IMES DISCUSSION PAPER SERIES

Negative Interest Rate Policy and the Influence ofMacroeconomic News on Yields

Rasmus Fatum, Naoko Hara, and Yohei Yamamoto
Discussion Paper No. 2019-E-2

INSTITUTE FOR MONETARY AND ECONOMIC STUDIESBANK OF JAPAN
2-1-1 NIHONBASHI-HONGOKUCHOCHUO-KU, TOKYO 103-8660
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Monetary and Economic Studies.

IMES Discussion Paper Series 2019-E-2
February 2019

Negative Interest Rate Policy and the Influence ofMacroeconomic News on Yields

Rasmus Fatum*, Naoko Hara**, and Yohei Yamamoto***
Abstract
We consider the influence of domestic and US macroeconomic news surprises on
daily bond yields over the January 1999 to January 2018 period for four advanced
Negative Interest Rate Policy (NIRP) economies – Germany, Japan, Sweden, andSwitzerland. Our results suggest that the influence of macroeconomic news
surprises is for all four countries under study during the NIRP period non-existentor noticeably weaker than during the preceding Zero Interest Rate Policy (ZIRP)
period. Our results are consistent with the suggestion that NIRP is characterized bya lower bound that is no less constraining than the zero lower bound thatcharacterizes ZIRP.
Keywords: NIRP; Bond Yields; Macroeconomic News
JEL classification: E43, E52, E58* Alberta School of Business, University of Alberta(E-mail: rasmus.fatum@ualberta.ca)** Deputy Director and Economist, Institute for Monetary and Economic Studies,Bank of Japan (E-mail: naoko.hara@boj.or.jp)*** Department of Economics, Hitotsubashi University(E-mail: yohei.yamamoto@econ.hit-u.ac.jp)
Fatum is also Research Associate at the Federal Reserve Bank of Dallas and member of the
Economic Policy Research Unit (EPRU) at the University of Copenhagen. This research was
prepared in part while Fatum was a visiting scholar at the Institute for Monetary and Economic
Studies, Bank of Japan. Fatum is grateful to colleagues at the IMES for their hospitality and for
helpful discussions. Fatum acknowledges financial support from a Foote Professorship inInternational Business and an EFF-SAS award. Yamamoto acknowledges financial support
from MEXT Grant-in-Aid for Scientific Research No. 17H00985. We thank Mototsugu
Shintani, Etsuro Shioji, Shigenori Shiratsuka, Nao Sudo, Carl Walsh, Jing Cynthia Wu and an
anonymous reviewer for helpful comments and Jens Iversen for help with the Swedish yielddata. The views expressed in this paper are those of the authors and do not necessarily reflect
the official views of the Bank of Japan.

1.

Introduction

We investigate the effects of domestic and US macroeconomic news surprises on bondyields over the January 1999 to January 2018 period for four advanced negative interest
rate policy (NIRP) economies – Germany, Japan, Sweden, and Switzerland. We focus on
the possibility of time-variation in the influence of news surprises coinciding with changes
in the country-specific domestic monetary policy stance.
Recent contributions by Swanson and Williams (2014a,b) suggest that the influenceof macroeconomic news on bond yields varies with the monetary policy stance. 1
Specifically, Swanson and Williams (2014a,b) compare the sensitivity of yields to news
surprises when the monetary policy stance is conventional, i.e. when interest rates areabove zero or above ultra-low levels, to when monetary policy is constrained by the zero
lower bound or characterized by ultra-low rates. They find that yields respond less to news
surprises during the latter policy regime and suggest that the extent to which news respondis indicative of the extent to which monetary policy is constrained.
Taking our cue from Swanson and Williams (2014a,b), our research objective is
two-fold. First, we attempt to shed light on whether the influence of news is different during
the NIRP period compared to during the preceding zero interest rate policy (ZIRP) period.
Second, in doing so we seek to provide insights on whether the NIRP regime can beconsidered more or less constraining than the zero-lower bound associated with thepreceding ZIRP regime. In addressing the first part of our research objective we attempt to
The vast literature and interest in whether macroeconomic news influence asset prices and whether the
influence is time-varying is not surprising considering that macroeconomic news help explain a nonnegligible share of the daily variation in asset prices. For example, Altavilla et al. (2017) find that macro
news explain about 10% of daily variation in bond yields while Evans and Lyons (2008) suggest that macro
news explain more than 30% of daily exchange rate variation.
1

1

make a specific contribution to the literature on macro news and asset prices while
addressing the second part of our research question allows us to attempt to contribute toour understanding of NIRP and the lower bound constraint associated with NIRP.2
To facilitate our investigation we employ a data set of interest rate series consistingof daily zero-coupon government bond yields with medium- and longer-term maturities
ranging from 1 to 10 years in conjunction with a comprehensive set of date-stamped UnitedStated (US) and non-US macroeconomic announcements and preceding survey
expectations. Our full sample spans the 1 January 1999 to 31 January 2018 period.
Our empirical analysis of the time-varying effects of macro news surprises on bondyields follows the two-step procedure described in Swanson and Williams (2014a,b). We
consider for each country three different monetary policy regimes (conventional, ZIRP and
NIRP) and we choose for each country the first regime as our normalization sub-sample.
While our focus is on daily frequency estimations we also carry out our empirical analysis
using monthly frequency series. To provide additional insights we also investigate theeffect of macroeconomic news surprises on the yield curve.
Overall, our results suggest that the influence of macroeconomic news surprises isfor all four countries either noticeably weaker or non-existent during the NIRP period thanduring the preceding ZIRP period. This is an important finding as it indicates that bondyields in NIRP countries are less hinged on fundamentals compared to during normal or
ZIRP regimes. Furthermore, it is a finding that is at a minimum consistent with the

Whether monetary policy during NIRP is more or less constrained than during ZIRP is not immediately
obvious. By construction, NIRP is associated with an accommodative monetary policy beyond what the zero
lower bound and the ZIRP would permit. However, as we argue in Section 2.1, a lower bound exists also for
NIRP countries although unlike the zero lower bound the exact position of the lower bound on the negative
scale is unknown and possibly time-varying.
22

suggestion that NIRP is associated with a lower bound that is no less constraining than the
ZIRP lower bound.
We extend our analysis and check the robustness of our baseline results byconsidering the possibility of asymmetric effects of good versus bad news and byaddressing whether the distribution of news appears to vary systematically across the threedifferent monetary policy regimes under study. We also assess separately the effects ofdomestic (non-US) news, and we control for key US monetary policy announcement dates.
To further check the robustness of our fixed-window regression results we also computethe sequence of coefficient estimates from one-year rolling windows. Moreover, we
compare our results to the context of an economy that maintained ultra-low interest rates
during the time-period when the countries considered in our baseline analysis pursued
NIRP regimes by extending our analysis to consider the effects of macroeconomic news
on yields for the case of United Kingdom (UK).
The rest of the paper is organized as follows. Section 2 describes institutional
aspects and characteristics of NIRP. Sections 3 and 4 detail our data and empiricalmethodology, respectively. Section 5 presents our results. Section 6 provides extensions
and robustness checks. Section 7 concludes.
2.

