Description
The purpose of the paper is to gauge the usefulness of the Monetary Condition Index
(MCI) and Financial Condition Index (FCI) for the conduct of monetary policy in Malaysia.
Journal of Financial Economic Policy
Measuring monetary conditions in a small open economy: the case of Malaysia
Muhamed Zulkhibri Abdul Majid
Article information:
To cite this document:
Muhamed Zulkhibri Abdul Majid, (2012),"Measuring monetary conditions in a small open economy: the
case of Malaysia", J ournal of Financial Economic Policy, Vol. 4 Iss 3 pp. 218 - 231
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Measuring monetary conditions
in a small open economy: the case
of Malaysia
Muhamed Zulkhibri Abdul Majid
Economic Research and Policy Department, Islamic Development Bank,
Jeddah, Saudi Arabia
Abstract
Purpose – The purpose of the paper is to gauge the usefulness of the Monetary Condition Index
(MCI) and Financial Condition Index (FCI) for the conduct of monetary policy in Malaysia.
Design/methodology/approach – The MCI is constructed as the weighted sum of changes in
the exchange rate and interest rate from their levels in a chosen base year. The weights are obtained
by summing up the coef?cients on the lags variables from estimating the determinants of
backward-looking aggregate demand.
Findings – The paper ?nds that the movement in?ation induces the movement in either interest rate
or exchange rate. The result also indicates that the interest rate channel is found to be more powerful
than the exchange rate channel. The method in determining the weights for each policy component of
the index however indicates some degree of instability due to some external shock affected the
exchange rate or the domestic short-term interest rate.
Originality/value – In a small open economy with deregulated markets, it is crucial to assess
the combined effect of interest rate and exchange rate on monetary conditions and the conduct of
monetary policy. Despite the index ability to explain monetary conditions in Malaysia, the estimate
of MCI and FCI should be used cautiously. The index does not offer a precise signal on the state
of monetary condition in Malaysia.
Keywords Malaysia, National economy, Monetary policy, Monetary condition index
Paper type Research paper
1. Introduction
Implicit in any monetary policy action or inaction, is an expectation of how the future
will unfold, that is, a forecast [. . .] The belief that some formal set of rules for policy
implementation can effectively eliminate that problem is, in my judgment an illusion. There is
no way to avoid making a forecast, explicitly or implicitly (Greenspan, 1994).
One way of measuring changes in monetary conditions is by estimating the Monetary
Conditions Index (MCI). The MCI, the weighted average of the short-term interest rate
and the exchange rate, is commonly used, at least in open economies as a composite
measure of the stance of monetary policy (Goodhart and Hoffman, 2001)[1]. The index
has several attractive features due to its simple motivation:
.
it is easyto calculate andintuitivelyappealingoperational target for monetarypolicy;
.
it generalizes interest-rate targeting to include effects of exchange rates on an
open economy;
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
All ?ndings, interpretations, and conclusions are solely of the authors’ opinion and do not
necessarily represent the views of the institutions.
JFEP
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Journal of Financial Economic Policy
Vol. 4 No. 3, 2012
pp. 218-231
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211245953
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it serves as a model-based policy guide between formal model forecasts; and
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it captures both domestic and foreign in?uences on the general monetary
conditions of a country.
In some central banks, it is use as a predictor for future inaction in order to take policy
decisions. The index was ?rst implemented in March 1990 by the New Zealand Central
Bank and the Canadian Central Bank, which is known as the biggest contributor
to the methodology to calculate MCI index. It then followed by Bank of England
(October, 1992), Sweden ( January, 1993), Finland (February, 1993), Australia
(April, 1993) and Spain (summer, 1994). In developing countries, countries like
Colombia, Thailand, Chile, Mexico and Turkey also calculate their own MCI while
other institutions use this index to compare monetary policy across countries.
The purpose of this paper is twofold:
(1) To provide empirical estimate of the MCI for Malaysia. As the role of interest
rates and the exchange rate in the economy risen over time, it is crucial to assess
the combined effect of policy variables when movements in relative interest
rates cannot fully explain movements in the exchange rate.
(2) To evaluate the concept of Financial Condition Index (FCI) to capture to
movement of asset prices and the way it is constructed for assessing monetary
conditions.
Therefore, there is a rising need to assess the variations regarding misalignment in the
asset markets and the sensitivity of the monetary authorities to respond.
This paper is set out as follows: Section 2 gives an overview of the monetary policy
framework in Malaysia. Sections 3 and 4 brie?y review related literature on MCI and
FCI, respectively. Section 5 provides some criticisms on the use and the construction of
MCI and FCI in practice. Section 6 outlines the methodology and estimation of MCI and
FCI. Finally, Section 7 provides concluding remarks.
