but these effects are not always statistically
significant. These results are robust to controlling for business cycle movements.
Consequently, our VAR analysis provides a clean interpretation of the stylized facts
regarding the dynamic relations between the VIX and the monetary policy stance
depicted in Figure 1. The primary component driving the co-movement between past
monetary policy stance and current VIX levels (first column of Figure 1) is risk aversion
but uncertainty also reacts to monetary policy. Both components of the VIX lie behind
the negative relation in the opposite direction (second column of Figure 1).
We hope that our analysis will inspire further empirical work and research on the
exact theoretical links between monetary policy and risk-taking behavior in asset
29
markets. A recent literature, mostly focusing on the origins of the financial crisis, has
considered a few channels that deserve further scrutiny. Adrian and Shin (2008) stress the
balance sheets of financial intermediaries and repo growth; Adalid and Detken (2007)
and Alessi and Detken (2008) stress the buildup of liquidity through money growth and
Borio and Lowe (2002) emphasize rapid credit expansion. 17 Recent work in the
consumption-based asset pricing literature attempts to understand the structural sources
of the VIX dynamics (see Bekaert and Engstrom (2010), Bollerslev, Tauchen and Zhou
(2009), Drechsler and Yaron (2011)). Yet, none of these models incorporates monetary
policy equations. In macroeconomics, a number of articles have embedded term structure
dynamics into the standard New-Keynesian workhorse model (Bekaert, Cho, Moreno
(2010), Rudebusch and Wu (2008)), but no models accommodate the dynamic
interactions between monetary policy, risk aversion and uncertainty, uncovered in this
article.
The policy implications of our work are potentially very important. Because monetary
policy significantly affects risk aversion and uncertainty and these financial variables
may affect the business cycle, we seem to have uncovered a monetary policy
transmission mechanism missing in extant macroeconomic models. Fed chairman
Bernanke (see Bernanke (2002)) interprets his work on the effect of monetary policy on
the stock market (Bernanke and Kuttner (2005)) as suggesting that monetary policy
would not have a sufficiently strong effect on asset markets to pop a “bubble” (see also
Bernanke and Gertler (2001), Gilchrist and Leahy (2002), and Greenspan (2002)).
17
In fact, we considered the effects of repo, money and credit growth on our results by including them in a
four-variable VAR together with RA, UC, and RERA (replacing the BC variable). We consistently found
that the direct effect of monetary policy on risk aversion and uncertainty we uncovered in our benchmark
VARs is preserved.
30
However, if monetary policy significantly affects risk appetite in asset markets, this
conclusion may not hold. If one channel is that lax monetary policy induces excess
leverage as in Adrian and Shin (2008), perhaps monetary policy is potent enough to weed
out financial excess. Conversely, in times of crisis and heightened risk aversion,
monetary policy can influence risk aversion and uncertainty in the market place, and
therefore affect real outcomes.
31
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monetary policy in a VAR make sense," International Economic Review 39(4), pp. 933941.
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67(5), pp. 1113-1155.
Taylor, J. B. (1993). “Discretion Versus Policy Rules in Practice,” Carnegie-Rochester
Conference Series on Public Policy 39, pp. 195–214.
Thorbecke, W. (1997). “On Stock Market Returns and Monetary Policy,” Journal of
Finance 52 (2), pp. 635-654.
Whaley, R. E. (2000). “The Investor Fear Gauge,” Journal of Portfolio Management,
Spring, pp. 12-17.
Wright, J. H. (2011). “What does Monetary Policy do to Long-Term Interest Rates at the
Zero Lower Bound?” working paper, Johns Hopkins University.
35
Figure 1: Cross-correlogram LVIX RERA
Notes: The first column presents the (lagged) cross-correlogram between the log of the VIX (LVIX) and
past values of the real interest rate (RERA). The second column presents the (lead) cross-correlogram
between LVIX and future values of RERA. Dashed vertical lines indicate 95% confidence intervals for the
cross-correlation. The third column presents the cross-correlation values. The index i indicates the number
of months either lagged or led for the real interest rate variable.
36
Table 1: Description of variables
Name
Label
Description (source)
Consumer price index
CPI
Consumer price index, all items
Dividend yield
Dividend yield of the Standard & Poor 500
index
Fed funds rate
FED
Fed funds target rate
Implied volatility
LVIX
Implied volatility of options on the
Standard & Poor 500 index, Log (VIX
/ 12 )
(Growth of) Industrial production
(D)IPI
Log (difference of) total industrial
production index
ISM index
ISM
ISM Purchasing Managers index
M1 money aggregate growth
M1
Month-on-month growth of M1
(Growth of) Non-farm employment
(D)EMP
Log (difference of) non-farm employment
Producer price index
PPI
Producer price index for intermediate
materials
Real interest rate
RERA
FED minus annual CPI inflation rate
Realized variance
RVAR
Realized variance [see Section 2.1]
Risk aversion
RA
Log (risk aversion) [see Section 2.1]
Taylor Rule deviations
TRULE
FED minus Taylor rule rate [see Section
2.2]
Three-month T-bill
Uncertainty (conditional variance)
Secondary market yield
UC
Log (uncertainty) [see Section 2.1]
Notes: Monthly frequency, end-of-the-month data (seasonally adjusted where applicable). Unless otherwise
mentioned, the data are from Thomson Datastream.
37
Figure 2: Risk aversion and uncertainty
Panel A: Risk aversion
120
Russian / LTCM
Crisis
Euro Area
Debt Crisis
Lehman
Aftermath
100
Asian
Crisis
80
Corporate
Scandals
Gulf War I
60
09/11
High Risk
Appetite
Mexican
Crisis
40
20
0
2010m1
2009m1
2008m1
2007m1
2006m1
2005m1
2004m1
2003m1
2002m1
2001m1
2000m1
1999m1
1998m1
1997m1
1996m1
1995m1
1994m1
1993m1
1992m1
1991m1
1990m1
Panel B: Uncertainty
180
Lehman
Aftermath
160
140
Russian / LTCM
Crisis
120
Corporate
Scandals
100
09/11
Asian
Crisis
80
Low
Uncertainty
Gulf War I
60
Euro Area
Debt
Mexican
Crisis
40
20
0
2010m1
2009m1
2008m1
2007m1
2006m1
2005m1
2004m1
2003m1
2002m1
2001m1
2000m1
1999m1
1998m1
1997m1
1996m1
1995m1
1994m1
1993m1
1992m1
1991m1
1990m1
Notes: Plots of risk aversion and uncertainty for our sample period (January 1990 – August 2010).