Institutional Aspects and Characteristics of NIRPThe unprecedented era of major central banks pursuing negative interest rate policies began
June 11, 2014, when the European Central Bank (ECB) deposit rate was lowered to -0.10
percent. Subsequently, on February 16, 2016, the second major central bank, the Bank of3

Japan, lowered its deposit rate to -0.10 percent.3 Between the introduction of NIRP by the
ECB and the Bank of Japan, Switzerland and Sweden also went from ultra-low to negativeinterest rates, and both did so around the same time. Switzerland lowered its deposit rate(the so-called “sight deposit rate”) to -0.75 percent on January 15, 2015, while Sweden
lowered its policy rate, the repo rate, to -0.10 percent on February 12, 2015.4 The salientand common policy objective of NIRP for all four countries under study is to counter
deflationary pressures and raise inflation. For Switzerland, the stated objective of NIRP isdual in that the policy also aims to reduce or prevent domestic currency appreciationpressures in order to avoid a stifling of economic growth.
Denmark is the first advanced economy to enter NIRP territory as Denmark
lowered its certificate of deposit rate to -0.20 percent as early as July 5, 2012. However,
because the objective of the Danish NIRP pertains to maintenance of the DKK vis-à-vis
the EUR within the Exchange Rate Mechanism (ERM) II framework, the context andcircumstance of the Danish NIRP are very different from those of the countries under study.
For this reason, we do not include Denmark in our sample. Furthermore, Bulgaria andHungary introduced negative policy rates on November 26, 2015, and March 22, 2016,
respectively. Neither Bulgaria nor Hungary is considered in our analysis as both countries
are generally classified as emerging market economies and thus very different from theadvanced economies under study.

Prior to the introduction of the Japanese NIRP, the ECB deposit rate had been further lowered and was at
the time of Japan entering NIRP held at -0.30 percent. Shortly after the Bank of Japan announcement of NIRP
the ECB on March 16, 2016, reduced its deposit rate to -0.40 percent. See Wu and Xia (2008) for details on
the ECB NIRP rate cuts and a careful analysis of their impact on the yield curve.
4
The Swedish deposit rate entered negative territory, at -0.50 percent, on June 7, 2014.
34

For additional details on advanced economy NIRP countries, and some early
assessments of the successfulness of NIRP, see ADBI (2016) and IMF (2017).
2.1

Characteristics of NIRP

The move from ZIRP or ultra-low interest rates to NIRP marks at least a nominally
dramatic shift in monetary policy. However, two essential aspects associated with the ZIRP
regime also characterize the NIRP regime. First, while the emergence of NIRP has certainly
proven that the lower bound is not binding at zero, or at above-zero but ultra-low interest
rates, a binding lower bound nevertheless remains. This is, simply, because negativeinterest rates are only meaningful when these are above or equate the cost of holding
money.5 Thus, a lower bound exists also for NIRP countries. Since the cost of holding
money is not directly observable and, furthermore, central banks may be reluctant to lower
interest rates to the point of testing the cost of holding money, the exact position of thelower bound on the negative scale is unknown. Consequently, the binding lower boundduring NIRP is a latent lower bound (LLB).
Second, as shown by Reifschneider and Williams (2000), monetary policy can beeffective even if policy rates are at the lower bound constraint by influencing current
expectations about the path of future monetary policy when economic conditions and thus
policies are such that the constraint is no longer binding. While the context of Reifschneider
and Williams (2000) is that of low inflation and low interest rates rather than that ofdeflationary pressure and NIRP regimes, the basic argument is the same during NIRP inthat monetary policy can still be effective in the face of currently binding constraints by

Dong and Wen (2017) note that how far in the negative interest rates can go depends on the cost to theprivate sector of holding money.
55

altering expectations about, in our context, post-NIRP policies. 6 Thus, two essential
characteristics associated with ZIRP, namely that monetary policy is constrained by aninterest rate floor and that monetary policy can nevertheless still be effective, are alsoessential characteristics of the NIRP regime.
These two essential characteristics of NIRP can be formalized using the illustrativemodel of Swanson and Williams (2014a). In their model, which is nested in the New
Keynesian framework proposed by Clarida, Gali and Gertler (1999) and Woodford (2003)
and others, the effect on the economy of the current short-term interest rate being
constrained (at the lower bound) may be insubstantial if expectations of the path of future
short-term interest rates are unconstrained. We assume, as in Swanson and Williams(2014a), that the central bank follows a Taylor-type rule (Taylor 1993) when setting its
short-term interest rate and, furthermore, that the interest rate decision is subject to theconstraint that interest rates cannot go below the LLB.
In this model, the short-term interest rate
=

where

{ , ∗}

is described as follows:(1)is the deposit rate and the lower bound.7 The term ∗ is the “optimal” short-term

policy rate determined by the Taylor rule such that:
∗

=

+

∗

+ (

−

)+

(2)

As discussed in Swanson and Williams (2014a), this is consistent with the empirical evidence of Gürkaynak,
Sack, and Swanson (2005) that show US monetary policy announcements to be mainly effective through thealtering of market expectations regarding future monetary policy rather than through changes in thecontemporaneous federal funds rate.
6

Wu and Xia (2018) state that in the context of the ECB the deposit rate is by definition the lower boundof the Euro Over Night Index Average (EONIA).
7

6

whereis the inflation rate,

target,is the output gap, and a and b are non-zero constants that sum to one.8 Thus,∗is the natural rate of interest,

Equations (1) and (2) encompass the special case in whichis the central bank inflation= 0 for all (as described in

Swanson and Williams, 2014a).
Equation (1) implies that the daily “change” in the short-term interest rate is
∆
∆∗
− ∗
∗
−

Δ =

if ∗ ≤
if ∗ >
if ∗ ≤
if ∗ >

and ∗
and ∗
and ∗
and ∗

≤
>
>
≤

(3)

An important implication of Equation (3) is in the first case, suggesting that thechange of the short-term interest rate does not directly reflect economic fundamentals
through the standard Taylor rule if the optimal rate stays below the deposit rate for both
periods, but it does so only through the change of the lower bound ∆ .