2. Monetary policy framework in Malaysia
Malaysia’s monetary policy framework is of?cially set out in the Central Bank
of Malaysia Act (CBA) 1958. The principal objectives of the central bank, Bank Negara
Malaysia (BNM), which are to issue currency, to keep reserves for safeguarding the
value of the currency, to act as a banker and ?nancial adviser to the government, and to
promote monetary stability and a sound ?nancial structure. The CBA however was
amended in 2003 to include additional objectives: to promote the reliable, ef?cient and
smooth operation of national payment and settlement systems. The ultimate policy
goal for BNM is economic growth and price stability (Bank Negara Malaysia, 2004).
However, the CBA does not identify any priority between its objectives and provides
little guidance on how to reconcile con?icting objectives.
In Malaysia, the BNM’s Annual Report provides a general description of monetary
policy, while the CBA outlines monetary policy objectives from which targets can be
derived. In the past, it has been argued that the lack of clear and published annual
targets substantially diminished monetary policy transparency and effectiveness.
In order to allay this concern, the BNM has stepped up its efforts, although with the
initial minimum disclosure, to enhance transparency by improving its communication
strategy and enhancing the dissemination of information to its stakeholders.
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The main operating target for the BNM is the interest rate. In April 2004, the BNM
replaced the three-month intervention rate with the overnight policy rate (OPR) as the
main indicator of its monetary policy stance. The OPR has a dual role:
(1) as a signaling device to indicate the monetary policy stance; and
(2) as a target rate for the day-today liquidity operations of the central bank.
Liquidity management is aimed at ensuring the appropriate level of liquidity that
would in?uence the overnight inter-bank rate to move closer to the OPR. Any changes
in the OPR are announced in the monetary policy statement (MPS), which is issued on
the same day as the corresponding Monetary Policy Committee (MPC) meeting.
In term of the exchange rate regime, from September 1998 to July 2005, the ringgit
was pegged to the US dollar. The policy of non-internationalisation of the ringgit
allowed Malaysia to set domestic interest rates (limiting access to ringgit offshore
operations), while keeping the exchange rate stable. The BNM does not use the
exchange rate as an instrument of policy and does not have a target for the exchange
rate. The overriding objective of the exchange rate policy is the “promotion of
exchange rate stability against the currencies of Malaysia’s major trading partners”
(Bank Negara Malaysia, 2005).
3. The literature on the MCI
The MCIs have become popular in several countries over the past few years as a way
of interpreting the stance of monetary policy and its effect on the economy. There
exist excellent sources which explain the speci?cation and construction of MCI
(Ericsson et al., 1998; Freedman, 1995). Much of the attention is focused to the role of
MCI in monetary policy conduct rather than the construction and usefulness of MCI.
The Bank of Canada pioneered the use of this concept in the early 1990s and used the
MCI as an operational target, setting a short-termtarget path that is compatible with the
ultimate in?ation target. As Freedman (1994) explains, “it is not an intermediate target
but it is simply a provisional reference path that shows the direction to take in the
short-run”. The central bank of Norway has constructed monetary conditions indices
but these are merely synthetic indicators and do not any operational role, while the MCI
indices of the Sveriges Riksbank and the Suomen Pankki are used as leading indicators
of in?ation for monitoring the in?ation targets.
The construction of MCI has been operationalised around the world in several ways.
Freedman (1994, 1995) de?nes it as an indicator to assist the central bank in tracing the
transmission process from policy instruments to ultimate target. Bo?nger (2001) builds
a model where MCI helps to minimize the loss function of the central bank subject to
internal and external equilibrium conditions, which yields a unique solution for real
interest and exchange rate. Stevens (1998) considers MCI as a hybrid of instrument and
target of a central bank, as in a free ?oating regime no direct control exists over the
exchange rate. Mayes and Viren (2000) stress the importance of profound knowledge of
empirical interactions between the interest and exchange rate.
According to Frochen (1996), MCI cannot be treated as a synthetic indicator of
monetary policy actions because it takes market-based variables into account and
market is also pricing in its own expectation and perception. This point of viewseems to
have been widely accepted in subsequent literature. Siklos (2000) emphasizes the role
of MCI in communication, as a feedback from the ?nancial markets towards
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the central bank. Blot and Levieuge (2008) examine the usefulness of MCI in G-7
countries to explain future economic activity. These ?ndings suggest that informational
content of MCI is very sporadic, in which the past evolution of exchange rate, interest
rate and asset prices affect the economic activities with different impact and timing. In a
similar line of research, Esteves (2003) calculates a MCI for the Portuguese economy. The
results suggest that despite all the simplifying assumptions underlying its construction,
the dynamic versions of MCI may be helpful in explaining the contribution of monetary
conditions of the Portuguese economy.