38
Figure 3: Structural-form IRFs for the 4-variable VAR (DIPI, RERA, RA, UC)
Panel A: Impulse RERA, response RA
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Panel B: Impulse RA, response RERA
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Panel C: Impulse RERA, response UC
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Panel D: Impulse UC, response RERA
Contemporaneous restrictions
Contemporaneous/long-run restrictions
39
Panel E: Impulse RERA, response DIPI
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Panel F: Impulse DIPI, response RERA
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Panel G: Impulse RA, response DIPI
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Panel H: Impulse DIPI, response RA
Contemporaneous restrictions
Contemporaneous/long-run restrictions
40
Panel I: Impulse UC, response DIPI
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Panel J: Impulse DIPI, response UC
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Panel K: Impulse RA, response UC
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Panel L: Impulse UC, response RA
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Notes: Estimated structural impulse-response functions (black lines) and 90% bootstrapped confidence
intervals (grey dashed lines) for the model with 3 lags (selected by Akaike), based on 1000 replications.
Panels on the left present results of the model with contemporaneous (Cholesky) restrictions, panels on the
right present results of the model with contemporaneous/long-run restrictions.
41
Figure 4: Structural variance decompositions
Impact of RERA shocks
Contemporaneous restrictions
Contemporaneous/long-run restrictions
Notes: Fractions of the structural variance due to RERA shocks for the four variables DIPI, RERA, RA and
UC (model with 3 lags, selected by Akaike). The panel on the left presents results of the model with
contemporaneous restrictions, the panel on the right presents results of the model with
contemporaneous/long-run restrictions.
42
Table 2: Robustness to monetary policy measures
Panel A: Monetary policy instrument – risk aversion pair
MP instrument
Real interest rate
- COR
- CLR
Taylor rule
- COR
- CLR
Fed funds rate
- COR
- CLR
(-1) M1 growth
- COR
- CLR
(-1) M1
- COR
Impulse MP, response RA
Impulse RA, response MP
sign
significant from-to (month)
sign
significant from-to (month)
/+
/+
0 - 2 (), 9 - 40 (+)
2 (), 9 – 40 (+)


-12 - 24
/+
+
0 (), 8 - 44 (+)
9 – 44


---
+
+
21 - 38
19 - 38


0 - 10
0-7
/+
/+
---


---
+
5 - 26

--
Panel B: Monetary policy instrument – uncertainty pair
MP instrument
Real interest rate
- COR
- CLR
Taylor rule
- COR
- CLR
Fed funds rate
- COR
- CLR
(-1) M1 growth
- COR
- CLR
(-1) M1
- COR
Impulse MP, response UC
Impulse UC, response MP
sign
significant from-to (month)
sign
significant from-to (month)
/+
+
0 - 1 (), 11 - 38 (+)
0 - 3 (), 11 - 40 (+)


---
/+
/+
0 - 1 (), 15 - 42 (+)
0 - 1 (), 17 - 43 (+)


---
/+
/+
---


14 - 31
--
+
+
3 - 12
3 - 12


---
+
5 - 19

--
Notes: Table 4 summarizes results for the interactions between monetary policy (as represented by four
different measures) and risk aversion (RA) in Panel A and between monetary policy and uncertainty (UC)
in panel B in the four-variable model with DIPI, MP, RA and UC. The MP measures considered are: real
rate, Taylor rule deviations, Fed funds rate, the negative of the M1 growth. Each Panel lists the range of
months for which impulse-response functions (VARs with contemporaneous (COR) and
contemporaneous/long-run (CLR) restrictions, respectively) were statistically significant within the 90%
confidence interval in the direction indicated in the column “sign”. The last row in each panel considers a
specification with M1 and industrial production both entering in levels rather than growth rates (COR
restrictions only).
43
Figure 5: Monetary policy shock in the 6-variable VAR (CPI EMP FED PPI RA UC)
Panel A: Impulse FED, response CPI
Panel B: Impulse FED, response PPI
Panel C: Impulse FED, response RA
Panel D: Impulse FED, response UC
Panel E: Impulse FED, response IPI
Panel F: Structural Variance Decompositions
Notes: Panels A-E: Estimated structural impulse-responses (black lines) to a monetary policy shock in the
6-variable model and 90% bootstrapped confidence intervals (dashed grey lines), for the model with 4 lags
(selected by Akaike), based on 1000 replications. Panel F: Fractions of the structural variance due to FED
shocks for the six variables.
44
Figure 6: Identification using high-frequency futures and business cycle news
announcements
Panel A: Impulse MP, response RA
Model 1
Model 2
Panel B: Impulse MP, response UC
Model 1
Model 2
Panel C: Impulse MP, response DIPI
Model 1
Model 2
Notes: Estimated structural impulse-response functions (black lines) and 90% bootstrapped confidence
intervals (grey dashed lines) for the model with 3 lags (selected by), based on 1000 replications. Four
restrictions are derived from high frequency data. Panels on the left present results of Model 1 (BC and MP
do not respond instantaneously to RA), panels on the right present results of Model 2 (BC and MP do not
respond instantaneously to UC).
45
Figure 7: Identification using high-frequency futures
Panel A: Impulse MP, response RA
Model 3
Model 4
Panel B: Impulse MP, response UC
Model 3
Model 4
Panel C: Impulse MP, response DIPI
Model 3
Model 4
Notes: Estimated structural impulse-response functions (black lines) and 90% bootstrapped confidence
intervals (grey dashed lines) for the model with 3 lags (selected by), based on 1000 replications. Panels on
the left present results of Model 3, panels on the right present results of Model 4. Both models assume zero
contemporaneous responses of the BC shocks to the other variables. Model 3 (Model 4) assumes that
monetary policy does not instantaneously react to RA (UC).