Following Swanson and Williams (2014a), and regardless of whether current shortterm interest rates are at the lower bound, at zero, or above, medium- and long-term interest
rates are determined by current expectations of the path of future short-term nominalinterest rates and the term premium such that the M-period yield to maturity on a zerocoupon nominal bond is described as follows:
=∑

where

(

)+

is the expectations operator at time and(4)
is a term premium.

The LLB is then described as:

=∑

8

(

)+

Hence, ∗ corresponds to the “shadow rate” (Black, 1995, and Wu and Xia, 2016).

7

(5)

≥

such thatalways holds by construction of Equation (1). Intuitively, the LLB

can thus be interpreted as the cost of holding cash for the next

− 1 periods as LLB is thesum of future deposit rates plus term premium.
Equation (4) implies that:
∆

=∑

)−∑

(

(

)

(6)

or, in words, that the daily change in the M-period yield to maturity is given by the change
in the expected future path of the short-term interest rate from time − 1 to .

Combining Equations (3) and (6) illustrates the following. If the optimal short-term
interest rate is expected to stay below the deposit rate for the next
=

(

) and

=

(

) for

− 1 periods, then

= 0, … ,− 1 and thechange in the medium- and long-term interest rates reduces to the change in the expected
future deposit rates. Thus, consistent with Equation (3), if the optimal short-term interest
rate is expected to stay below the deposit rate for the next

− 1 periods, the change inneither medium- nor long-term interest rates is directly reflective of economic
fundamentals via the Taylor rule but rather via only the deposit rate.
As discussed in Swanson and Williams (2014a,b), the responsiveness of yields tomacroeconomic news may indicate the extent to which monetary policy is constrained
because macroeconomic news in normal economic circumstances can elicit an off-setting
policy response, e.g. a negative GDP announcement can prompt a more accommodative
monetary policy stance. Because of the existence of the LLB it is not necessarily the casethat the move from ZIRP to NIRP marks a relaxation of a binding constraint. Since the
LLB is unobservable, current negative interest rates may already be at the LLB or, if they
are not, each additional interest rate decrease, if any, will either reach the LLB or move8

rates further towards the LLB. The implications for the effects of macroeconomic news onyields, therefore, is not that NIRP returns responsiveness of yields to news back to normal.
Instead, if monetary policy during NIRP is even more constrained than during ZIRP, theeffects of macroeconomic news will be further subdued.
3.

Data

Our news data consists of a comprehensive set of date-stamped US and non-US
macroeconomic announcements and preceding survey expectations.9 Our full sample spansthe 1 January 1999 to 31 January 2018 period.10 We consider news variables that otherstudies typically find to be important and, as detailed in Table 2, our news data covers five
news series for Germany, seven news series for Sweden, five news series for Switzerland,and nine news series for Japan. In addition, we include in our analysis nine news series for
the US. The US news series are included in all our baseline estimations, i.e. for eachcountry we consider the effects of the domestic news pertaining to the country in question
alongside the effects of the US news.
Our data on survey expectations is obtained from Money Market Services (MMS)
provided by Haver Analytics and from Bloomberg News Service. Following the literature(e.g. Andersen et al. 2003, 2007), we construct for each news variable the standardized

Fatum and Scholnick (2008) show that failure to disentangle the expected component of news may lead toan underestimation of the impact of news.
10
Table 1 displays the country-specific monetary policy regime change dates. For the case of Japan, while
the beginning of the ZIRP period coincides with the beginning of our full sample period, we refer to theperiod preceding the introduction of the Japanese quantitative easing (QE) as characterized by conventionalmonetary policy and the period from the beginning of the Japanese QE to the introduction of the Japanesequantitative and qualitative easing (QQE) as characterized by ZIRP.
99

news surprise as the unexpected component of the announcement divided by the associatedsample standard deviation.11
When constructing the news data set for each country we carefully account for thedifference in timing across US and domestic macroeconomic announcements.
Our interest rate data series consist of daily zero-coupon government bond yields
with medium- and longer-term maturities ranging from 1 to 10 years. German, Japanese,and Swiss yields are available from Deutsche Bundesbank, the Japanese Ministry of
Finance, and the Swiss National Bank, respectively. Swedish yields are provided by
Sveriges Riksbank. Figure 1 shows the evolution of the yields over the full sample.
4.

Empirical Methodology

To assess the same-day effects of macroeconomic news surprises on government bondyields we first estimate the following baseline regression model:
where Δ

=

−

Δ

=

+

′ +

is the one-day change of a given the bond yield,

vector of news surprise components associated with the country-specific

(7)
is a

×1

different

macroeconomic surprise augmented by the inclusion of US macroeconomic surprises.12
For a given news variable an entry in vector

takes on the value of the surprise component

on day when a macroeconomic news announcement is released for this particular variableLetdenote the value of a given news variable announcement on day . Let refer to the median valueof the preceding market expectations, and let denote the sample standard deviation of all the surprisecomponents associated with this indicator using the entire sample period. The standardized surprise of the.
macroeconomic fundamental announced on day is then defined as
11

For example, in the case of Germany
the nine US news variables.

12= 14 as the five German news variables are considered alongside10and zero otherwise. The

× 1 coefficient vector

captures the sensitivity of a given bondyield to the individual news surprise. The error termis mean zero.

