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THE SOVEREIGN YIELD CURVE AND THEMACROECONOMY IN CHINA
YIFENG YAN* Xi’an Jiaotong UniversityJU’E GUO Xi’an Jiaotong
University
Abstract. A dynamic Nelson–Siegel model is adopted to estimate
three time-varying factors of yieldcurves, the level, the slope and
the curvature, and a vector autoregressive model is built to
studyinteractions between macro variables and the yield curve.
Results show that, first, money supplygrowth is a more effective
instrument to curb inflation than the monetary policy interest
rate;however, the central bank also adjusts the interest rate to
stabilize money supply. Second, invest-ment is an important measure
to stimulate the Chinese economy, but it also pushes up money
supplygrowth, which results in higher inflation. Third, the yield
curve reacts significantly to innovations toinvestment growth and
money supply growth. The segmentation of China’s bond market
hinders theefficient implementation of monetary policy, and the
monetary policy transmission mechanism isstill weak in China.
Finally, interactions between the yield curve and the macroeconomy
in China arenearly unidirectional. Macroeconomic variables reshape
the yield curve, but direct adjustments ofthe yield curve do not
significantly change macroeconomic variables. Due to the incomplete
liber-alization of financial markets, there exists a wide
disjunction between the real economy and financialmarkets in
China.
JEL: E43, E44, E51, E52, E62, G12
1. INTRODUCTION
In 1981, China resumed Treasury bond issuance, and, according to
the Bank forInternational Settlements, by July 2012 China’s bond
market had become theworld’s third largest. On June 1997, the
People’s Bank of China (PBC)instructed commercial banks to leave
the exchange bond market to preventcredit funds from speculating in
the stock market. Since then, China’s bondmarket has been segmented
into the exchange bond market and the interbankbond market. The
interbank bond market is open to institutional investors,including,
for example, commercial banks, security companies and
insurancecompanies. The exchange bond market is open to individuals
and non-bankfinancial institutions. In addition, listed commercial
banks have been allowed totrade in the exchange bond market since
December 2010. The bond market inChina remains segmented because
regulation, the bond custodian system andclearing in these two
markets are separate from each other. The interbank bondmarket is
supervised by the PBC, and the exchange bond market is supervised
by
*Address for Correspondence: School of Management, Xi’an
Jiaotong University, No. 28, XianningWest Road, Xi’an 710049,
China. E-mail: [email protected]. We are very grateful to
theeditor and two anonymous referees for helpful comments and
suggestions. We also thank theNational Natural Science Foundation
of China (No. 71173169) for financial support.
bs_bs_banner
Pacific Economic Review, 20: 3 (2015) pp. 415–441doi:
10.1111/1468-0106.12063
© 2015 Wiley Publishing Asia Pty Ltd
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the China Securities Regulatory Commission. The China Central
Depositoryand Clearing Company undertakes the function of
centralized depository andhandles settlement for the interbank bond
market; the China Securities Deposi-tory and Clearing Corporation
does the same for the exchange bond market.
The major types of bonds available in China’s bond market
include Treasurybonds, central bank bills, financial bonds,
enterprise bonds and corporatebonds. Central bank bills are
short-term securities issued in the interbank bondmarket by the PBC
as a monetary policy instrument. Financial bonds are issuedby the
policy banks, commercial banks and other financial institutions.
Centralbank bills and financial bonds are the most actively traded
bonds in China.Enterprise bonds are issued by large-sized
state-owned companies, while corpo-rate bonds can be issued by any
company. Enterprise bonds can be traded onboth bond markets, but
corporate bonds are only traded on the exchangemarket. Enterprise
bonds have a much larger outstanding amount and arecurrently more
actively traded than corporate bonds. The interbank market ismuch
larger than the exchange market, and commercial banks dominate
tradingactivity in the interbank bond market.
China’s central bank initially conducted its monetary policy by
directly con-trolling the credit quotas of each commercial bank. In
1998, the PBC terminatedthe system of national bank credit quotas.
Since then, the central bank hasmoved towards applying indirect
ways to control money supply. The majormonetary policy instruments
comprise open market operations, reserve require-ments, deposit and
lending interest rates, refinancing and rediscount rates.
Openmarket operations are implemented principally through repo
transactions ofTreasury bonds as well as issuing and trading of
central bank bills in theinterbank market. Central bank bills were
introduced into China’s interbankmarket in 2003. They are the most
liquid bonds because of the large amount ofthese bills outstanding
in the market and the regular weekly issuances. Thedeposit and
lending interest rates were under the control of the PBC before
2013.On July 2013, the PBC announced the removal of lending
interest rate control,but the deposit interest rates have not been
fully liberalized yet. China’s centralbank also conducts monetary
policy through window guidance and a standinglending facility,
which was established on November 2013.
Generally, China’s financial markets are strictly regulated, and
Chinese mon-etary policy is mainly conducted through administrative
and quantitative meas-ures (Koivu, 2009). He and Wang (2012) find
that Chinese market interest ratesare sensitive to changes in the
benchmark deposit interest rates and changes inthe reserve
requirements, but not particularly reactive to open market
opera-tions. Market-based monetary policy instruments of the PBC,
such as the reporate and the benchmark lending rate, have little
impact on the Chinese economy,and non-market-based measures, such
as growth rates of total loan and moneysupply, are effective in
adjusting the real economy and the price level (see e.g.Qin et al.,
2005; Siklos and Zhang, 2010; He et al., 2013).
Despite the inefficiency of market-based monetary policy, Porter
and Cassola(2011) find evidence of an emerging transmission
channel, and that severalnecessary elements to move towards the
application of indirect monetary policy
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are already in place with further liberalization of Chinese
financial markets.Koivu (2009) also points out that Chinese loan
demand has become moredependent on interest rates since 2001.
Although bond yields are not fullyefficient because of regulation,
liquidity and segmentation, Chinese bond yieldscontain considerable
information about the state of the economy and act as
anintermediary in monetary policy transmission: changes in PBC bill
rates (aproxy monetary policy variable) influence the structure of
Treasury, financialand corporate bond yield curves, which are then
associated with changes inoutput and inflation (Porter and Cassola,
2011).
The existing papers studying the transmission mechanism of
Chinese mon-etary policy mainly focus on the efficiency and
improvement of Chinese mon-etary policy implementation as well as
the comparison of monetary policyinstruments. The functioning and
effectiveness of bond markets, especially theirinteractions with
monetary policy and the real economy, have not been inves-tigated
extensively in the existing studies, with the exception of Porter
andCassola (2011) and Fan and Johansson (2010). Fan and Johansson
(2010) modelChinese bond yields using the 1-year deposit interest
rate as a state variable in anaffine framework. Their results
suggest that a macro-finance approach for ana-lysing bond yields
also works in a Chinese institutional setting that
differssignificantly from that of the USA and Europe. Their main
effort is to incorpo-rate monetary policy into a model of China’s
yield curve, and they suggest futureresearch should include
alternative economic variables such as inflation andother measures
of monetary policy as explanatory variables when analysingChina’s
bond market. Porter and Cassola (2011) proceed in this direction.
Theyincorporate macroeconomic variables (industrial value added
growth and con-sumer price index inflation) as well as the 3-month
PBC bill rate in their analysis,and study the interaction between
the policy bank bonds market and the realeconomy.
The present paper aims to extend and deepen our understanding of
howmacro variables as well as market-based and non-market-based
measures ofmonetary policy interact with Chinese bond yields. The
paper provides newevidence from the Chinese economy on the
controversy over the relationshipsbetween macro variables and yield
curves. This study differentiates from existingstudies in the
following aspects: first, considering that the sovereign yield
curveprovides a fundamental benchmark in the economy, we study the
interactionbetween the real economy and the Treasury bonds market
instead of the policybank bonds market; second, we expand the scope
of the real economy byincluding investment and consumption in
addition to output and inflationbecause they are major drivers of
the economy (see e.g. Mehrotra et al., 2013);third, because of the
importance of quantitative monetary policy measures inChina, both
money supply and monetary policy interest rates are studied in
thepresent paper; finally, we incorporate fiscal policy and foreign
trade variablesinto our study because they play important roles in
China’s economy (see e.g.Chow, 2006; He et al., 2009).