46
Figure 8: Identification using daily futures
Panel A: Impulse MP, response RA
Panel B: Impulse MP, response UC
Panel C: Impulse MP, response DIPI
Notes: Estimated impulse-response functions to “cleansed” MP surprise (black lines) and 90%
bootstrapped confidence intervals (grey dashed lines).
47
Appendix: The VIX and Risk
To obtain intuition on how the VIX is related to the actual (“physical”) expected
variance of stock returns and to risk preferences, we analyze a one-period discrete state
economy. Imagine a stock return distribution with three different states xi , as follows:
Good state: x g    a with probability (1  p ) / 2 ,
Bad state : xb    a with probability (1  p ) / 2 ,
Crash state: xc  c with probability p ,
where   0 , a  0 and p  0 are parameters to be determined. We set them to match
moments of US stock returns - the mean, the variance (standard deviation) and the
skewness - while fixing the crash return at an empirically plausible number.
The mean is given by:
X 
1 p
1 p
xg 
xb  pc  (1  p )   pc .
2
2
(1)
The variance is given by:
V 2 
1 p
1 p
(  a  X ) 2 
(   a  X ) 2  p (c  X ) 2
2
2
(2)
and the skewness ( Sk ) by:
3
2
V Sk 
1 p
1 p
(  a  X ) 3 
(   a  X ) 3  p (c  X ) 3 .
2
2
(3)
Consider an investor with power utility over wealth in a one-period world, so that in
equilibrium she invests her entire wealth in the stock market:
~
 (W0 R )1 
~
U (W )  E 
(4)
,
 1  
~
where R is the gross return on the stock market, W0 is initial wealth and  is the
coefficient of relative risk aversion.
The “pricing kernel” in this economy is given by marginal utility, denoted by m , and
~
is proportional to R . Hence, the stochastic part of the pricing kernel moves inversely
with the return on the stock market. When the stock market is down, marginal utility is
relatively high and vice versa.
48
The physical variance of the stock market is exogenous in this economy, and is
simply given by V . This variance is computed using the actual probabilities. The VIX
represents the “risk-neutral” conditional variance. It is computed using the so-called
“risk-neutral probabilities,” which are simply probabilities adjusted for risk.
In particular, for a general state probability  i for state i , the risk-neutral probability
is:
mi
R 
 i i .
E m
E m
 iRN   i
(5)
So, for a given  , we can easily compute the risk-neutral probabilities since Ri  xi  1 .
For an economy with K states, the risk-neutral variance is then given by:
K
VIX 2    iRN ( xi  X ) 2
(6)
i 1
and the variance premium is:
K
VP  VIX 2  V   ( iRN   i )( xi  X ) 2 .
(7)
i 1
In our economy, the risk-neutral probability puts more weight on the crash state and
the crash state induces plenty of additional variance, rendering the variance premium
positive. The higher is risk aversion, the more weight the crash state gets, and the higher
the variance premium will be. The expression for the variance premium has a particularly
simple form:
VP  ( gRN 
where  gRN 
1 p
1 p
)( x g  X ) 2  ( bRN 
)( xb  X ) 2  ( cRN  p )( xc  X ) 2
2
2
(8)
1  p (   a  1) 
1  p (   a  1) 
(c  1) 
,  bRN 
and  cRN  p
.
E m
E m
E m
2
2
Numerical Examples
Suppose the statistics to match are as follows: X  10 % ,   15% , both on an
annualized basis; and Sk  1 on a monthly basis. We set c  25% (a monthly number).
This crash return is in line with the stock market collapses in October 1987 and October
2008. The implied crash probability to match the skewness coefficient of -1 is given
by p  0.5 % . With a monthly investment horizon, the crash probability implies a crash
every 200 months, or roughly once every two decades. Panel A of Appendix Table 1
49
provides, for different values of the coefficient of relative risk aversion  , the values for
the VIX on an annualized basis in percent (VIX), the log of the VIX on a monthly basis
(LVIX), i.e., log(VIX / 12 ), the annualized variance premium (VP), and our risk
aversion proxy computed on a monthly basis (RA), i.e., log(VIX 2 / 12   2 / 12 ). Note
that the variance premium and our risk aversion measure are monotonically increasing in
the coefficient of relative risk aversion  .
In structural models,  is typically assumed to be time-invariant, and the time
variation in the variance premium is generated through different mechanisms. For
example, in Drechsler and Yaron (2011), who formulate a consumption-based asset
pricing model with recursive preferences, the variance premium is directly linked to the
probability of a “negative jump” to expected consumption growth. The analogous
mechanism in our simple economy would be to decrease the skewness of the return
distribution by increasing the crash probability p . This obviously represents “risk”
instead of “risk aversion”. Yet, it is the interaction of risk aversion and skewness that
gives rise to large readings in our risk aversion proxy. To illustrate, let us consider an
example with lower skewness. Setting skewness equal to -2 requires a higher crash
probability of p  1 % . Panel B of Appendix Table 1 shows that the VIX increases, and
increases more the higher the coefficient of relative risk aversion, both in absolute and in
relative terms. The variance premium roughly doubles for all  levels, whereas our risk
aversion proxy increases by about 0.7.
In Bekaert and Engstrom (2010), when a recession becomes more likely, the
representative agent also becomes more risk averse through a Campbell-Cochrane
(1999)-like external habit formulation. The recession fear then induces high levels of the
VIX. We can informally illustrate such a mechanism in our one-period model. Imagine
that the utility function is over wealth relative to an exogenous benchmark wealth level
Wbm . Normalizing the initial wealth W0 to 1, the pricing kernel is now given by
R~  W 

bm


~ ~
, and the coefficient of relative risk aversion is  R / R  Wbm . Consequently,
~
risk aversion is state dependent and increases as R decreases towards the benchmark
level. It is easy to see how a dynamic version of this economy, for instance with a slow-
50
moving Wbm , could generate risk aversion that is changing over time as return
realizations change the distance between actual wealth and the benchmark wealth level.