A possible concern pertaining to our linear model specification is the implicit
symmetry assumption with respect to the responsiveness of our dependent variable to goodand bad news. When monetary policy rates are constrained, whether by the zero lower
bound or by the LLB, monetary policy may not respond to off-set bad news by moving in
the negative direction as much as they would to off-set good news by moving in the positive
direction for good and bad news surprises of comparable absolute magnitude. In thepresence of such policy rate asymmetry, the responsiveness of bond rates to news surprises
could be similarly asymmetric. This is a concern in our context because longer-term yields,
our dependent variable, are, as discussed earlier, considered an average of expected future
short-term rates. However, as argued by Swanson and Williams (2014a,b), when the shortterm interest rateis constrained, and policymakers would prefer to substantially further

reduce and maintainbelow the constraining bound, in such a situation, the short-term

rate becomes completely unresponsive in both positive and negative directions as long as
positive news surprises are not so substantial that they bring short-term rates above thebinding constraint and cause the economy to exit the binding regime. In other words, if thelower bound is strongly binding the one-sided constraint effectively becomes
symmetrically binding.13
Estimating the model described in Equation (7) provides us with detailed
information on the influence of news surprises on bond yields. However, since theindividual news variables included in13

are released only once a month or once a quarter,

We extend our framework to formally consider asymmetries in Section 6.

11

estimating the effects of news using relatively sparse regressors may provide only
imprecise estimates. This concern is amplified when addressing the possibility ofsystematic time variations in the estimated coefficients. Hence, we decompose thecoefficients in (7) following Swanson and Williams (2014a,b) such that
Δ

where

and

=

+

′

+

(8)

are scalars.14 These scalars are potentially time varying. In particular andwith respect to time variation, we consider separately and country by country sub-samples
identified according to monetary policy regimes

as defined by their respective andcountry-specific regime change dates. We consider for each country three monetary policy
regimes and denote these the conventional monetary policy (CMP) regime, the ZIRP
regime, and the NIRP regime, respectively. We address the identification problem withrespect toandby imposing for each country that= 1 during the CMP regime. We

choose the CMP regime as our normalization sub-sample since for each country this regime
is associated with the largest sub-sample size.
We then estimate Equation (8) following the two-step estimation procedure of
Swanson and Williams (2014a,b). In the first step we estimate Equation (8) using the CMP
sub-sample. In the second step we use≡′ , where

proxy forandby OLS with heteroskedasticity robust′

in order to estimate

standard errors.15 If

is the first step estimate, as ais less than one in the second (ZIRP) or the third (NIRP) sub-sample,

this then indicates that overall the sensitivity of a given bond yield to news surprises has

14 In other words, rather than focusing on the effects of individual news surprises on bond yields, we consider

the effects news using for each country a single country-specific index variable that encompasses all countryspecific and US news surprises described in our news data set.
15
We include observations when all the elements in are zero, following the literature. However, the results
are qualitatively the same if no-announcement days are excluded.

12

declined relative to the first (CMP) sub-sample. This approach allows us to similarlycompare the estimate ofacross any two regimes, e.g. ZIRP versus NIRP, to assess

whether the overall sensitivity to news surprises has increased or decreased.16
The model described in Equation (8) is estimated separately for each NIRP country
considered (Germany, Sweden, Switzerland, and Japan) across the full sample period aswell as separately across each (country-specific) CMP, ZIRP, and NIRP regime sub-sample
period. All our estimations are carried out using government bond yields pertaining to 1year, 2-year, and 10-year maturities.
The two-step procedure has two important advantages. First, the overall time
variation of the effects of news surprises is captured byunder the assumption that therelative magnitudes of the effects of individual news surprises

are time invariant. Second,the overall significance of the effects of news surprises is also assessed by testing for thenull hypothesis:= 0 against the alternative hypothesis:≠ 0 in the second step.

While our main focus is on daily frequency analysis we also implement the twostep procedure using the monthly average ofandto assess what Altavilla et al. (2017)refer to as the persistence effect of the macroeconomic news surprises on bond yields.17
To provide additional insights we also investigate the effect of macroeconomic
news surprises on the yield curve. We use a simple approach in which we extract the first
three principal components from our yield curve data set.18 More specifically, we do as
16 Swanson and Williams (2014a,b) estimate

using 1-year rolling windows. In order to focus on the effects
of news separately across different monetary policy regimes we estimate using fixed windows definedaccording to monetary policy regime change dates. Compared to Swanson and Williams (2014a,b), ourresults are akin to "snapshots" across different samples of varying lengths.
17
Altavilla et al. (2017) find evidence that the explanatory power of news surprises with respect to thevariation in bond yields increases when their model is estimated using data at a lower frequency.
18 See Inoue and Rossi (2018a,b) for recent contributions to yield curve modelling and its application tomonetary policy analysis.

13follows. First, letLet= [ ′, ⋯ ,= [ ′, ⋯ ,′]′ be a]′ be a× 3 eigenvector matrix of a× 10 matrix of yields with=[′, ⋯ ,

×matrix (′).

] being a 10 × 1

vector of yields. The upper subscript denotes the maturity (year). Second, we then reestimate our baseline model described by Equation (4) one at a time for each element inas our new dependent variable in place of the dependent variable. We follow Litterman

and Sheinkman (1991) and Ang and Piazzesi (2003) and others in labeling the threeelements inas the level, the slope, and the curvature components.

5.

Results

Table 3 shows the results of estimating Equation (8) country by country and separately
across the full and the three monetary policy regime defined sub-samples using daily data.
These are our baseline results. As our full sample results suggest, news surprises
systematically influence bond yields for all four countries when we consider the 1999 to2018 period as a whole. The magnitude and significance of the news index coefficientestimates pertaining to the ZIRP sub-sample period suggest that yields are influenced bymacro news surprises during this period as well but generally less so or, in the case of Japan,
not at all. These results are consistent with the findings of Swanson and Williams (2014b)
in that the sensitivity of yields to news surprises remains significant during ZIRP for
Germany. Moreover, the news index coefficient estimate remains strongly significant
during the ZIRP period for all countries but Japan. Most importantly, and directly
addressing our research question, our baseline results clearly suggest that the influence ofmacroeconomic news surprises is for all countries either noticeably weaker or non-existentduring the NIRP period. This is an important finding. It indicates that bond yields in NIRP