Methodologically, we follow the framework of Afonso and Martins
(2012),who use a dynamic Nelson–Siegel model to estimate yield
curves and a vector
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 417
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autoregressive (VAR) system to investigate linkages between
financial variablesand the real economy. The Nelson–Siegel method
captures the level, slope andcurvature of the yield curve (Nelson
and Siegel, 1987), and plays a very impor-tant role in studies of
the term structure of interest rates (see e.g. Gurkaynakaet al.,
2007). Diebold and Li (2006) and Diebold et al. (2006) further
develop adynamic Nelson–Siegel model with time-varying level, slope
and curvaturefactors. The parsimonious Nelson–Siegel model provides
good forecastability ofthe yield curve (see e.g. Diebold and Li,
2006; Vicente and Tabak, 2008; Yu andSalyards, 2009), and follows
Zellner’s (1992) ‘KISS’ (i.e. keep it sophisticatedlysimple)
principle of forecasting. However, very few articles have used
thedynamic Nelson–Siegel model to study Chinese bond markets. Luo
et al. (2012)find that the dynamic Nelson–Siegel model fits and
forecasts the term structureof Chinese Treasury yields very well,
and demonstrate that time-varying factorsof the model may be
interpreted as the level, slope and curvature of the yieldcurve.
Porter and Cassola (2011) also adopt the dynamic Nelson–Siegel
modelto study Chinese bond markets.
A large subset of the literature attempts to identify and
distinguish deter-minants or influencing factors of the level,
slope and curvature of the yieldcurve. The level captures the
long-maturity yield, the slope captures behav-iours of
short-maturity yields and the curvature captures behaviours of
mid-maturity yields (Porter and Cassola, 2011). Accordingly, the
level is typicallyassociated with the medium-term or long-run
nominal anchor, namely, thetarget or expectation for long-term
inflation; the slope is associated withchanges in the short rate
and the reaction of monetary policy to the cyclicalstate of an
economy; the curvature reflects the difference between the spreadof
intermediate and short maturities, and the spread of long and
intermediatematurities (see e.g. Rudebusch and Wu, 2008; Bekaert et
al., 2010; Afonso andMartins, 2012; Aguiar-Conraria et al., 2012).
However, the empirical evidenceremains somewhat mixed. Besides
inflation-related variables, the level is alsoinfluenced by, for
instance, the aggregate supply shocks from the privatesector (Wu,
2003), shocks to the monetary policy interest rate (Sultan,
2005),technology shocks (Wu, 2006) and shocks to the marginal rate
of substitutionbetween consumption and leisure (Evans and Marshall,
2007). In addition tomonetary policy shocks (see e.g. Wu, 2006;
Farka and DaSilva, 2011), theslope can also be affected by
variables such as output, employment (Lu andWu, 2009) and other
business cycle indicators. The curvature captures themonetary
policy stance of the central bank (see e.g. Dewachter and
Lyrio,2006; Bekaert et al., 2010; Lengwiler and Lenz, 2010) and is
impacted byunemployment (Huse, 2011). Moreover, fiscal behaviour,
such as governmentdebt burden, plays a role in determining the
level and slope of the yield curve(see e.g. Benigno and Missale,
2004; Afonso and Martins, 2012). In an inter-national context,
Diebold et al. (2008) find that global yield factors exist andare
economically important for Germany, Japan, the UK and the USA;the
temporal decline in the global level factor reflects the reduction
ofinflation and movements in the global slope factor reflect the
global businesscycle.
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The yield curve also contains future information on, for
instance, real activityand inflation, and could provide an even
better prediction than professionalmacroeconomic forecasters
(Rudebusch and Williams, 2009). Some studies findthat the yield
spread and the slope can be used to predict recession and
inflation(see e.g. Ivanova et al., 2000; Mehl, 2009). Favero et al.
(2012) and Moench(2012) point out that the level, slope and
curvature can all contribute in fore-casting output and inflation.
The stability of the predictive power of the yieldcurve for output
and inflation is supported by many studies (see e.g. Schich,2002;
Estrella, 2005), although there is some conflicting evidence (see
e.g. Anget al., 2006; Nobili, 2007).
The samples in this study start in January 2002 and end in
December 2012.Using the dynamic Nelson–Siegel model, we estimate
three time-varying latentfactors (level, slope and curvature) of
the yield curve under a state-space frame-work. Furthermore, we
establish a VAR system of macro variables and the threelatent
factors, and explore the relationships among variables through
impulseresponse analysis.
The following are the main findings and conclusions in this
study. First,Chinese authorities conduct monetary and fiscal
policies to boost economicgrowth and stabilize prices. To curb
inflation, money supply growth is a moreeffective instrument than
interest rates; however, the central bank also adjuststhe interest
rate to stabilize money supply. Second, investment is an
importantmeans of stimulating the Chinese economy, but it also
pushes up money supplygrowth, which results in higher inflation.
Third, the yield curve reacts signifi-cantly to innovations to
investment growth and money supply growth, but theopen market
operations through the monetary policy interest rate conducted
inthe interbank market do not reshape the yield curve in the
exchange market. Thesegmentation of China’s bond market hinders
implementation of monetarypolicy, and the monetary policy
transmission mechanism is still weak in China.Fourth, interactions
between the yield curve and the macroeconomy in Chinaare nearly
unidirectional rather than bidirectional. Macroeconomic
variablesreshape the yield curve, but direct adjustments of the
yield curve do not signifi-cantly affect macroeconomic variables.
Due to the incomplete liberalization offinancial markets, there
exists a wide disjunction between the real economy andfinancial
markets in China.
The remainder of this paper is organized as follows. Section 2
introducesthe methodology, consisting of the dynamic Nelson–Siegel
model and VARspecifications. An empirical analysis is conducted in
Section 3 and Section 4concludes.
2. METHODOLOGY
This paper follows the methodology of Afonso and Martins (2012),
whichcomprises two steps. First, three latent factors (level, slope
and curvature) ofyield curves are estimated under the framework of
the dynamic Nelson–Siegelmodel proposed by Diebold et al. (2006).
Second, a VAR model is used toexplore linkages between the
sovereign yield curve and the macroeconomy.
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 419
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Our choice of a parsimonious Nelson–Siegel model to estimate
three latentfactors rather than an arbitrage-free model is inspired
by Diebold and Li (2006,pp. 361–2) and Diebold et al. (2006, p.
333), who state that:
It is not obvious that use of arbitrage-free models is necessary
or desirable for pro-ducing good forecasts; an a priori restriction
may be violated in the data due toilliquidity in thinly traded
regions of the yield curve; if the no-arbitrage restriction
doesindeed hold for the data, then it will at least approximately
be captured by the dynamicNelson–Siegel model which is a flexible
approximation to the data.
Coroneo et al. (2011) provide empirical evidence that the
Nelson–Siegelmodel performs as well as its no-arbitrage counterpart
in forecasting.
2.1. The yield curve latent factors
To gather information from the cross-section of yields with
various maturities atany point in time, principal component
analysis or the Nelson–Siegel factormodel are widely used. In the
spirit of Diebold et al. (2006), the Nelson–Siegelformulation
imposes some economically-motivated restrictions, including
posi-tive forward rates at all horizons and a discount factor that
approaches zero asmaturity increases, and these restrictions may be
necessary in the analysis of yieldcurve dynamics. The Nelson–Siegel
representation of the yield curve parsimoni-ously estimates the
yield curve at any point in time using the following equation:
ye e
eτ β βλτ
βλτ
λτ λτλτ( ) = + −⎛
⎝⎜⎞⎠⎟
+ − −⎛⎝⎜
⎞⎠⎟
− −−
1 2 31 1
, (1)
where y(τ) denotes a set of yields, τ denotes maturity, and β1,
β2, β3 and λ areparameters.
Diebold and Li (2006) extends the former Nelson–Siegel model
into adynamic latent factor model, where β1, β2 and β3 are
time-varying, and can beinterpreted as the level (L), slope (S) and
curvature (C) of the yield curve,respectively, at any point in
time. Thus, we obtain:
y L Se
Ce
et t t tτ λτ λτ
λτ λτλτ( ) = + −⎛
⎝⎜⎞⎠⎟
+ − −⎛⎝⎜
⎞⎠⎟
− −−1 1 . (2)
Loadings of Lt are equal to one for all maturities, and other
two-factorloadings approach zero as maturities increase, so Lt is a
long-term factor, and isclosely related to the long end of the
yield curve; meanwhile, it may be inter-preted as the level of the
yield curve as an increase in Lt increases all yieldsequally.