To illustrate this mechanism, Panel C considers three different benchmark levels for
Wbm (0.05, 0.25 and 0.5) with  fixed at 4 and Sk  1 , implying p  0.5 % . The
second column shows expected relative risk aversion in the economy (CRRA), weighting
the three possible realizations for risk aversion with the actual state probabilities. The
other columns are as in the panels above. Clearly, for Wbm  0 , CRRA = 4 and we
replicate the values in Panel A for   4 . Keeping  fixed and increasing Wbm , effective
risk aversion increases. For example, CRRA increases from 4.21 to 7.97 as Wbm increases
from 0.05 to 0.5. The VIX increases from 17.87 to 27.93 and our risk aversion proxy RA
increases from 2.06 to 3.83. In sum, our risk aversion measure monotonically increases
with true risk aversion in the underlying economy.
Appendix Table 1: The VIX and Risk Aversion
Panel A: Varying  , Sk  1 , p  0.5 %
Parameters
Sk  1,   2
Sk  1,   4
Sk  1,   6
VIX
LVIX
VP
RA
15.987
17.612
20.139
1.529
1.626
1.760
0.003
0.008
0.018
0.936
1.960
2.711
Panel B: Varying  , Sk  2 , p  1 %
Parameters
Sk  2,   2
Sk  2,   4
Sk  2,   6
VIX
LVIX
VP
RA
16.908
19.841
24.075
1.585
1.745
1.939
0.006
0.017
0.036
1.624
2.643
3.386
Panel C: Varying Wbm ,   4 , Sk  1 , p  0.5 %
Parameters
  4, Wbm  0
  4, Wbm  0.05
  4, Wbm  0.25
  4, Wbm  0.50
CRRA
VIX
LVIX
VP
RA
4.000
17.612
1.626
0.008
1.960
4.209
17.868
1.641
0.009
2.061
5.323
19.598
1.733
0.016
2.584
7.968
27.934
2.087
0.056
3.835
Notes: Values of the VIX on an annualized basis in percent (VIX), the log of the VIX on a monthly basis
(LVIX), the annualized variance premium (VP), and our proxy for risk aversion on a monthly basis (RA)
for different values of the underlying parameters, while keeping the crash return c fixed at -25%. In Panel
A, the varying parameter is the coefficient of relative risk aversion γ while skewness Sk is fixed at -1. In
Panel B, skewness Sk is fixed at -2. Panel C computes, for γ fixed at 4 and Sk fixed at -1, expected risk
aversion (CRRA) and the other four variables for different values of the benchmark wealth level Wbm.
51
49
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41. "Stock market valuation in the United States", by P. Bisciari, A. Durré and A. Nyssens, Document series,
November 2003.
42. "Modeling the term structure of interest rates: Where do we stand?", by K. Maes, Research series,
February 2004.
43. "Interbank exposures: An ampirical examination of system risk in the Belgian banking system", by
H. Degryse and G. Nguyen, Research series, March 2004.
44. "How frequently do prices change? Evidence based on the micro data underlying the Belgian CPI", by
L. Aucremanne and E. Dhyne, Research series, April 2004.
45. "Firms' investment decisions in response to demand and price uncertainty", by C. Fuss and
Ph. Vermeulen, Research series, April 2004.
46. "SMEs and bank lending relationships: The impact of mergers", by H. Degryse, N. Masschelein and
J. Mitchell, Research series, May 2004.
47. "The determinants of pass-through of market conditions to bank retail interest rates in Belgium", by
F. De Graeve, O. De Jonghe and R. Vander Vennet, Research series, May 2004.
48. "Sectoral vs. country diversification benefits and downside risk", by M. Emiris, Research series,
May 2004.
49. "How does liquidity react to stress periods in a limit order market?", by H. Beltran, A. Durré and P. Giot,
Research series, May 2004.
50. "Financial consolidation and liquidity: Prudential regulation and/or competition policy?", by
P. Van Cayseele, Research series, May 2004.
51. "Basel II and operational risk: Implications for risk measurement and management in the financial
sector", by A. Chapelle, Y. Crama, G. Hübner and J.-P. Peters, Research series, May 2004.
52. "The efficiency and stability of banks and markets", by F. Allen, Research series, May 2004.
53. "Does financial liberalization spur growth?", by G. Bekaert, C.R. Harvey and C. Lundblad, Research
series, May 2004.
54. "Regulating financial conglomerates", by X. Freixas, G. Lóránth, A.D. Morrison and H.S. Shin, Research
series, May 2004.
55. "Liquidity and financial market stability", by M. O'Hara, Research series, May 2004.
56. "Economisch belang van de Vlaamse zeehavens: Verslag 2002", by F. Lagneaux, Document series,
June 2004.
57. "Determinants of euro term structure of credit spreads", by A. Van Landschoot, Research series, July
2004.
58. "Macroeconomic and monetary policy-making at the European Commission, from the Rome Treaties to
the Hague Summit", by I. Maes, Research series, July 2004.
59. "Liberalisation of network industries: Is electricity an exception to the rule?", by F. Coppens and D. Vivet,
Document series, September 2004.
60. "Forecasting with a Bayesian DSGE model: An application to the euro area", by F. Smets and
R. Wouters, Research series, September 2004.
61. "Comparing shocks and frictions in US and euro area business cycle: A Bayesian DSGE approach", by
F. Smets and R. Wouters, Research series, October 2004.
62. "Voting on pensions: A survey", by G. de Walque, Research series, October 2004.
63. "Asymmetric growth and inflation developments in the acceding countries: A new assessment", by S. Ide
and P. Moës, Research series, October 2004.
64. "Importance économique du Port Autonome de Liège: rapport 2002", by F. Lagneaux, Document series,
November 2004.
54
NBB WORKING PAPER No. 229 - OCTOBER 2012
65. "Price-setting behaviour in Belgium: What can be learned from an ad hoc survey", by L. Aucremanne and
M. Druant, Research series, March 2005.
66. "Time-dependent versus state-dependent pricing: A panel data approach to the determinants of Belgian
consumer price changes", by L. Aucremanne and E. Dhyne, Research series, April 2005.
67. "Indirect effects – A formal definition and degrees of dependency as an alternative to technical
coefficients", by F. Coppens, Research series, May 2005.
68. "Noname – A new quarterly model for Belgium", by Ph. Jeanfils and K. Burggraeve, Research series,
May 2005.
69. "Economic importance of the Flemish maritime ports: Report 2003", by F. Lagneaux, Document series,
May 2005.