14

countries are essentially less hinged on fundamentals and, furthermore, it is at leastconsistent with the suggestion that the LLB associated with ZIRP is no less constraining
than the ZIRP lower bound.
Table 4 reports the results of the monthly frequency estimations. As the table shows,for the full sample our monthly frequency results are very similar to the daily frequency
results reported in Table 3. Consistent with Altavilla et al. (2017), when comparing theoverall explanatory power of the news surprises we generally find that the effects of news
surprises on yields is persistent in the sense that theis larger when the monthly

frequency data is used to capture the variations in yields. In contrast with the dailyfrequency estimation results, we find at most only marginally significant news index
coefficient estimates across the ZIRP sub-sample when considering monthly frequency
data. We find no monthly frequency effects of news surprises during NIRP for either of thefour countries under study. Unsurprisingly, considering that we do not find any news
surprise coefficient estimates to be significant at conventional levels when analyzing
monthly frequency data, the explanatory power of news as measured byhas declined

from the CMP period to the NIRP period.
Turning to the yield curve analysis characterized by the three principal components,
Table 5 reports our results. As the table shows, when we consider the full sample period,
all the three components of the yield curve are strongly sensitive to the macro news
surprises in all four NIRP countries. This finding is consistent with the suggestion thatnews surprises can affect the shape of the yield curve by influencing the expectation of the
future path of the short-term interest rate. Not surprisingly, considering that there is a wide
consensus that the first few principal components capture almost all the variation in the15

term structure (e.g. Duffee, 2013), we find that in case of each of the four NIRP countries
the first three principal components explain more than 95% of the total variation ofindividual yields.19
Consistent with our baseline results as well as with our monthly data findings, theprincipal component analysis pertaining to the NIRP sub-sample indicates that thesensitivity to news surprises is not statistically significant at conventional levels for neither
one of the three yield curve components for any of the four NIRP countries under study
with the exception of the first principal component for Sweden.20
6.

Extensions and Robustness

In this section we extend our analysis and check the robustness of our main results byconsidering the possibility of asymmetric effects of good versus bad news, by addressing
whether the distribution of news appears to differ across sub-samples, by assessing
separately the effects of domestic (non-US) news, by controlling for key US large scaleasset purchase (LSAP) announcements, and by estimating rolling regressions. To compare
our results to the context of an economy that maintained ultra-low interest rates during thetime-period when the countries considered in our baseline analysis pursued NIRP regimes
we also extend our analysis to consider the effects of macroeconomic news on yields forthe case of UK.21

Results pertaining to the explanatory power of the first three components with respect to total variation ofindividual yields are not shown for brevity but available from the authors upon request.
20
We also carried out the principal components investigation using monthly frequency estimations. The
components analysis using monthly frequency data yields results similar to the daily data results reported inTable 5 and are not included for brevity but available from the authors upon request.
21
For brevity, only results pertaining to the UK are reported. All other extension and robustness results areavailable upon request.
19

16

First, we consider the possibility that good news might influence bond yields
differently than bad news and the possibility that such differences might be sub-sample
specific, i.e. we consider for each of the countries in our sample possible good news versus
bad news asymmetries separately across the CMP, the ZIRP, and the NIRP regimes. Thisis a particularly important consideration as it pertains to the validity of our linear
specification especially during the ZIRP and the NIRP periods, as discussed in Section 3.
Moreover, while most studies tend to find mostly sparse empirical evidence in support ofgood versus bad news asymmetries some studies find some, albeit mostly limited, support
for the suggestion that such asymmetries manifest across different stages of the businesscycle (see, for example, Andersen et al., 2007, on the effects of news in bond and othermarkets as well as, for example, Andersen et al., 2003, on the effects of news in foreignexchange markets). 22 As it turns out, while we find some differences in coefficientestimates when comparing the influence of good and bad news on yields across the fullsample as well as separately across the monetary policy regimes, these differences are notsignificant and do not point to a systematic pattern for any of the countries considered.
Thus, while our investigation of asymmetries confirms the robustness of our main findings
we do not find evidence of systematically different effects of good versus bad news.
Second, to address the possibility that what appears to be a systematic change inthe responsiveness of yields to news across different monetary policy regimes might be theconsequence of an underlying systematic change in the distribution of news across said
regimes we follow Swanson and Williams (2014a,b) and plot the distribution of the news
index variable country by country and separately across the full sample and across each of
While our sub-samples are defined according to the stance of monetary policy stance rather than the stageof the business cycle, the former is at a minimum influenced by the latter.
22

17

the three policy regime defined sub-samples. Similar to the plots of US nonfarm payrolls
surprises and US Core CPI surprises pre- and post-GFC documented in Swanson andWilliams (2014a,b), our news index variable distribution plots are country by country
similar across the CMP, the ZIRP, and the NIRP sample periods, thereby offering no
support for the suggestion that the change in the relative effect of news across the three
periods can be explained by changes in the distribution of news, i.e. changes in the news
variable itself.
Third, we redo our baseline analysis with only non-US news included as
explanatory variables, i.e. for each country we now consider only the influence on theyields of the country in question of the domestic news from this particular country. When
doing so we find that for the full sample, domestic news matter for domestic bond yields
for all but Switzerland. For Germany and Sweden we find that domestic news matter lessor not at all for respective domestic bond yields during the NIRP regime relative to during
the ZIRP regime. Only for Japan do we find that domestic news do not matter at all fordomestic bond yields during neither the NIRP nor the ZIRP regime.23 Overall, our domestic
news only results are generally consistent with the previously discussed baseline findings.
Fourth, we extend the analysis to take into account the possibility that US QE
monetary policy announcements influence NIRP country bond yields and check whether
our previously discussed baseline results are robust to the inclusion of theseannouncements. To do so we first obtain the dates and associated time-stamps for sevenmajor US QE announcements in order to construct a US-QE dummy variable that takes on

The lack of influence of Japanese macro news on Japanese bond yields is consistent with Cheung et al.
(2018) and their finding that from the GFC period and onwards the influence of Japanese macro news on the
JPY/USD rate has all but disappeared.
23