Loadings of St start at one and monotonically decrease to zero
asmaturities increase; therefore, St is a short-term factor, and it
may be interpretedas the slope of the yield curve, which can be
defined as the long-term yield minusthe short-term yield. An
increase in St increases short yields more than longyields, thereby
changing the slope of the yield curve. Loadings of Ct start at
zero
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and increase to a maximum at the middle of the yield curve, then
return to zeroas maturities increase, so Ct is a medium-term
factor, and it may be interpretedas the curvature of the yield
curve. In other words, an increase in Ct increasesmedium-term
yields, but barely moves very short and very long yields,
therebychanging the curvature of the yield curve.
As in Diebold et al. (2006), if the stochastic process of Lt, St
and Ct is assumedto be a VAR process of order one, then dynamics of
a large set of yields withvarious maturities can be presented as a
state-space model. The state transitionequation is:
L
S
C
a a a
a a a
a a a
t L
t S
t C
−−−
⎡
⎣
⎢⎢⎢
⎤
⎦
⎥⎥⎥
=⎡
⎣
⎢⎢⎢
μμμ
11 12 13
21 22 23
31 32 33
⎤⎤
⎦
⎥⎥⎥
−−−
⎡
⎣
⎢⎢⎢
⎤
⎦
⎥⎥⎥
+( )( )( )
⎡
⎣
⎢⎢⎢
−
−
−
L
S
C
L
S
C
t L
t S
t C
t
t
t
1
1
1
μμμ
ηηη
⎤⎤
⎦
⎥⎥⎥, (3)
where t = 1, 2, . . . , T, μL, μS and μC denote means of the
three factors, and ηt(L),ηt(S) and ηt(C) are innovations to the
system.
The measurement equation, which expresses a set of N yields as
differentcombinations of the three latent factors, is given as:
y
y
y
e ee
t
t
t N
ττ
τ
λτ λτ
λτ λτλτ
11
2
1
11 11 1
( )( )
( )
⎡
⎣
⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥
=
− − −− −
−
�
11
22
2
11 1
11 1
2 2
− − −
− − −
− −−
− −−
e ee
e ee
N N
N N
λτ λτλτ
λτ λτλτ
λτ λτ
λτ λτ
� � �
NN
L
S
C
t
t
t
t
t
t N
⎡
⎣
⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎡
⎣
⎢⎢⎢
⎤
⎦
⎥⎥⎥
+
( )( )
ε τε τ
ε τ
1
2
�(( )
⎡
⎣
⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥
(4)
where t = 1, 2, . . . , T and εt(τ1), εt(τ2), . . . , εt(τN) are
measurement errors.In matrix notation, the former transition
equation and measurement equation
comprise the following state-space system:
f A ft t t− −( ) = ( ) +−m m h1 (5)
y ft t= +L et , (6)
where ft = (Lt, St, Ct)′ is a 3-dimensional state vector, yt =
(yt(τ1), yt(τ2), . . . ,yt(τN))′ is an N-dimensional observation
vector, A and Λ are 3 × 3 and N × 3coefficient matrices, and {ηt}
and {εt} are 3-dimensional and N-dimensionalwhite noise series.
For the Kalman filter to be linearly least-squares optimal, it
is assumed thatthe white noise transition and measurement
disturbances are orthogonal to eachother and uncorrelated with the
state vector:
he
t
t
QH
⎡⎣⎢
⎤⎦⎥
∼ ⎡⎣⎢
⎤⎦⎥
⎡⎣⎢
⎤⎦⎥
⎛⎝⎜
⎞⎠⎟
WN0
0
0
0, (7)
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 421
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E ft t′( ) =h 0 (8)
E ft t′( ) =e 0. (9)
It is further assumed that the variance–covariance matrix H of
innovations tothe measurement equation is diagonal and the
variance–covariance matrix Q ofinnovations to the transition
equation is non-diagonal. The assumption ofdiagonal H implies that
deviations of observed yields from the fitted yield curveare
uncorrelated with each other, and makes estimation more tractable
given alarge dimension of an observation vector. Meanwhile, the
assumption of non-diagonal Q allows the innovations to the three
latent factors to be correlated.
The former state-space system can be estimated using the Kalman
filter.Given initial values of the parameters, including μ, A, λ, Q
and H, the Kalmanfilter calculates one-step-ahead forecast errors
and variances of the forecasterrors from t = 2 through to t = T. A
log-likelihood function is computed usingthese forecast errors and
variances. Then, a standard numerical method, such asthe
Berndt–Hall–Hall–Hausman (BHHH), algorithm is applied to maximize
thelog-likelihood function by a convergence criterion (e.g. the
change in the log-likelihood function is not larger than 10−1 from
one iteration to the next.), andthe maximum likelihood estimates of
parameters can be obtained after conver-gence. Finally, the Kalman
smoother is used to compute the three latent factorsbased on
complete data set information from t = T through to t = 2.
(Harvey,1990 and Durbin and Koopman, 2012 provide comprehensive
introductions tothe state-space model and the Kalman filter.)
2.2. Vector autoregressive setting
A VAR model is used to investigate the relationship between the
yield curve andthe macroeconomy. As in previous studies (see e.g.
Chen, 2009; Huse, 2011;Favero et al., 2012), besides the three
latent factors, that is, level (L), slope (S)and curvature (C), we
also incorporate the following variables in the VARsystem:
inflation (pi), output growth (p), consumption growth (c),
investmentgrowth (i), foreign trade growth (e), government
expenditure growth (f), mon-etary policy interest rate (r) and
monetary aggregate growth (m).
A VAR(p) model is in the following form:
X c V Xt i t t= + +−=∑ ii
p
1
e , (10)
where Xt = (pit, pt, ct, it, et, ft, rt, mt, Lt, St, Ct)′ is an
11-dimensional vector, c is an11-dimensional vector of intercepts,
Vi is an 11 × 11 coefficient matrix and εt isan 11-dimensional
vector of disturbances.
The Cholesky decomposition of the variance–covariance matrix of
innova-tions in an impulse response analysis implies that the
innovations to variables inthe front rows of Xt have
contemporaneous impacts on variables in the back
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rows of Xt, but the innovations to variables in the back rows do
not havecontemporaneous impacts on variables in the front rows, so
variables should beordered from the most exogenous to the least
exogenous. As in Afonso andMartins (2012), it is assumed that
financial variables may be instantaneouslyaffected by shocks to
macroeconomic variables but the latter are not
affectedcontemporaneously by the former; therefore, the monetary
policy interest rate,monetary aggregate growth and the three latent
factors are placed in the last fivepositions. Considering that the
the monetary policy interest rate as an instru-ment instantaneously
affects monetary aggregate growth and that the monetarypolicy
interest rate does not instantaneously impact macroeconomic
variablesbecause of monetary policy lags, we place the monetary
policy interest ratebefore monetary aggregate growth and after
macroeconomic variables. More-over, we assume that shocks to
macroeconomic variables may instantaneouslyimpact fiscal variables
because of the automatic stabilizing function of fiscalpolicy, but
that a shock to fiscal variables does not have any immediate
macro-economic effect because of fiscal policy lags, so we place
government expendi-ture growth in the position immediately before
the monetary policy interest rate.Finally, we assume that shocks to
inflation and output may immediately affectsectors of consumption,
investment and foreign trade, but that shocks to sectorsof
consumption, investment and foreign trade do not have immediate
impactson inflation and output because of price rigidity and
capacity adjustment lags;thus, we put growth of consumption,
investment and foreign trade behindinflation and output growth.
3. EMPIRICAL ANALYSIS
3.1. Data
Because there are no existing zero coupon yield curves published
by the People’sBank of China, we have to use the original Treasury
bonds trading data1 toestimate zero coupon rates for 17 maturities
of 3, 6, 9, 12, 15, 18, 21, 24, 30, 36,48, 60, 72, 84, 96, 108 and
120 months, as in Diebold et al. (2006), who adopt thecommonly-used
Nelson–Siegel–Svensson method. Then the calculated zerocoupon rates
are used to compute the three latent factors: level, slope
andcurvature.
As in previous studies (see e.g. Dewachter and Lyrio, 2006;
Diebold and Li,2006; Diebold et al., 2008), we use start-of-month
Treasury bonds trading datafor the period 2002:1–2012:12 for
China’s exchange bond market to calculatezero coupon rates. There
are two reasons for choosing 2002 as the start of thesample period:
first, the rapid development of China’s bond market started
after2002, and there were not enough Treasury bonds traded per day
in China’s bondmarket before 2002 for the model estimation; second,
some of China’s monthly
1 To calculate zero coupon rates, this research collects the net
closing prices of all Treasury bondstraded on the first trading day
of each month from 2002 to 2012, as well as basic
informationregarding each bond, including par value, coupon rate,
coupon payment frequency and time tomaturity.