70. "Measuring inflation persistence: A structural time series approach", by M. Dossche and G. Everaert,
Research series, June 2005.
71. "Financial intermediation theory and implications for the sources of value in structured finance markets",
by J. Mitchell, Document series, July 2005.
72. "Liquidity risk in securities settlement", by J. Devriese and J. Mitchell, Research series, July 2005.
73. "An international analysis of earnings, stock prices and bond yields", by A. Durré and P. Giot, Research
series, September 2005.
74. "Price setting in the euro area: Some stylized facts from Individual Consumer Price Data", by E. Dhyne,
L. J. Álvarez, H. Le Bihan, G. Veronese, D. Dias, J. Hoffmann, N. Jonker, P. Lünnemann, F. Rumler and
J. Vilmunen, Research series, September 2005.
75. "Importance économique du Port Autonome de Liège: rapport 2003", by F. Lagneaux, Document series,
October 2005.
76. "The pricing behaviour of firms in the euro area: New survey evidence, by S. Fabiani, M. Druant,
I. Hernando, C. Kwapil, B. Landau, C. Loupias, F. Martins, T. Mathä, R. Sabbatini, H. Stahl and
A. Stokman, Research series, November 2005.
77. "Income uncertainty and aggregate consumption”, by L. Pozzi, Research series, November 2005.
78. "Crédits aux particuliers - Analyse des données de la Centrale des Crédits aux Particuliers", by
H. De Doncker, Document series, January 2006.
79. "Is there a difference between solicited and unsolicited bank ratings and, if so, why?", by P. Van Roy,
Research series, February 2006.
80. "A generalised dynamic factor model for the Belgian economy - Useful business cycle indicators and
GDP growth forecasts", by Ch. Van Nieuwenhuyze, Research series, February 2006.
81. "Réduction linéaire de cotisations patronales à la sécurité sociale et financement alternatif", by
Ph. Jeanfils, L. Van Meensel, Ph. Du Caju, Y. Saks, K. Buysse and K. Van Cauter, Document series,
March 2006.
82. "The patterns and determinants of price setting in the Belgian industry", by D. Cornille and M. Dossche,
Research series, May 2006.
83. "A multi-factor model for the valuation and risk management of demand deposits", by H. Dewachter,
M. Lyrio and K. Maes, Research series, May 2006.
84. "The single European electricity market: A long road to convergence", by F. Coppens and D. Vivet,
Document series, May 2006.
85. "Firm-specific production factors in a DSGE model with Taylor price setting", by G. de Walque, F. Smets
and R. Wouters, Research series, June 2006.
86. "Economic importance of the Belgian ports: Flemish maritime ports and Liège port complex - Report
2004", by F. Lagneaux, Document series, June 2006.
87. "The response of firms' investment and financing to adverse cash flow shocks: The role of bank
relationships", by C. Fuss and Ph. Vermeulen, Research series, July 2006.
88. "The term structure of interest rates in a DSGE model", by M. Emiris, Research series, July 2006.
89. "The production function approach to the Belgian output gap, estimation of a multivariate structural time
series model", by Ph. Moës, Research series, September 2006.
90. "Industry wage differentials, unobserved ability, and rent-sharing: Evidence from matched worker-firm
data, 1995-2002", by R. Plasman, F. Rycx and I. Tojerow, Research series, October 2006.
91. "The dynamics of trade and competition", by N. Chen, J. Imbs and A. Scott, Research series, October
2006.
92. "A New Keynesian model with unemployment", by O. Blanchard and J. Gali, Research series, October
2006.
93. "Price and wage setting in an integrating Europe: Firm level evidence", by F. Abraham, J. Konings and
S. Vanormelingen, Research series, October 2006.
94. "Simulation, estimation and welfare implications of monetary policies in a 3-country NOEM model", by
J. Plasmans, T. Michalak and J. Fornero, Research series, October 2006.
NBB WORKING PAPER No. 229 - OCTOBER 2012
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95. "Inflation persistence and price-setting behaviour in the euro area: A summary of the Inflation
Persistence Network evidence ", by F. Altissimo, M. Ehrmann and F. Smets, Research series, October
2006.
96. "How wages change: Micro evidence from the International Wage Flexibility Project", by W.T. Dickens,
L. Goette, E.L. Groshen, S. Holden, J. Messina, M.E. Schweitzer, J. Turunen and M. Ward, Research
series, October 2006.
97. "Nominal wage rigidities in a new Keynesian model with frictional unemployment", by V. Bodart,
G. de Walque, O. Pierrard, H.R. Sneessens and R. Wouters, Research series, October 2006.
98. "Dynamics on monetary policy in a fair wage model of the business cycle", by D. De la Croix,
G. de Walque and R. Wouters, Research series, October 2006.
99. "The kinked demand curve and price rigidity: Evidence from scanner data", by M. Dossche, F. Heylen
and D. Van den Poel, Research series, October 2006.
100. "Lumpy price adjustments: A microeconometric analysis", by E. Dhyne, C. Fuss, H. Peseran and
P. Sevestre, Research series, October 2006.
101. "Reasons for wage rigidity in Germany", by W. Franz and F. Pfeiffer, Research series, October 2006.
102. "Fiscal sustainability indicators and policy design in the face of ageing", by G. Langenus, Research
series, October 2006.
103. "Macroeconomic fluctuations and firm entry: Theory and evidence", by V. Lewis, Research series,
October 2006.
104. "Exploring the CDS-bond basis", by J. De Wit, Research series, November 2006.
105. "Sector concentration in loan portfolios and economic capital", by K. Düllmann and N. Masschelein,
Research series, November 2006.
106. "R&D in the Belgian pharmaceutical sector", by H. De Doncker, Document series, December 2006.
107. "Importance et évolution des investissements directs en Belgique", by Ch. Piette, Document series,
January 2007.
108. "Investment-specific technology shocks and labor market frictions", by R. De Bock, Research series,
February 2007.
109. "Shocks and frictions in US business cycles: A Bayesian DSGE approach", by F. Smets and R. Wouters,
Research series, February 2007.