18

the value one when a US QE announcement occurs, and zero otherwise.24 In turn, we reestimate our baseline models separately across the full and the three sub-samples (CMP,
ZIRP, and NIRP) with the US-QE dummy variable included as an additional explanatory
variable.
The results of controlling for US QE announcements suggest that the US-QE
dummy variable is mostly insignificant. This is a surprising finding, implying that the US
QE announcements are inconsequential to the foreign NIRP bond markets. However,preceding survey expectations pertaining to the unscheduled US QE announcements areunavailable and, therefore, we are unable to control for only the surprise component ofthese announcements. Consequently, our results pertaining to the coefficient estimates of
the US-QE dummy variable are interpreted with much caution. More importantly for our
research question at hand, the addition of this US monetary policy news control variable
does not in any way change our previously discussed baseline results as the macro news
results based on estimations that omit this control are identical to the macro news results
based on estimations that include this control.25
Fifth, to further check the robustness of our fixed-window regression results wealso compute the sequence of coefficient estimates from one-year rolling windows for 1year, 2-year and 10-year yields, respectively. We do so separately for each of the four
countries. We follow the same specification as Swanson and Williams (2014a, b) exceptthat our standard errors are computed from the second stage regression only. Similar to
See Rosenberg (2015) for details on the seven major US QE announcements dates considered.
For completeness, we also check the robustness of our results with respect to US QE announcements bysimply omitting the US announcement dates from our sample. When we re-estimate our models on the
reduced sample our baseline results remain unchanged. We also control for non-US monetary policy
announcements by omitting non-US announcement dates from our sample separately for each of the relevant
NIRP country considered and find our previously discussed results robust to this data change.
24
25

19

Swanson and Williams, our rolling regression results show fluctuations in sensitivity over
the CMP period and a general decline in sensitivity during the ZIRP regime, subject tosome country heterogeneity. This decline in sensitivity is, on average, further pronounced
during the NIRP regime. Overall, our rolling regression results are consistent with ourbaseline results in that the coefficient estimates generally decline from the CMP period to
the ZIRP period and even more so during the NIRP period.
Sixth and final, we extend our analysis to consider the effects of macroeconomic
news on yields for the case of UK. Doing so facilitates a comparison of our results to thosepertaining to an economy that maintained ultra-low interest rates during the time-period
when the countries considered in our baseline analysis pursued NIRP regimes.26 We report
the results of the UK analysis in Table 6. As the table shows, and broadly consistent withthe findings of Swanson and Williams (2014b), we find no systematic and statisticallydiscernible difference between the responsiveness of UK yields to news when comparing
our results across the full sample to those pertaining to the ZIRP comparable period (which
the UK begins March 2009 and continues throughout the remainder of our full sample
period).27 Certainly, the results of this extension do not provide direct evidence that NIRP
regimes are necessarily associated with less responsiveness of yields to news. However,the fact that we find evidence of a general decline in responsiveness to news when
considering NIRP countries, but do not repeat this finding when considering an economy
that remained in a ZIRP comparable regime rather than moving into NIRP, is at least

UK is also considered, alongside Germany, in Swanson and Williams (2014b). Swanson and Williams(2014b) note that given the interest rate structure of UK rates the UK period of ultra-low interest rates iscomparable to ZIRP even though UK policy rates did not reach zero.
27
This finding is broadly consistent with Swanson and Williams (2014b). They report no statically significant
evidence that their sensitivity measure of UK yields to news declines during the ZIRP comparable period.
26

20

consistent with our baseline results and the suggestion that news are relatively lessinfluential on bond yields during NIRP compared to during ZIRP.
7.

Conclusion

In this paper we have considered the influence of domestic and US macroeconomic news
surprises on bond yields over the January 1999 to January 2018 period for four advanced
NIRP economies – Germany, Japan, Sweden, and Switzerland - with focus on thepossibility of time-variation in the influence of news coinciding with changes in thedomestic monetary policy stance. Specifically, we have separately for each of the four
NIRP countries in our sample assessed the influence of news surprises on yields across thefull sample period as well as separately across the country-specific CMP, ZIRP, and NIRP
periods.
Overall, our results suggest that the influence of macroeconomic news surprises isfor all four countries either noticeably weaker or non-existent during the NIRP period whencompared to the preceding ZIRP period. This is an important finding. It indicates that bondyields in NIRP countries are essentially less hinged on fundamentals and, furthermore, it
is a finding that is at a minimum consistent with the suggestion that the LLB associatedwith NIRP is no less constraining than the ZIRP lower bound.
It is prudent to stress an important caveat when interpreting our results. By
construction, our results do not provide direct evidence that the observed changes in thesensitivity of yields to news surprises is necessarily due to changes in the monetary policy
stance. While we find that changes in sensitivity are systematic and coincide with monetary
policy regime changes for all four NIRP countries considered, particularly we find that the21sensitivity of yields to news surprises is reduced during NIRP, these results provide
coincidental rather than causal evidence as we are not providing statistical evidence thatthe reported sensitivity changes are necessarily due to monetary policy regime changes.
We are not able to provide such causal evidence in the context of our present empiricalframework since our “sample” of monetary policy regimes essentially consists of three data
points, i.e. CMP, ZIRP, and NIRP. Moreover, we cannot rule out that other economic
factors and events influence how bond yields react to macro news.
Nevertheless, the fact that we find a clear pattern of statistical evidence indicatingthat the influence of macroeconomic news surprises during NIRP is significantly lesspronounced compared to during ZIRP for all four NIRP countries in our sample, alongside
the fact that we find that the influence of news surprises on yields did not decrease for thecomparison country that did not enter into a NIRP regime, are at a minimum consistent
with the suggestion that macroeconomic news affect bond yields less when policy rates arenegative. Thus, our results are also at a minimum consistent with the suggestion thatmonetary policy during NIRP is more rather than less constrained compared to during ZIRP,
thereby lending credence to the notion of a constraining LLB as a characteristic of NIRP
regimes.