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macroeconomic statistics, such as the M2 growth rate and fixed
investmentgrowth rate, are not available for years prior to
2002.
Macroeconomic variables are comprised of the year-on-year
percentage rateof the change in the consumer price index (pi),2 the
growth rate of industrialproduction (p), the growth rate of total
retail sales of social consumer goods (c),the growth rate of fixed
asset investment (i), the growth rate of the total volumeof imports
and exports (e), the growth rate of government expenditure (f),
theweighted average interest rate of 7-day bond-pledged repos (r,
the monetarypolicy interest rate) and the M2 growth rate (m). All
of the growth rates men-tioned above are year-on-year percentage
growth rates. The GDP growth rate isa better indicator of output
growth than the industrial production growth rate,but it is only
calculated quarterly or annually, not monthly. Industrial
produc-tion growth is a reasonable proxy for output growth (see
e.g. Chen, 2009; Huse,2011; Favero et al., 2012). Data sources are
outlined in the Appendix.
3.2. Evaluating zero coupon rates
Before computing the three latent factors using the dynamic
Nelson–Siegelmodel, we need to fit a yield curve for each day in
the sample period using theNelson–Siegel–Svensson formula:
ye e
eeτ β β
λ τβ
λ τβ
λ τ λ τλ τ( ) = + −⎛
⎝⎜⎞⎠⎟
+ − −⎛⎝⎜
⎞⎠⎟
+ −− −
−−
1 21
31
41 1 11 1
1
λλ τλ τ
λ τ
22
2
−⎛⎝⎜
⎞⎠⎟
−e , (11)
where y(τ) denotes a set of yields, τ denotes maturity, and β1,
β2, β3, β4, λ1 and λ2are parameters. The estimated zero coupon
rates for each day in the sampleperiod are illustrated in Figure
1.
To further validate the efficiency of the Nelson–Siegel model,
we conductprincipal component analysis, which shows that the first
three principal com-ponents account for more than 99% of variations
in zero-coupon yields; they arethe level, slope and curvature
factor, respectively, according to their factorloadings.
3.3. Estimating the dynamic Nelson–Siegel model
Because we consider 17 maturities, yt is a 17-dimensional
vector, and there are 36parameters to be estimated using numerical
optimization (see Equation 5, 6 and7): 3 elements of the
3-dimensional mean state vector μ, 9 elements of the 3 ×
3transition matrix A, 1 element (λ) of the 17 × 3 measurement
matrix Λ, 6elements of the 3 × 3 symmetric variance–covariance
matrix of the transitionsystem innovations, Q, and 17 elements of
the 17 × 17 diagonal variance–covariance matrix of the measurement
innovations, H.
2 The year-on-year percentage rate of change of the consumer
price index is frequently quoted as theindicator of the inflation
rate.
Y. YAN AND J. GUO424
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Given the initial values of the state-space model, we use the
Kalman filter tocalculate the Gaussian log-likelihood function, and
iterate the BHHH algorithmto maximize the log-likelihood function
according to a convergence criterion of10−1 for the change in the
log-likelihood function from one iteration to the next.We impose
non-negativity on all estimated variances through estimating
logvariances, and initialize all variances at 1. We estimate the
three latent factorsand λ for each day using the Nelson–Siegel
model, then initialize the mean statevector μ using the means of
the estimated three latent factors series, initialize λusing the
mean of the estimated λ series, and initialize the transition
equationmatrix A using the VAR(1) coefficient matrix of the
estimated three latentfactors series.
The estimate of λ is 0.207339, which implies that the curvature
Ct reaches itsmaximum at maturity of 9 months and that the slope St
decays quickly. Figure 2illustrates loadings of the level, slope
and curvature at each maturity. Comparedwith estimates of λ in
Diebold and Li (2006), Diebold et al. (2006) and Afonsoand Martins
(2012) implying maximums of Ct at 23, 29 or 48 months for theUSA
and at 43 months for Germany, the maturity of 9 months at which
Ct
6
5
4
3
2
1
0
Zero
rate
s
January2012January2010
January2008January2006
January2004January2002
Time0
24
68
10
Maturities (i
n years)
Figure 1. Zero coupon rates at each month, 2002:1–2012:12Notes:
The figure shows the estimated zero coupon rates with
maximalmaturity of 10 years at each month from January 2002 to
December 2012;time, the month when zero coupon rates are
calculated; maturities, maturitiesof 3, 6, 9, 12, 15, 18, 21, 24,
30, 36, 48, 60, 72, 84, 96, 108 and 120 months,here expressed in
years; zerorates, corresponding zero coupon rates, expressedin
percentages.
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 425
© 2015 Wiley Publishing Asia Pty Ltd
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reaches its maximum is much smaller in China. This pattern is
consistent withtrading by much less patient individuals in the
exchange market (Porter andCassola, 2011).
Figure 3 shows the estimates of the three latent factors which
are calculatedusing the Kalman smoother after convergence of
maximum-likelihood estima-tion. The level peaks during high
inflation periods from the end of 2003 to
1.0
0.8
0.6
0.4
0.2
0.0
0 20 40 60 80 100 120Maturities (in months)
Level loading
Slope loading
Curvature loading
1–e–λτλτ
1–e–λτ–e–λτλτ
Figure 2. Loadings of the level, slope and curvature factors,
2002:1–2012:12Notes: The figure shows the loading of each latent
factor at each maturity,expressed in months; LEVEL, the level of
the yield curve; SLOPE, the slopeof the yield curve; CURVATURE, the
curvature of the yield curve.
64
20
–2–4
–6
2002 2004 2006 2008 2010 2012
LEVELSLOPECURVATURE
Figure 3. Estimates of the level, slope and curvature factors of
yield curves,2002:1–2012:12Notes: The figure shows the values of
the three latent factors at each monthfrom January 2002 to December
2012; LEVEL, the level of the yield curve;SLOPE, the slope of the
yield curve; CURVATURE, the curvature of theyield curve.
Y. YAN AND J. GUO426
© 2015 Wiley Publishing Asia Pty Ltd
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mid-2005 and from the second half of 2007 to the end of 2008,
decreases sharplywith the deflation at the beginning of 2009, and
during other periods the level israther stable. The slope is
consistently negative, which implies ascending yieldcurves, and it
has an apparent negative correlation with the level from 2003
to2008. The curvature displays much higher variation than the level
and the slope,with an apparent positive correlation with the level
since 2005.
To further check the efficiency of our estimates of the three
latent factors, wecompare the three latent factors with their
empirical proxies:
Level yt= ( )120 (12)
Slope y yt t= −( ) ( )3 120 (13)
Curvature y y yt t t= − −( ) ( ) ( )2 9 3 120 , (14)
where yt(τ) denotes a zero coupon rate with maturity of τ
months, and the 9months maturity of yt(9) is chosen according to
the estimate of λ. Figure 4 showsthat the three latent factors move
in line with their corresponding empiricalproxies. Furthermore, the
correlations between the level, slope, curvature andtheir empirical
counterparts are 96, 80 and 69%, respectively, and they are at
asimilar size to previous studies (see e.g. Afonso and Martins,
2012).
3.4. Impulse response analysis
After obtaining the three latent factors of yield curves, we
continue to estimatea VAR system consisting of eight macro
variables and the three latent factors.Based on the Schwarz and
Hannan–Quinn information criteria together with aresidual
autocorrelation analysis, we estimate a VAR(1) model for the
data.Then we report impulse response functions to Cholesky one
standard deviationinnovations along with 95% confidence intervals.
Three categories of impulseresponses will be considered in turn:
macroeconomic responses to macroeco-nomic surprises, yield curve
responses to macroeconomic surprises and macro-economic responses
to yield curve surprises.