110. "Economic impact of port activity: A disaggregate analysis. The case of Antwerp", by F. Coppens,
F. Lagneaux, H. Meersman, N. Sellekaerts, E. Van de Voorde, G. van Gastel, Th. Vanelslander,
A. Verhetsel, Document series, February 2007.
111. "Price setting in the euro area: Some stylised facts from individual producer price data", by
Ph. Vermeulen, D. Dias, M. Dossche, E. Gautier, I. Hernando, R. Sabbatini, H. Stahl, Research series,
March 2007.
112. "Assessing the gap between observed and perceived inflation in the euro area: Is the credibility of the
HICP at stake?", by L. Aucremanne, M. Collin and Th. Stragier, Research series, April 2007.
113. "The spread of Keynesian economics: A comparison of the Belgian and Italian experiences", by I. Maes,
Research series, April 2007.
114. "Imports and exports at the level of the firm: Evidence from Belgium", by M. Muûls and M. Pisu,
Research series, May 2007.
115. "Economic importance of the Belgian ports: Flemish maritime ports and Liège port complex - Report
2005", by F. Lagneaux, Document series, May 2007.
116. "Temporal distribution of price changes: Staggering in the large and synchronization in the small", by
E. Dhyne and J. Konieczny, Research series, June 2007.
117. "Can excess liquidity signal an asset price boom?", by A. Bruggeman, Research series, August 2007.
118. "The performance of credit rating systems in the assessment of collateral used in Eurosystem monetary
policy operations", by F. Coppens, F. González and G. Winkler, Research series, September 2007.
119. "The determinants of stock and bond return comovements", by L. Baele, G. Bekaert and K. Inghelbrecht,
Research series, October 2007.
120. "Monitoring pro-cyclicality under the capital requirements directive: Preliminary concepts for developing a
framework", by N. Masschelein, Document series, October 2007.
121. "Dynamic order submission strategies with competition between a dealer market and a crossing
network", by H. Degryse, M. Van Achter and G. Wuyts, Research series, November 2007.
122. "The gas chain: Influence of its specificities on the liberalisation process", by C. Swartenbroekx,
Document series, November 2007.
123. "Failure prediction models: Performance, disagreements, and internal rating systems", by J. Mitchell and
P. Van Roy, Research series, December 2007.
124. "Downward wage rigidity for different workers and firms: An evaluation for Belgium using the IWFP
procedure", by Ph. Du Caju, C. Fuss and L. Wintr, Research series, December 2007.
125. "Economic importance of Belgian transport logistics", by F. Lagneaux, Document series, January 2008.
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NBB WORKING PAPER No. 229 - OCTOBER 2012
126. "Some evidence on late bidding in eBay auctions", by L. Wintr, Research series, January 2008.
127. "How do firms adjust their wage bill in Belgium? A decomposition along the intensive and extensive
margins", by C. Fuss, Research series, January 2008.
128. "Exports and productivity – Comparable evidence for 14 countries", by The International Study Group on
Exports and Productivity, Research series, February 2008.
129. "Estimation of monetary policy preferences in a forward-looking model: A Bayesian approach", by
P. Ilbas, Research series, March 2008.
130. "Job creation, job destruction and firms' international trade involvement", by M. Pisu, Research series,
March 2008.
131. "Do survey indicators let us see the business cycle? A frequency decomposition", by L. Dresse and
Ch. Van Nieuwenhuyze, Research series, March 2008.
132. "Searching for additional sources of inflation persistence: The micro-price panel data approach", by
R. Raciborski, Research series, April 2008.
133. "Short-term forecasting of GDP using large monthly datasets - A pseudo real-time forecast evaluation
exercise", by K. Barhoumi, S. Benk, R. Cristadoro, A. Den Reijer, A. Jakaitiene, P. Jelonek, A. Rua,
G. Rünstler, K. Ruth and Ch. Van Nieuwenhuyze, Research series, June 2008.
134. "Economic importance of the Belgian ports: Flemish maritime ports, Liège port complex and the port of
Brussels - Report 2006", by S. Vennix, Document series, June 2008.
135. "Imperfect exchange rate pass-through: The role of distribution services and variable demand elasticity",
by Ph. Jeanfils, Research series, August 2008.
136. "Multivariate structural time series models with dual cycles: Implications for measurement of output gap
and potential growth", by Ph. Moës, Research series, August 2008.
137. "Agency problems in structured finance - A case study of European CLOs", by J. Keller, Document
series, August 2008.
138. "The efficiency frontier as a method for gauging the performance of public expenditure: A Belgian case
study", by B. Eugène, Research series, September 2008.
139. "Exporters and credit constraints. A firm-level approach", by M. Muûls, Research series, September
2008.
140. "Export destinations and learning-by-exporting: Evidence from Belgium", by M. Pisu, Research series,
September 2008.
141. "Monetary aggregates and liquidity in a neo-Wicksellian framework", by M. Canzoneri, R. Cumby,
B. Diba and D. López-Salido, Research series, October 2008.
142 "Liquidity, inflation and asset prices in a time-varying framework for the euro area, by Ch. Baumeister,
E. Durinck and G. Peersman, Research series, October 2008.
143. "The bond premium in a DSGE model with long-run real and nominal risks", by G. D. Rudebusch and
E. T. Swanson, Research series, October 2008.
144. "Imperfect information, macroeconomic dynamics and the yield curve: An encompassing macro-finance
model", by H. Dewachter, Research series, October 2008.
145. "Housing market spillovers: Evidence from an estimated DSGE model", by M. Iacoviello and S. Neri,
Research series, October 2008.
146. "Credit frictions and optimal monetary policy", by V. Cúrdia and M. Woodford, Research series, October
2008.
147. "Central Bank misperceptions and the role of money in interest rate rules", by G. Beck and V. Wieland,
Research series, October 2008.
148. "Financial (in)stability, supervision and liquidity injections: A dynamic general equilibrium approach", by
G. de Walque, O. Pierrard and A. Rouabah, Research series, October 2008.
149. "Monetary policy, asset prices and macroeconomic conditions: A panel-VAR study", by K. AssenmacherWesche and S. Gerlach, Research series, October 2008.