22

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24

Figure 1. Yields for NIRP countriesa) Germany
7b) Sweden
7

1-year62-year

5

3

2

2

1

1

0

0

-1

-1
99 01 03 05 07 09 11 13 15 17-2c) Switzerland
765
4

99 01 03 05 07 09 11 13 15 17d) Japan7

1-year62-year510-year4

3

1-year2-year10-year

3

2

2

1

1

0

0

-1
-2

10-year43-22-year510-year4

1-year

6-1
99 01 03 05 07 09 11 13 15 17-2

25

99 01 03 05 07 09 11 13 15 17

Table 1. Monetary policy regime change dates

ZIRP

NIRP

Germany

10/30/2008
6/11/2014

SwedenSwitzerland

2/18/2015

1/22/2015

7/13/200912/11/2008

26

Japan

4/4/2013

2/16/2016Table 2. Macroeconomic newsa) Germany

Domestic news

Non-zero announcement surprises

Full

CPI

152

Real retail sales

218

IFO

Unemployment Change
Real GDP preliminary

US news

Frequency30

1/10/2000

Monthly43

1/13/1999

Monthly

ZIRP

NIRP

75

47

113

62

222

114

222

115

61

Starting date

Pre-ZIRP

65
66

43
41

30

21

10

Job claims

972

497

288

187

CPI core

138

69

40

29

Capacity utilization
ISM manufacturing
Nonfarm payrolls

PPI for final demand
Retail sales

Unemployment Rate
RGDP advanceb) Sweden

209

109

224

115

172

90

228
179
163
74

117
82
81

39

61
67
68
45
58
55

20

1/21/1999
1/8/1999

2/19/1999
1/7/1999

MonthlyMonthly

Quarterly
Weekly39

1/15/1999

Monthly42

1/4/1999

Monthly43

1/14/1999
1/8/199937

1/12/1999

27

1/8/1999

39
15

Non-zero announcement surprises

6/13/2001

MonthlyMonthlyMonthlyMonthlyMonthly

1/29/1999

Quarterly

Starting dateFrequency

Full

Pre-ZIRP

ZIRP

NIRP

CPI all items

187

104

54

29

1/19/1999

Monthly

Retail sales

224

123

66

35

1/20/1999

Monthly

Domestic news

Industrial production
Unemployment Rate
Trade balance
PMI

Real GDP

198
199
132
147

103
114
62
50

64
59
53
65

31
26
17
32

72

41

20

Job claims

972

532

289

151

CPI core

138

76

39

23

US news

Capacity utilization
ISM manufacturing
Nonfarm payrolls

PPI for final demand
Retail sales

Unemployment Rate
RGDP advance

Note: Starting dates are in local time.

209

117

224

124

172

96

228
179
163
74

126
90
88

42

27

60
66
67
44
58
54

20

11

1/27/1999
1/20/1999

MonthlyMonthly

3/4/1999

Monthly

3/10/1999

Quarterly

12/1/2004
1/7/1999

Monthly
Weekly32

1/15/1999

Monthly34

1/4/1999

Monthly32

1/12/1999

Monthly21

1/8/1999

35
31
12

1/14/1999
1/8/1999

6/13/2001
1/29/1999

MonthlyMonthlyMonthlyMonthly

QuarterlyTable 2. Macroeconomic news (cont.)c) Switzerland

Non-zero announcement surprises

Full

Pre-ZIRP

ZIRP

NIRP

169

88

57

24

79

48

24

61

Job claims
CPI core

Domestic news

CPI all items
PMI

Unemployment rate

Production & import prices
Real GDP preliminary

US news

Capacity utilization
ISM manufacturing
Nonfarm payrolls

PPI for final demand
Retail sales

Unemployment Rate
RGDP advanced) JapanDomestic news

170
185

22

972

503

314

155

138

70

45

23

117

172

91

228
179
163
74

Tankan large manuf.

Tankan large non-manuf.
Real GDP preliminary

US news

119
83
83

40

67
72
73
49
64
58

21

11

Full

Monthly

36
22

1/8/1999

32
13

Frequency

9/28/2001

Monthly

4/28/2003

Monthly

10/14/2003

7

4/5/1999

Quarterly

2/16/2005

Quarterly

34

24

2/10/2000

24

45

10

30

12

6

8

5

20

16

74

Starting date

16

34

102

RGDP advance

Quarterly

10/29/2001

138

163

Monthly

26

28

179

Monthly

1/29/1999

24

CPI core

Unemployment Rate

Monthly

89

33

101

Retail sales

6/13/2001

Monthly

23

41

172

1/8/1999

1/12/1999

Monthly

33

158

228

1/14/1999

32

148

PPI for final demand

Weekly1/4/1999

723

Nonfarm payrolls

1/7/1999

Monthly

117

216

224

Quarterly

35

972

ISM manufacturing

6/8/2000

17

157

209

MonthlyMonthly

Job claims

Capacity utilization

1/7/1999

1/15/1999

Monthly

14

215

48

Monthly

4/1/2003

91

130

62

2/4/1999

1/15/1999

Pre-QQE QQE-ZIRP QQE-NIRP

187
131

Frequency

32

Non-zero announcement surprises

173

Trade balance

110

224

Retail sales
PPI

7

28

122

Machinery orders

34
29

209

97

72

59

CPI Tokyo

Industrial production

64

Starting date

156
168
170
126
128
122
55

28

31
34
35
24
31
25

11

11/24/1999
4/1/2002

1/7/1999

MonthlyMonthlyMonthlyMonthly

Quarterly
Weekly22

1/15/1999

Monthly22

1/4/1999

Monthly23

1/14/1999
1/8/1999

MonthlyMonthly22

1/12/1999

Monthly16

1/8/1999

Monthly20
8

6/13/2001
1/29/1999

Monthly

QuarterlyTable 3. Baseline results: Daily frequencya) Germany
Full sample
Index
R-sq

1-year0.708( 0.075)2-year
***

0.044

0.714

( 0.065)

R-sq

0.355

( 0.123)

***

0.060

ZIRP (10/30/2008-6/10/2014)
Index

10-year

***

0.015

0.401

( 0.121)

***

0.023

R-sq

0.072

Full sample
Index
R-sq

1-year0.699( 0.133)

***

0.021

0.722

( 0.103)

R-sq

0.301

( 0.133)
0.009

0.350

( 0.115)

***

R-sq

-0.015( 0.240)
0.000

**

Index

0.778

( 0.111)

***

Index

0.015
0.008

( 0.232)
0.000

0.550

( 0.199)
0.012
0.129

( 0.271)0.001( 0.069)

***

0.066

0.508

( 0.108)0.858( 0.067)0.321( 0.093)

***

Index

***

0.685

( 0.127)( 0.196)0.481( 0.084)

***

0.009

0.770

( 0.154)

***

***

R-sq

0.183

( 0.274)( 0.249)

0.000

0.003

QQE-NIRP (2/16/2016-1/31/2018)
Index

R-sq

-1.036( 0.569)*0.012( 0.268)

***

0.658

( 0.239)***

10-year

0.016

0.086

1.168

0.022

QQE-ZIRP (4/4/2013-2/15/2016)
Index( 0.106)

***

0.040

2-year
***

1.003

0.046

0.100

1-year

0.666

***

0.053

0.062

Full sample

10-year

0.085

0.035

d) Japan

R-sq

***

0.776

2-yearNIRP (2/18/2015-1/31/2018)