3.4.1. Interactions among macroeconomic variablesFigure 5
displays significant macroeconomic responses to
macroeconomicshocks. First, a positive shock to inflation leads to
rising interest rates becausemonetary policy is tightened to
control inflation. Consumption growth immedi-ately increases
following a positive inflation surprise because rising
pricesdirectly increase payment for consumer goods. Second, a
positive shock toindustrial production growth pushes up foreign
trade because foreign countriesare important resource providers and
consumption markets for China rightnow. Enhancing industrial
production growth also increases inflation anddecreases money
supply growth because stimulative monetary policies areexpected to
quit under a flourishing economy. Third, a positive shock to
invest-
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 427
© 2015 Wiley Publishing Asia Pty Ltd
-
ment growth raises industrial production growth gradually and
enhances moneysupply growth because of rising loans for financing
the investment. Risinginvestment growth also leads to more rapid
growth of consumption afterapproximately 6 months. Fourth, a
positive shock to foreign trade growthdecreases money supply
growth. When foreign trade performance is good,monetary policy will
be tightened. Fifth, a positive shock to governmentexpenditure
growth reduces the growth of foreign trade because domesticdemand
is expanded by government. Higher government expenditure growthalso
leads to an immediate increase in money supply, and active fiscal
policy isoften accompanied by easing monetary policy. Sixth, a
positive shock to mon-etary policy interest rate decreases money
supply growth, as expected. Surpris-ingly, a rising monetary policy
interest rate pushes up inflation slightly ratherthan reducing
inflation; this result is consistent with Diebold et al. (2006),
whoattribute this finding to the market’s future inflation
expectations boosted by thecentral bank’s concern about overheating
and inflationary pressures. Finally, a
0.02
50.
040
y (1
20)
2002 2004 2006 2008 2010 2012
2.5
3.5
4.5
LEVE
LLEVELy (120)
–0.0
3–0
.01
y (3
) – y
(120
)
2002 2004 2006 2008 2010 2012
–5–3
–10
SLO
PE
SLOPEy (3) – y (120)
–0.0
20–0
.005
2002 2004 2006 2008 2010 2012
–20
12
3C
URV
ATU
RECURVATURE2y (9) – y (3) – y (120)
2y (9
) – y
(3) –
y (1
20)
Figure 4. The level, slope, curvature factors and their
empirical proxiesNotes: y(3), a zero coupon rate with maturity of 3
months; y(9), a zerocoupon rate with maturity of 9 months; y(120),
a zero coupon rate withmaturity of 120 months; LEVEL, the level of
the yield curve; SLOPE, theslope of the yield curve; CURVATURE, the
curvature of the yield curve.
Y. YAN AND J. GUO428
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positive shock to money supply growth increases inflation
gradually andenhances consumption growth after approximately 6
months.
The previous interactions among macroeconomic variables indicate
the fol-lowing general results. First, Chinese monetary policy is
mainly conductedthrough quantitative measures instead of
market-based measures, and moneysupply growth is a more effective
instrument to curb inflation than the monetarypolicy interest rate.
Second, investment is still an important measure to stimulatethe
Chinese economy, but it also pushes up money supply growth, which
willresult in higher inflation. Adjusting the interest rate is an
option for the centralbank to stabilize money supply. Finally,
Chinese authorities conduct monetaryand fiscal policies to boost
economic growth and stabilize prices.
3.4.2. Yield curve responses to macroeconomic surprisesThe
responses of yield curves to macroeconomic surprises are shown
inFigure 6. First, a surprise increase in inflation leads to a very
brief rise in thecurvature (a more concave yield curve) during the
7th and 10th months. Theresponse of the level to a positive
inflation shock is not significant, although thelevel tends to rise
with inflation. The long-term inflation is influenced little
bytransitory changes in short-term inflation. This reaction is
consistent with Porter
0.0
0.4
0.8
5 10 15 20 25
Response of c to pi
–0.0
50.
050.
155 10 15 20 25
Response of r to pi
–0.1
0.1
0.3
5 10 15 20 25
Response of pi to p
–0.6
–0.2
5 10 15 20 25
Response of m to p
02
46
5 10 15 20 25
Response of e to p–0
.20.
20.
61.
0
5 10 15 20 25
Response of p to i–0
.50.
00.
5
5 10 15 20 25
Response of c to i
–0.4
0.0
0.4
5 10 15 20 25
Response of m to i
–6–4
–20
1
5 10 15 20 25
Response of e to f
–0.6
–0.2
0.2
5 10 15 20 25
–0.2
0.0
0.2
0.4
5 10 15 20 25
–0.1
0.1
0.2
5 10 15 20 25
–0.4
–0.2
0.0
0.2
5 10 15 20 25
–0.1
0.1
0.3
5 10 15 20 25–0.
10.
10.
35 10 15 20 25
Response of m to e
Response of m to f Response of pi to r Response of m to r
Response of c to mResponse of pi to m
Figure 5. Macro surprises and responsesNotes: pi, inflation; p,
growth rate of industrial production; c, growth rate oftotal retail
sales of social consumer goods; i, growth rate of fixed
assetinvestment; e, growth rate of total volume of imports and
exports; f, growthrate of government expenditure; r, the weighted
average interest rate of 7-daybond-pledged repos; m, M2 growth
rate.
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 429
© 2015 Wiley Publishing Asia Pty Ltd
-
and Cassola (2011), who point out that long-term inflation is
more stronglyanchored in China than in the USA. Second, there are
no statistically significantresponses of the yield curve to
innovations to industrial production growth andconsumption growth.
Third, a surprise increase in investment growth pushes upthe level
and reduces the slope (a steeper yield curve). Higher investment
growthenhances the market’s long-term perception of inflation.
Short-term yields
–0.0
40.0
00.
040.
08
5 10 15 20 25
Response of L to pi
–0.0
20.
020.
065 10 15 20 25
Response of L to p
–0.0
6–0
.02
0.02
5 10 15 20 25
Response of L to c
0.00
0.05
0.10
5 10 15 20 25
Response of L to i
–0.0
40.
000.
04
5 10 15 20 25
Response of L to e–0
.04
0.00
0.04
5 10 15 20 25
Response of L to f–0
.04
0.00
0.04
5 10 15 20 25
Response of L to r
–0.0
20.
020.
065 10 15 20 25
Response of L to m
–0.1
5–0
.05
0.05
5 10 15 20 25
Response of S to pi
–0.1
00.
00
5 10 15 20 25
Response of S to p
–0.1
00.
000.
10
5 10 15 20 25
Response of S to c
–0.2
5–0
.15
–0.0
50.
05
5 10 15 20 25
Response of S to i
–0.0
50.
05
5 10 15 20 25
Response of S to e
–0.1
00.
005 10 15 20 25
Response of S to f
–0.0
50.0
00.
050.
10
5 10 15 20 25
Response of S to r
–0.1
00.
000.
05
5 10 15 20 25
Response of S to m
–0.0
50.
05
5 10 15 20 25
Response of C to pi
–0.1
50.
100.
00
5 10 15 20 25
Response of C to p
–0.1
50.
000.
10
5 10 15 20 25
Response of C to c
–0.1
00.
000.
10
5 10 15 20 25
Response of C to e
–0.2
0–0
.05
0.10
5 10 15 20 25
Response of C to f
–0.0
50.
150.
05
5 10 15 20 25
Response of C to r
–0.1
00.
000.
10
5 10 15 20 25
Response of C to m
–0.1
00.0
00.
10
5 10 15 20 25
Response of C to i
Figure 6. Macro surprises and the level, slope, curvature
responsesNotes: pi, inflation; p, growth rate of industrial
production; c, growth rate oftotal retail sales of social consumer
goods; i, growth rate of fixed assetinvestment; e, growth rate of
total volume of imports and exports; f, growthrate of government
expenditure; r, the weighted average interest rate of
7-daybond-pledged repos; m, M2 growth rate; L, the level of the
yield curve; S, theslope of the yield curve; C, the curvature of
the yield curve.
Y. YAN AND J. GUO430
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decrease because projects with lower returns are carried out due
to expandedinvestment; as a result, the slope is lowered and the
yield curve becomes steeper.Fourth, the yield curve is not affected
significantly by innovations to foreigntrade and government
expenditure growth. This result reflects inefficiency
ofimport–export and fiscal policies in shaping the yield curve.
Fifth, there is nosignificant response of the yield curve to an
innovation to monetary policyinterest rate. In practice, the open
market operation instrument of the monetarypolicy interest rate is
determined in China’s interbank market, and the yieldcurve in this
study is based on China’s exchange market. The result that
thesurprise increase in monetary policy interest rate does not
affect the yield curvereflects not only the segmentation between
the interbank bond market and theexchange bond market but also the
weakness of the transmission channel ofmonetary policy in China.
Finally, a positive innovation to money supplygrowth increases the
level and decreases the slope, but the responses are
barelysignificant. Higher money supply growth pushes up the
market’s perception oflong-term inflation; as a result, the level
of the yield curve rises. The cost ofshort-term financing is
lowered because of monetary expansion, so the slope ofthe yield
curve decreases.