150. "Risk premiums and macroeconomic dynamics in a heterogeneous agent model", by F. De Graeve,
M. Dossche, M. Emiris, H. Sneessens and R. Wouters, Research series, October 2008.
151. "Financial factors in economic fluctuations", by L. J. Christiano, R. Motto and M. Rotagno, Research
series, to be published.
152. "Rent-sharing under different bargaining regimes: Evidence from linked employer-employee data", by
M. Rusinek and F. Rycx, Research series, December 2008.
153. "Forecast with judgment and models", by F. Monti, Research series, December 2008.
154. "Institutional features of wage bargaining in 23 European countries, the US and Japan", by Ph. Du Caju,
E. Gautier, D. Momferatou and M. Ward-Warmedinger, Research series, December 2008.
155. "Fiscal sustainability and policy implications for the euro area", by F. Balassone, J. Cunha, G. Langenus,
B. Manzke, J Pavot, D. Prammer and P. Tommasino, Research series, January 2009.
156. "Understanding sectoral differences in downward real wage rigidity: Workforce composition, institutions,
technology and competition", by Ph. Du Caju, C. Fuss and L. Wintr, Research series, February 2009.
NBB WORKING PAPER No. 229 - OCTOBER 2012
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157. "Sequential bargaining in a New Keynesian model with frictional unemployment and staggered wage
negotiation", by G. de Walque, O. Pierrard, H. Sneessens and R. Wouters, Research series, February
2009.
158. "Economic importance of air transport and airport activities in Belgium", by F. Kupfer and F. Lagneaux,
Document series, March 2009.
159. "Rigid labour compensation and flexible employment? Firm-Level evidence with regard to productivity for
Belgium", by C. Fuss and L. Wintr, Research series, March 2009.
160. "The Belgian iron and steel industry in the international context", by F. Lagneaux and D. Vivet, Document
series, March 2009.
161. "Trade, wages and productivity", by K. Behrens, G. Mion, Y. Murata and J. Südekum, Research series,
March 2009.
162. "Labour flows in Belgium", by P. Heuse and Y. Saks, Research series, April 2009.
163. "The young Lamfalussy: An empirical and policy-oriented growth theorist", by I. Maes, Research series,
April 2009.
164. "Inflation dynamics with labour market matching: Assessing alternative specifications", by K. Christoffel,
J. Costain, G. de Walque, K. Kuester, T. Linzert, S. Millard and O. Pierrard, Research series, May 2009.
165. "Understanding inflation dynamics: Where do we stand?", by M. Dossche, Research series, June 2009.
166. "Input-output connections between sectors and optimal monetary policy", by E. Kara, Research series,
June 2009.
167. "Back to the basics in banking? A micro-analysis of banking system stability", by O. De Jonghe,
Research series, June 2009.
168. "Model misspecification, learning and the exchange rate disconnect puzzle", by V. Lewis and
A. Markiewicz, Research series, July 2009.
169. "The use of fixed-term contracts and the labour adjustment in Belgium", by E. Dhyne and B. Mahy,
Research series, July 2009.
170. "Analysis of business demography using markov chains – An application to Belgian data”, by F. Coppens
and F. Verduyn, Research series, July 2009.
171. "A global assessment of the degree of price stickiness - Results from the NBB business survey", by
E. Dhyne, Research series, July 2009.
172. "Economic importance of the Belgian ports: Flemish maritime ports, Liège port complex and the port of
Brussels - Report 2007", by C. Mathys, Document series, July 2009.
173. "Evaluating a monetary business cycle model with unemployment for the euro area", by N. Groshenny,
Research series, July 2009.
174. "How are firms' wages and prices linked: Survey evidence in Europe", by M. Druant, S. Fabiani and
G. Kezdi, A. Lamo, F. Martins and R. Sabbatini, Research series, August 2009.
175. "Micro-data on nominal rigidity, inflation persistence and optimal monetary policy", by E. Kara, Research
series, September 2009.
176. "On the origins of the BIS macro-prudential approach to financial stability: Alexandre Lamfalussy and
financial fragility", by I. Maes, Research series, October 2009.
177. "Incentives and tranche retention in securitisation: A screening model", by I. Fender and J. Mitchell,
Research series, October 2009.
178. "Optimal monetary policy and firm entry", by V. Lewis, Research series, October 2009.
179. "Staying, dropping, or switching: The impacts of bank mergers on small firms", by H. Degryse,
N. Masschelein and J. Mitchell, Research series, October 2009.
180. "Inter-industry wage differentials: How much does rent sharing matter?", by Ph. Du Caju, F. Rycx and
I. Tojerow, Research series, October 2009.
181. "Empirical evidence on the aggregate effects of anticipated and unanticipated US tax policy shocks", by
K. Mertens and M. O. Ravn, Research series, November 2009.
182. "Downward nominal and real wage rigidity: Survey evidence from European firms", by J. Babecký,
Ph. Du Caju, T. Kosma, M. Lawless, J. Messina and T. Rõõm, Research series, November 2009.
183. "The margins of labour cost adjustment: Survey evidence from European firms", by J. Babecký,
Ph. Du Caju, T. Kosma, M. Lawless, J. Messina and T. Rõõm, Research series, November 2009.
184. "Discriminatory fees, coordination and investment in shared ATM networks" by S. Ferrari, Research
series, January 2010.
185. "Self-fulfilling liquidity dry-ups", by F. Malherbe, Research series, March 2010.
186. "The development of monetary policy in the 20th century - some reflections", by O. Issing, Research
series, April 2010.
187. "Getting rid of Keynes? A survey of the history of macroeconomics from Keynes to Lucas and beyond",
by M. De Vroey, Research series, April 2010.
188. "A century of macroeconomic and monetary thought at the National Bank of Belgium", by I. Maes,
Research series, April 2010.
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NBB WORKING PAPER No. 229 - OCTOBER 2012
189. "Inter-industry wage differentials in EU countries: What do cross-country time-varying data add to the
picture?", by Ph. Du Caju, G. Kátay, A. Lamo, D. Nicolitsas and S. Poelhekke, Research series,
April 2010.
190. "What determines euro area bank CDS spreads?", by J. Annaert, M. De Ceuster, P. Van Roy and
C. Vespro, Research series, May 2010.
191. "The incidence of nominal and real wage rigidity: An individual-based sectoral approach", by J. Messina,
Ph. Du Caju, C. F. Duarte, N. L. Hansen, M. Izquierdo, Research series, June 2010.