R-sq

***

1-yearZIRP (7/13/2009-2/17/2015)

R-sq

0.024

NIRP (1/22/2015-1/31/2018)
Index

***

10-year

0.033

**

Index

0.010

2-yearZIRP (12/11/2008-1/21/2015)
Index( 0.191)

0.001c) Switzerland( 0.171)0.388( 0.071)

0.002

0.647

Full sample

R-sq

0.024

0.057

( 0.081)( 0.083)
0.044

NIRP (6/11/2014-1/31/2018)
Index

0.821

***

b) Sweden-0.089( 0.358)
0.000

0.779

( 0.133)

***

0.015

0.118

( 0.286)
0.001-0.333( 0.255)
0.006

Notes:i) Heteroscedasticity-corrected standard errors are reported in parenthesis.

ii) A constant is included in all estimations but associated coefficient estimates and standard
errors are not reported for brevity.

iii) ***, **, and * represent statistical significance at 1%, 5%, and 10% levels, respectively.

29

Table 4. Baseline results: Monthly frequencya) Germany
Full sample
Index
R-sq

1-year0.740( 0.261)2-year
***

0.081

0.736

( 0.211)10-year
***

0.105

R-sq

0.184

0.231

( 0.300)

0.016

R-sq

-0.2380.039

Full sample
Index
R-sq

1-year0.804( 0.232)
0.102

0.772

( 0.199)
0.101

ZIRP (12/11/2008-1/21/2015)
Index

R-sq

0.216

( 0.111)
0.049

*

0.012

( 0.169)
0.000

NIRP (1/22/2015-1/31/2018)
Index
R-sq

0.369

( 0.311)
0.041

0.319

( 0.312)
0.018

Index
R-sq

10-year***0.608( 0.256)
0.040-0.142( 0.290)0.002-0.270( 0.394)
0.006

0.664

( 0.198)2-year
***

0.080

0.745

( 0.181)10-year
***

0.088

0.126

0.324

( 0.175)( 0.447)

0.025

**

0.134

0.001

-0.148( 0.198)( 0.616)1-year2-year10-year

0.025

Full sample

R-sq

0.154

( 0.141)d) Japan

Index

0.763

( 0.252)
0.031

0.014

***

0.851

( 0.304)

0.001

***

0.023

QQE-ZIRP (4/4/2013-2/15/2016)
Index

R-sq

0.250

( 0.229)
0.027

0.588

( 0.311)0.110*

QQE-NIRP (2/16/2016-1/31/2018)
Index
R-sq

Notes: See notes to Table 3.

30( 0.252)

***

0.199

( 0.202)

0.006

0.718

0.031

NIRP (2/18/2015-1/31/2018)

0.016

2-year***R-sq( 0.601)

0.018c) Switzerland

Index

0.493

( 0.157)

1-yearZIRP (7/13/2009-2/17/2015)

0.021-0.133( 0.195)

Index( 0.465)

NIRP (6/11/2014-1/31/2018)
Index

Full sample

R-sq

0.552

( 0.240)0.009( 0.276)
0.055

ZIRP (10/30/2008-6/10/2014)
Index

0.859

***

b) Sweden

0.293( 0.553)
0.006

0.044

( 0.684)
0.000

0.897

( 0.311)
0.038

0.905

( 0.748)
0.103

0.014

( 0.682)
0.000

***

Table 5. Regression results of principal componentsa) Germany

1st PC

Full sample
Index
R-sq

0.770

( 0.068)

***

0.062

2nd PC

3rd PC

0.366

0.567

( 0.141)0.003

ZIRP (10/30/2008-6/10/2014)
Index
R-sq

0.548

( 0.139)

***

***

0.030

-0.685( 0.200)

***

0.011

R-sq

0.183

0.006

c) Switzerland

1st PC

Full sample
Index
R-sq

0.781

( 0.092)

***

0.040

0.535

0.534

ZIRP (12/11/2008-1/21/2015)
Index

R-sq

0.532

( 0.142)0.023-0.170( 0.249)
0.001

NIRP (1/22/2015-1/31/2018)
Index
R-sq

0.183

( 0.194)
0.002-0.638( 0.428)
0.009

R-sq

***

( 0.203)
0.003-0.276( 0.255)
0.001

0.250

( 0.220)0.936( 0.081)

***

0.068

3rd PC

0.760

0.843

( 0.088)0.945( 0.183)

Index

***

0.050

R-sq

d) Japan

***

0.570

( 0.145)0.429( 0.135)

Index
R-sq

***

( 0.124)
0.020

***

( 0.185)

***

*

R-sq

0.083

( 0.223)
0.001

0.001

R-sq

Notes: See notes to Table 3.

31-0.175( 0.220)
0.002

0.164

( 0.299)

2nd PC

3rd PC

0.856

0.657

( 0.218)

***

0.007

0.650

( 0.352)
0.006*

QQE-NIRP (2/16/2016-1/31/2018)Index( 0.376)

**

0.001

QQE-ZIRP (4/4/2013-2/15/2016)
Index

0.895

0.009

0.017

1st PC

0.771

0.329

( 0.178)

***

0.010

0.012

0.043

Full sample

***

0.041

NIRP (2/18/2015-1/31/2018)

0.000

3rd PC( 0.161)Index( 0.210)

2nd PC

0.005***-0.063( 0.204)

0.002

Index

2nd PCZIRP (7/13/2009-2/17/2015)

0.034( 0.365)

1st PC

Full sample

R-sq

0.000

-0.330( 0.113)

***

0.005

NIRP (6/11/2014-1/31/2018)Index( 0.186)b) Sweden-0.411( 0.844)0.001( 0.163)

***

0.009

-0.059( 0.350)0.000-1.259( 0.706)
0.022*

Table 6. Baseline results for UK: Daily frequency
Full sample
Index
R-sq

1-year0.680( 0.062)2-year

***

0.059

0.769

( 0.064)10-year

***

0.065

0.081

Index

0.333

0.084

ZIRP (3/5/2009-1/31/2018)

R-sq( 0.058)
0.038

***

0.483

( 0.072)0.041( 0.091)

***

0.045

CMP (1/1/1999-3/4/2009)
R-sq

0.989

0.061
***

0.969

( 0.154)

***

0.030

Notes: See notes to Table 3.

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