The previous yield curve responses to macroeconomic surprises
reflect thefollowing important conclusions. First, China’s
long-term inflation is stronglyanchored and is affected little by
transitory changes of short-term inflation.Second, macroeconomic
variables, including industrial production growth,import–export
growth and government expenditure growth, do not have signifi-cant
influences on the yield curve in China. Third, there are
significant responsesof the yield curve to investment growth and
money supply growth. Higherinvestment growth increases long-term
inflation and decreases short-term ratesof return. Higher money
supply growth also pushes up long-term inflation andreduces
short-term financing costs. Finally, the open market operation
con-ducted in China’s interbank market through monetary policy
interest rates doesnot reshape the yield curve in China’s exchange
market. The result reflects thatthe segmentation of China’s bond
market hinders the efficient implementationof monetary policy, and
that the transmission channel of monetary policy is stillweak in
China.
3.4.3. Macroeconomic responses to yield curve surprisesNow
consider responses of macroeconomic variables to yield curve shocks
(seeFigure 7). A positive shock to the level leads to an increase
in consumptiongrowth, and such a reaction is consistent with Zhang
and Wan (2002), who statethat higher expected inflation will
discourage asset accumulation and increasecurrent spending.
Surprisingly, there are no statistically significant responses
ofmacroeconomic variables to a positive shock to the slope. A
positive shock tothe curvature pushes up consumption growth and
reduces the monetary policyinterest rate, and the responses are
barely significant.
The previous macroeconomic responses to yield curve surprises
presentimportant observations. First, an increase in the level,
which is interpreted as themarket’s perception of long-term
inflation, does not enhance short-term infla-
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 431
© 2015 Wiley Publishing Asia Pty Ltd
-
tion or hamper industrial production growth as expected, and
higher requiredreturn on investment does not reduce investment
growth either. In addition, thehigher level induces no significant
responses of the monetary policy interest rateand money supply
growth. It appears that the monetary authority is not con-cerned
about the level of the yield curve in the exchange market. Perhaps
themonetary authority focuses on short-term inflation rather than
long-term infla-
–0.4
–0.2
0.0
0.2
Response of pi to L
5 10 15 20 25 –0.
5–0
.3–0
.10.
1
Response of p to L
5 10 15 20 25
–0.2
0.2
0.6
Response of c to L
5 10 15 20 25
–0.5
0.5
1.5
Response of i to L
5 10 15 20 25
–2.0
–1.0
0.0
1.0
Response of e to L
5 10 15 20 25
–1.5
–0.5
0.5
Response of f to L
5 10 15 20 25
–0.1
00.
000.
10Response of r to L
5 10 15 20 25 –0.
40.
00.
20.
4 Response of m to L
5 10 15 20 25 –0.
100.
000.
10
Response of pi to S
5 10 15 20 25
–0.2
0.0
0.2
0.4
Response of p to S
5 10 15 20 25
–0.4
–0.2
0.0
0.2
Response of c to s
5 10 15 20 25
–0.5
0.0
0.5
1.0
Response of i to S
5 10 15 20 25
–1.5
–0.5
0.5
Response of e to S
5 10 15 20 25–1
.00.
01.
0
Response of f to S
5 10 15 20 25 –0.
100.
000.
10
Response of r to S
5 10 15 20 25
–0.2
0.0
0.2
Response of m to s
5 10 15 20 25 –0.
25–0
.10
0.00
Response of pi to S
5 10 15 20 25
–0.4
–0.2
0.0
Response of p to C
5 10 15 20 25
–0.2
0.2
0.4
Response of c to C
5 10 15 20 25
–1.5
–0.5
0.5
Response of e to C
5 10 15 20 25
–2.5
–1.5
–0.5
0.5
Response of f to C
5 10 15 20 25
–0.1
5–0
.05
Response of r to C
5 10 15 20 25
–0.1
0.1
0.3
Response of m to C
5 10 15 20 25
–1.0
0.0
1.0
Response of i to C
5 10 15 20 25
Figure 7. The level, slope, curvature surprises and the macro
responsesNotes: pi, inflation; p, growth rate of industrial
production; c, growth rate oftotal retail sales of social consumer
goods; i, growth rate of fixed assetinvestment; e, growth rate of
total volume of imports and exports; f, growthrate of government
expenditure; r, the weighted average interest rate of
7-daybond-pledged repos; m, M2 growth rate; L, the level of the
yield curve; S, theslope of the yield curve; C, the curvature of
the yield curve.
Y. YAN AND J. GUO432
© 2015 Wiley Publishing Asia Pty Ltd
-
tion, or the yield curve in the exchange market is not an
efficient benchmarkyield curve for conducting monetary policy.
Besides, the increasing level doesnot affect investment growth
significantly, which means the yield curve does notaccurately
reflect the financing cost. As China’s interest rates have not been
fullyliberalized yet, in practice, the regulated official deposit
and loan interest ratesare better indicators of the financing cost.
Second, there are no significantresponses of the monetary policy
interest rate and money supply growth to theslope surprise, and
this result reaffirms that the interrelation between openmarket
operations conducted in the interbank market and the yield curve in
theexchange market is very weak. Third, it is shown in Subsection
3.4.2 that thecurvature reacts significantly to innovations to
inflation and industrial produc-tion growth, but in this subsection
we do not find significant responses ofinflation and industrial
production growth to an innovation to the curvature.Finally, it is
evident that interactions between the yield curve and
themacroeconomy in China are nearly unidirectional rather than
bidirectional.Macroeconomic variables reshape the yield curve, but
direct adjustments of theyield curve do not lead to significant
changes in macroeconomic variables. Dueto the incomplete
liberalization of interest rates and market segmentation, theyield
curve does not accurately reflect the cost of capital, and it is
not an effectivebenchmark yield curve. As a result, there exists a
wide disjunction between thereal economy and financial markets in
China.
3.5. Forecast error variance decompositions
Panel 1 in Table 1 shows that inflation surprises explain more
than 70% of thevariance of the errors in forecasting inflation at a
4-month horizon. Meanwhile,innovations to industrial production
growth stay around 20% for forecast hori-zons of 4 months and
beyond. The money supply innovations become more andmore important
in explaining that forecast error variance from 2.93% at a4-month
horizon to above 12% at a 24-month horizon. The importance
ofinnovations to the level of the yield curve keeps rising and
reaches above 5% ata 24-month horizon.
From panel 2 in Table 1, more than 72% of the forecast error
variance ofindustrial production growth is explained by innovations
to industrial produc-tion growth itself during a 24-month horizon.
Besides, the importance of inno-vations to investment growth
stabilizes at around 12% and that of innovationsto the monetary
policy interest rate remains at around 4.5%.
Panel 3 in Table 1 shows that approximately 73% of the variance
of the errorsin forecasting consumption growth at a 4-month horizon
is explained by inno-vations to consumption growth itself. From the
4-month horizon onwards, thepart explained by consumption growth
surprises reduces gradually to approxi-mately 40% at a 24-month
horizon. This reduction is mainly attributed toinnovations to
investment growth, money supply growth and the level factor.The
importance of innovations to investment growth stays above 16% from
the16-month horizon onwards. Innovations to the level factor and
inflation alsoexplain some of the forecast error variance; both
contributions are above 10%.
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 433
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-
Tab
le1.
For
ecas
ter
ror
vari
ance
deco
mpo
siti
ons
Per
iod
pip
ci
ef
rm
LS
C
1.F
orec
asti
ngin
flati
on(p
i)4
70.8
418
.56
0.68
0.74
1.37
0.03
3.53
2.93
0.61
0.47
0.24
857
.20
22.8
01.
144.
251.
400.
132.
507.
550.
430.
601.
9912
53.0
021
.99
1.35
5.65
1.18
0.37
1.78
10.4
11.
360.
482.
4316
51.6
320
.40
1.32
5.52
1.01
0.65
1.50
12.0
43.
120.
482.
3420
50.9
119
.18
1.26
5.17
1.00
0.89
1.39
12.7
14.
760.
502.
2324
50.2
918
.56
1.22
5.08
1.11
1.04
1.35
12.8
05.
880.
512.
162.
For
ecas
ting
grow
thra
teof
indu
stri
alpr
oduc
tion
(p)
40.
4580
.13
1.47
11.3
00.
910.
223.
270.
380.
540.
500.
838
0.57
74.9
51.