192. "Economic importance of the Belgian ports: Flemish maritime ports, Liège port complex and the port of
Brussels - Report 2008", by C. Mathys, Document series, July 2010.
193. "Wages, labor or prices: how do firms react to shocks?", by E. Dhyne and M. Druant, Research series,
July 2010.
194. "Trade with China and skill upgrading: Evidence from Belgian firm level data", by G. Mion,
H. Vandenbussche, and L. Zhu, Research series, September 2010.
195. "Trade crisis? What trade crisis?", by K. Behrens, G. Corcos and G. Mion, Research series,
September 2010.
196. "Trade and the global recession", by J. Eaton, S. Kortum, B. Neiman and J. Romalis, Research series,
October 2010.
197. "Internationalization strategy and performance of small and medium sized enterprises", by J. Onkelinx
and L. Sleuwaegen, Research series, October 2010.
198. "The internationalization process of firms: From exports to FDI?", by P. Conconi, A. Sapir and
M. Zanardi, Research series, October 2010.
199. "Intermediaries in international trade: Direct versus indirect modes of export", by A. B. Bernard, M. Grazzi
and C. Tomasi, Research series, October 2010.
200. "Trade in services: IT and task content", by A. Ariu and G. Mion, Research series, October 2010.
201. "The productivity and export spillovers of the internationalisation behaviour of Belgian firms", by
M. Dumont, B. Merlevede, C. Piette and G. Rayp, Research series, October 2010.
202. "Market size, competition, and the product mix of exporters", by T. Mayer, M. J. Melitz and
G. I. P. Ottaviano, Research series, October 2010.
203. "Multi-product exporters, carry-along trade and the margins of trade", by A. B. Bernard, I. Van Beveren
and H. Vandenbussche, Research series, October 2010.
204. "Can Belgian firms cope with the Chinese dragon and the Asian tigers? The export performance of multiproduct firms on foreign markets" by F. Abraham and J. Van Hove, Research series, October 2010.
205. "Immigration, offshoring and American jobs", by G. I. P. Ottaviano, G. Peri and G. C. Wright, Research
series, October 2010.
206. "The effects of internationalisation on domestic labour demand by skills: Firm-level evidence for
Belgium", by L. Cuyvers, E. Dhyne, and R. Soeng, Research series, October 2010.
207. "Labour demand adjustment: Does foreign ownership matter?", by E. Dhyne, C. Fuss and C. Mathieu,
Research series, October 2010.
208. "The Taylor principle and (in-)determinacy in a New Keynesian model with hiring frictions and skill loss",
by A. Rannenberg, Research series, November 2010.
209. "Wage and employment effects of a wage norm: The Polish transition experience" by
A. de Crombrugghe and G. de Walque, Research series, February 2011.
210. "Estimating monetary policy reaction functions: A discrete choice approach" by J. Boeckx,
Research series, February 2011.
211. "Firm entry, inflation and the monetary transmission mechanism" by V. Lewis and C. Poilly,
Research series, February 2011.
212. "The link between mobile telephony arrears and credit arrears" by H. De Doncker, Document series,
March 2011.
213. "Development of a financial health indicator based on companies' annual accounts", by D. Vivet,
Document series, April 2011.
214. "Wage structure effects of international trade: Evidence from a small open economy", by Ph. Du Caju,
F. Rycx and I. Tojerow, Research series, April 2011.
215. "Economic importance of the Belgian ports: Flemish maritime ports, Liège port complex and the port of
Brussels - Report 2009", by C. Mathys, Document series, June 2011.
216. "Verti-zontal differentiation in monopolistic competition", by F. Di Comite, J.-F. Thisse and
H. Vandenbussche, Research series, October 2011.
217. "The evolution of Alexandre Lamfalussy's thought on the international and European monetary system
(1961-1993)" by I. Maes, Research series, November 2011.
218. "Economic importance of air transport and airport activities in Belgium – Report 2009", by X. Deville and
S. Vennix, Document series, December 2011.
NBB WORKING PAPER No. 229 - OCTOBER 2012
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219. "Comparative advantage, multi-product firms and trade liberalisation: An empirical test", by C. Fuss and
L. Zhu, Research series, January 2012.
220. "Institutions and export dynamics", by L. Araujo, G. Mion and E. Ornelas, Research series,
February 2012.
221. "Implementation of EU legislation on rail liberalisation in Belgium, France, Germany and the
Netherlands", by X. Deville and F. Verduyn, Document series, March 2012.
222. "Tommaso Padoa-Schioppa and the origins of the euro", by I. Maes, Document series, March 2012.
223. "(Not so) easy come, (still) easy go? Footloose multinationals revisited", by P. Blanchard, E. Dhyne,
C. Fuss and C. Mathieu, Research series, March 2012.
224. "Asymmetric information in credit markets, bank leverage cycles and macroeconomic dynamics", by
A. Rannenberg, Research series, April 2012.
225. "Economic importance of the Belgian ports: Flemish maritime ports, Liège port complex and the port of
Brussels - Report 2010", by C. Mathys, Document series, July 2012.
226. "Dissecting the dynamics of the US trade balance in an estimated equilibrium model", by P. Jacob and
G. Peersman, Research series, August 2012.
227. "Regime switches in volatility and correlation of financial institutions", by K. Boudt, J. Daníelsson,
S.J. Koopman and A. Lucas, Research series, October 2012.
228. "Measuring and testing for the systemically important financial institutions", by C. Castro and S. Ferrari,
Research series, October 2012.
229. "Risk, uncertainty and monetary policy", by G. Bekaert, M. Hoerova and M. Lo Duca, Research series,
October 2012.
60
NBB WORKING PAPER No. 229 - OCTOBER 2012
National Bank of Belgium
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RLP Brussels – Company’s number : 0203.201.340
Registered office : boulevard de Berlaimont 14 – BE -1000 Brussels
www.nbb.be
Editor
Jan Smets
Member of the Board of directors of the National Bank of Belgium
© Illustrations : National Bank of Belgium
Layout : Analysis and Research Group
Cover : NBB AG – Prepress & Image
Published in October 2012