3212
.68
2.14
0.34
4.24
1.07
1.43
0.46
0.79
120.
5973
.66
1.30
12.8
22.
220.
334.
461.
491.
870.
480.
7816
0.60
73.3
21.
2912
.77
2.22
0.35
4.53
1.57
2.07
0.48
0.78
200.
6573
.15
1.30
12.7
72.
260.
364.
541.
572.
130.
480.
8024
0.74
72.9
81.
3012
.79
2.29
0.36
4.52
1.59
2.13
0.48
0.81
3.F
orec
asti
nggr
owth
rate
ofto
talr
etai
lsal
esof
soci
alco
nsum
ergo
ods
(c)
47.
363.
8472
.78
3.38
0.79
1.43
0.92
0.73
4.94
0.65
3.17
87.
325.
8654
.94
9.12
0.63
1.23
1.14
3.39
12.6
70.
922.
7912
8.15
6.38
45.3
914
.66
0.57
1.51
1.21
5.42
13.1
60.
802.
7516
9.38
6.46
41.6
116
.44
0.60
1.82
1.24
6.76
12.2
50.
742.
7220
10.2
66.
3640
.03
16.6
60.
682.
051.
247.
5211
.79
0.73
2.68
2410
.71
6.26
39.3
616
.52
0.79
2.20
1.24
7.84
11.6
90.
732.
654.
For
ecas
ting
grow
thra
teof
fixed
asse
tin
vest
men
t(i
)4
2.47
0.39
2.57
88.6
01.
890.
031.
210.
661.
980.
100.
108
2.29
0.88
2.78
85.6
81.
940.
051.
561.
452.
900.
140.
3412
2.26
0.93
2.79
85.1
11.
900.
111.
721.
792.
870.
130.
3916
2.25
0.94
2.78
84.8
81.
920.
161.
781.
902.
860.
140.
3920
2.25
0.98
2.78
84.7
51.
960.
191.
791.
922.
860.
140.
3924
2.26
1.03
2.77
84.6
71.
990.
191.
791.
922.
860.
140.
39
Y. YAN AND J. GUO434
© 2015 Wiley Publishing Asia Pty Ltd
-
5.F
orec
asti
nggr
owth
rate
ofto
talv
olum
eof
impo
rts
and
expo
rts
(e)
40.
8431
.16
0.46
2.13
51.0
712
.66
0.23
0.27
0.41
0.04
0.72
80.
7832
.29
0.43
4.51
46.4
311
.38
0.52
1.09
1.75
0.05
0.78
120.
9231
.82
0.42
5.01
44.7
210
.83
0.76
1.78
2.95
0.05
0.75
160.
9531
.43
0.41
4.99
44.1
210
.68
0.87
2.02
3.72
0.06
0.74
200.
9631
.32
0.42
5.02
43.8
510
.62
0.91
2.03
4.06
0.07
0.75
241.
0331
.31
0.43
5.13
43.6
210
.56
0.91
2.04
4.13
0.07
0.77
6.F
orec
asti
nggr
owth
rate
ofgo
vern
men
tex
pend
itur
e(f
)4
5.24
2.90
4.87
1.78
5.94
77.0
01.
360.
010.
220.
200.
498
6.31
2.84
4.75
2.34
5.93
75.1
71.
500.
010.
460.
210.
4912
6.79
2.88
4.71
2.39
5.88
74.5
11.
560.
050.
520.
220.
5016
7.06
2.93
4.69
2.38
5.85
74.1
11.
570.
100.
580.
220.
5120
7.21
2.96
4.68
2.38
5.83
73.8
71.
560.
140.
650.
220.
5124
7.27
2.96
4.67
2.37
5.82
73.7
41.
560.
160.
700.
220.
517.
For
ecas
ting
the
wei
ghte
dav
erag
ein
tere
stra
teof
7-da
ybo
nd-p
ledg
edre
pos
(r)
47.
301.
533.
071.
950.
010.
0581
.32
0.23
0.23
0.63
3.68
816
.00
3.37
3.96
2.04
0.17
0.07
65.5
90.
582.
070.
555.
6112
20.2
95.
083.
621.
960.
440.
0656
.76
1.23
4.98
0.48
5.11
1622
.08
5.68
3.38
1.89
0.51
0.06
52.3
51.
876.
920.
484.
7920
22.7
05.
713.
251.
900.
490.
0850
.30
2.23
8.23
0.48
4.62
2422
.80
5.63
3.19
2.01
0.49
0.10
49.3
92.
349.
010.
494.
548.
For
ecas
ting
M2
grow
thra
te(m
)4
1.51
5.06
1.80
8.31
7.83
8.57
5.16
61.0
10.
510.
220.
038
1.14
15.0
01.
375.
8016
.42
11.5
36.
5140
.67
0.86
0.23
0.46
122.
6321
.95
1.38
5.49
18.8
210
.72
5.60
31.5
10.
710.
190.
9816
4.89
24.4
61.
426.
1918
.32
9.48
4.84
28.0
50.
950.
161.
2420
6.69
24.6
61.
416.
4617
.36
8.75
4.47
27.0
71.
650.
171.
3124
7.69
24.2
21.
396.
3716
.74
8.46
4.32
26.8
42.
470.
181.
31
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 435
© 2015 Wiley Publishing Asia Pty Ltd
-
Tab
le1.
Con
tinu
ed
Per
iod
pip
ci
ef
rm
LS
C
9.F
orec
asti
ngth
ele
velo
fth
eyi
eld
curv
e(L
)4
0.48
2.91
0.10
14.5
70.
070.
470.
080.
9579
.50
0.02
0.87
82.
855.
790.
6425
.18
0.05
0.69
0.11
2.93
60.1
10.
021.
6312
6.00
7.48
1.00
28.2
10.
050.
940.
175.
2248
.98
0.02
1.93
168.
628.
011.
1428
.32
0.05
1.22
0.21
7.02
43.3
30.
042.
0420
10.2
77.
961.
1727
.65
0.10
1.46
0.24
8.09
40.9
70.
072.
0424
11.0
47.
791.
1627
.07
0.20
1.63
0.25
8.55
40.2
10.
082.
0110
.F
orec
asti
ngth
esl
ope
ofth
eyi
eld
curv
e(S
)4
0.18
1.65
3.49
8.36
0.34
2.09
1.36
1.45
17.2
549
.20
14.6
48
0.64
1.37
2.17
19.7
50.
882.
111.
614.
5429
.86
28.0
39.
0416
3.20
1.86
2.00
26.2
51.
273.
021.
606.
9726
.85
19.7
97.
2120
4.18
1.90
1.99
26.2
81.
373.
241.
587.
6325
.82
19.0
27.
0024
4.72
1.88
1.98
26.0
61.
473.
381.
577.
9525
.40
18.7
06.
9011
.F
orec
asti
ngth
ecu
rvat
ure
ofth
eyi
eld
curv
e(C
)4
0.28
1.29
1.02
0.97
0.45
1.32
1.27
0.33
13.9
355
.78
23.3
68
2.07
2.92
2.55
1.98
0.53
1.15
1.58
0.31
13.2
547
.88
25.7
712
3.91
4.22
2.58
1.95
0.91
1.10
1.63
0.53
13.3
245
.38
24.4
716
5.07
4.79
2.53
1.92
1.01
1.07
1.60
0.93
13.4
343
.94
23.7
220
5.69
4.91
2.49
1.90
1.00
1.06
1.57
1.20
13.6
943
.16
23.3
324
5.92
4.90
2.48
1.89
0.99
1.07
1.55
1.32
13.9
442
.80
23.1
3
Not
es:
pi,i
nflat
ion;
p,gr
owth
rate
ofin
dust
rial
prod
ucti
on;c
,gro
wth
rate
ofto
talr
etai
lsal
esof
soci
alco
nsum
ergo
ods;
i,gr
owth
rate
offix
edas
set
inve
stm
ent;
e,gr
owth
rate
ofto
talv
olum
eof
impo
rts
and
expo
rts;
f,gr
owth
rate
ofgo
vern
men
texp
endi
ture
;r,t
hew
eigh
ted
aver
age
inte
rest
rate
of7-
day
bond
-ple
dged
repo
s;m
,M2
grow
thra
te;
L,t
hele
velo
fth
eyi
eld
curv
e;S
,the
slop
eof
the
yiel
dcu
rve;
C,t
hecu
rvat
ure
ofth
eyi
eld
curv
e.E
ach
row
show
sth
epe
rcen
tage
ofth
eva
rian
ceof
the
erro
rin
fore
cast
ing
the
vari
able
men
tion
edin
the
titl
eof
the
tabl
e,at
each
fore
cast
ing
hori
zon
(in
mon
ths)
give
nin
the
first
colu
mn.
Y. YAN AND J. GUO436
© 2015 Wiley Publishing Asia Pty Ltd
-
Panel 4 in Table 1 presents the forecast error variance
decomposition ofinvestment growth. Almost all of the forecast error
variance is explained byinnovations to investment growth itself.
Innovations to other variables accountfor a negligible part of the
forecast error variance. Among them, the contribu-tions of
surprises to the level factor and consumption growth stay above
2.5%.
Panel 5 in Table 1 shows that the variance of the errors in
forecasting import–export growth is explained by surprises to
import–export growth itself, and, toa lesser extent, by surprises
to industrial production growth. The importance ofimport–export
growth surprises falls gradually, from approximately 51% at
a4-month horizon to approximately 44% at a 24-month horizon, while
that ofinnovations to industrial production growth stabilizes at
around 31% for fore-cast horizons of 4 months and beyond.
Innovations to government expendituregrowth account for
approximately 11% from the 8-month horizon onwards, andthe
importance of surprises to investment growth rises from
approximately 2%at a 4-month horizon to above 5% at a 24-month
horizon.
From panel 6 in Table 1, more than 73% of the variance of the
errors inforecasting government expenditure growth is explained by
innovations to gov-ernment expenditure growth itself. The
importance of surprises to industrialproduction growth and
import–export growth remains above 5% from the4-month horizon
onwards.
Panel 7 in Table 1 shows that approximately 81% of the forecast
error vari-ance of the monetary policy interest rate is explained
by innovations to themonetary policy interest rate itself at a
4-month horizon; meanwhile, approxi-mately 7% is attributed to
inflation surprises. The importance of surprises to themonetary
policy interest rate diminishes below 50% at a 24-month
horizon;meanwhile, the contribution of the inflation surprises
increases to above 22%. Inaddition, the part explained by
innovations to the level factor becomes increas-ingly large, and
approaches 10% at a 24-month horizon.
From panel 8 in Table 1, we can see that approximately 61% of
the forecasterror variance of money supply growth is explained by
surprises to moneysupply growth at a 4-month horizon; meanwhile,
approximately 5% of theforecast error variance is attributed to
innovations to industrial productiongrowth. At a 24-month horizon,
the importance of surprises to money supplygrowth falls gradually
below 27%; meanwhile, that of innovations to industrialproduction
growth exceeds 24%. The part explained by surprises to
import–export growth rises from below 8% at a 4-month horizon to
nearly 17% at a24-month horizon. Innovations to government
expenditure growth account forapproximately 8% of the variance of
the errors in forecasting money supplygrowth at a 24-month horizon,
while the importance of innovations to themonetary policy interest
rate stabilizes at around 5%.
Panel 9 of Table 1 presents the forecast error variance
decomposition of thelevel of the yield curve. At a 4-month horizon,
innovations to the level explainnearly 80% of the variance of the
errors in forecasting the level; meanwhile,approximately 15% of the
forecast error variance is attributed to investmentsurprises. From
the 4-month horizon onwards, the importance of innovations tothe
level reduces to approximately 40%, while that of investment
surprises
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 437
© 2015 Wiley Publishing Asia Pty Ltd
-
stabilizes at around 27%. The part explained by inflation
surprises rises fromapproximately 0.5% at a 4-month horizon to
above 11% at a 24-month horizon,and innovations to industrial
production growth and money supply growth alsoaccount for
approximately 8 and 8.6%, respectively, at a 24-month horizon.
Panel 10 in Table 1 shows that at a 4-month horizon, almost half
of thevariance of the errors in forecasting the slope is attributed
to the slope surprises;meanwhile, approximately 17% is explained by
the level surprises and approxi-mately 8% is driven by investment
shocks. The importance of the slope surprisesfalls below 19% at a
24-month horizon, and that of the level surprises is rela-tively
stable at around 25%. Investment surprises become another driver of
thevariance of the errors in forecasting the slope at a 24-month
horizon, and theimportance stabilizes at around 26%.
From panel 11 in Table 1, the curvature surprises explain
approximately 23%of the variance of the errors in forecasting the
curvature at a 4-month horizon,and the importance of the curvature
surprises stabilizes at around 23% at a24-month horizon after
increasing slightly at an 8-month horizon. The slopesurprises are
the main driver of the variance of the errors in forecasting
thecurvature. The slope surprises account for approximately 56% of
the forecasterror variance at a 4-month horizon, and their
importance reduces gradually toapproximately 43% at a 24-month
horizon. Moreover, the importance of thelevel surprises stabilizes
at around 13% for forecast horizons of 4 months andbeyond.
4. CONCLUSION
This paper studies the sovereign yield curve and its
interactions with themacroeconomy in China for the period January
2002–December 2012. Almostall variations in China’s zero-coupon
yields can be explained by the first threeprincipal components,
which can be interpreted as the level, slope and curvatureof the
yield curve. We estimate these three time-varying latent factors in
thedynamic Nelson–Siegel model. We further establish a VAR system
consisting ofmacroeconomic variables and the three factors to
explore interactions amongvariables by impulse response analysis.
Our findings are summarized as follows.
Chinese authorities conduct monetary and fiscal policies to
boost economicgrowth and stabilize prices. In China, money supply
growth is a more effectiveinstrument to curb inflation than the
monetary policy interest rate, and mon-etary policy is mainly
conducted through quantitative measures instead ofmarket-based
measures. Investment is still an important measure to stimulatethe
Chinese economy, but it also pushes up money supply growth, which
resultsin higher inflation. Adjusting the monetary policy interest
rate is an option forthe central bank to stabilize money supply.
However, raising the monetarypolicy interest rate pushes inflation
up slightly rather than reducing inflation,because the market’s
future inflation expectations are boosted by the centralbank’s
concern about overheating and inflationary pressures.
China’s long-term inflation is strongly anchored and is affected
little bytransitory changes in short-term inflation. Macroeconomic
variables, including
Y. YAN AND J. GUO438
© 2015 Wiley Publishing Asia Pty Ltd
-
industrial production growth, import–export growth and
government expendi-ture growth, do not have significant influence
on the yield curve in China, butthe yield curve reacts
significantly to innovations to investment growth andmoney supply
growth. Both higher investment growth and higher money supplygrowth
push up long-term inflation and decrease short-term yields. The
openmarket operations through the monetary policy interest rate
conducted in theinterbank market do not reshape the yield curve in
the exchange market. Thesegmentation of China’s bond market hinders
the efficient implementation ofmonetary policy, and the monetary
policy transmission mechanism is still weakin China.
Interactions between the yield curve and the macroeconomy in
China arenearly unidirectional rather than bidirectional.
Macroeconomic variablesreshape the yield curve, but direct
adjustments of the yield curve do not result insignificant changes
in macroeconomic variables. Due to the incomplete liberali-zation
of interest rates and market segmentation, the yield curve does
notaccurately reflect the cost of capital , and it is not an
efficient benchmark yieldcurve. As a result, there exists a wide
disjunction between the real economy andfinancial markets in
China.
China’s bond market is segmented into the exchange bond market
and theinterbank bond market. The present study has focused on
interactions betweenthe yield curve and the macroeconomy, and has
revealed that the market seg-mentation affects the transmission of
monetary policy. However, it is still anopen question whether the
segmentation in bond markets influences the inter-actions between
macro variables and yield curves. We leave this topic for
futureresearch.
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APPENDIX
Data sources
Treasury bonds trading data on the first trading day of each
month fromJanuary 2002 to December 2012: The Shanghai Stock
Exchange (http://www.sse.com.cn).
Macroeconomic variables including the consumer price index, the
growth rateof industrial production, the growth rate of total
retail sales of social consumergoods, the growth rate of fixed
asset investment, the growth rate of total volumeof imports and
exports, the growth rate of government expenditure, theweighted
average interest rate of 7-day bond-pledged repos and the M2
growthrate are collected mainly from the China Stock Market &
Accounting Researchdatabase (http://www.gtarsc.com/) and partly
from the National Bureau ofStatistics of China
(http://www.stats.gov.cn/tjsj/ndsj/).
THE SOVEREIGN YIELD CURVE AND THE MACROECONOMY IN CHINA 441
© 2015 Wiley Publishing Asia Pty Ltd
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