Despite the extensive literature examining the role of MCI in developed countries, the
evidence from developing countries is scarce. Hyder and Khan (2006) discuss how
changes in interest rate and exchange rate are used for assessing the overall monetary
policy stance in Pakistan. The constructed MCI indicates that Pakistan has eight tight
and six soft periods of monetary stance during March 1991 to April. Knedlik (2005)
estimates the weights of the MCI for South Africa to track the development of monetary
conditions in post-apartheid era. The results suggest that changes of real interest
rates have a 1.9 times higher in?uence on monetary conditions than changes in
real exchange rates.
4. Extended MCI: the Financial Condition Index
Taking into account the increasing debate over the role played by asset prices in
monetary transmission mechanism, through wealth effects and balance sheet effects,
many central banks and institutions have developed FCI in recent years. Policy-makers
and international institutions often use FCI in their assessment of the monetary policy
stance with different de?nition of FCIs and methodologies. Some researchers compute
FCIs that measure the tightness/accommodativeness of ?nancial factors relative to their
historical average in terms of an effective policy rate (Guichard and Turner, 2008), while
others measure the estimated contribution to growth from ?nancial shocks in a given
quarter (Swiston, 2008).
Financial conditions can be de?ned as the current state of ?nancial variables that
in?uence economic behavior and future state of the economy. In theory, such ?nancial
variables may include anything that characterises the supply or demand of ?nancial
instruments relevant for economic activity. This list might comprise a wide arrayof asset
prices and quantities (both stocks and ?ows), as well as indicators of potential asset
supply and demand. Ideally, the FCI measures the ?nancial shocks (exogenous shifts in
?nancial conditions that in?uence or otherwise predict future economic activity).
The construction of FCIs relies on few different approaches. Goodhart and Hofmann
(2001) propose two different methodologies:
(1) simulate a large-scale macro-econometric model and implement a system with
reduced-form aggregate demand equations; and
(2) a principal components methodology, which extracts a common factor from a
group of several ?nancial variables.
This common factor captures the greatest common variation in the variables and
is either used as the FCI or is added to the central bank policy rate to make up the FCI.
In most cases, ?nancial condition indices are based on the current value of
?nancial variables, but some take into account the lagged ?nancial variables in the
estimation.
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Gauthier et al. (2004) estimate the FCIs for Canada based on three approaches:
(1) IS-curve-based model;
(2) generalised impulse response functions; and
(3) factor analysis, to address one or more criticisms applied to MCIs and FCIs.
In addition to short- and long-term interest rates and the exchange rate, the results
suggest that housing prices, equity prices and bond yield risk-premia are signi?cant
in explaining output. In a similar line of research, Lack (2003) examines the role of
housing and stock prices in the monetary transmission mechanism in Switzerland by
expanding the MCI into FCI. The results suggest that housing prices increase the
predictive power for in?ation of the new FCIs compared to traditional MCIs.
Montagnoli and Napolitano (2006) investigate the role of asset prices in the conduct
of monetary policy in the USA, Canada, the euro area and the UK and construct FCI for
the four countries using the Kalman ?lter algorithm. The results using the Taylor rules
equation suggest that the FCI enter positively and statistically signi?cant into the
Federal Reserve Bank, Bank of England and Bank of Canada interest rate setting. This
gives a positive view for the use of the FCI as an important short-term indicator to
guide the conduct of monetary policy in three out of four countries analyzed.
In practice, central banks, international organisations such as IMF and OECD and
?nancial institutions such as Deutsche Bank, Goldman Sachs and J.P. Morgan resort to
MCI and FCI as a single simple indicator for measuring monetary conditions. Also, there
are several well-established FCIs constructed for the USA such as Bloomberg FCI, the
Citi FCI and Kansas City Federal Reserve Financial Stress Index while there are limited
numbers of FCIs constructed for other developing countries. These indices are based on
a wide range of construction methodologies and ?nancial variables.
5. MCI and FCI: some criticisms
Notwithstanding the intuitive attraction of MCI and FCI, substantive limitations in
the use of the index arise from tactical dif?culties, the choice of weights and variables,
the underlying model’s assumptions, and the associated uncertainty of the estimated
relative weight (Batini and Turnbull, 2000). First, the relationships between the policy
instruments, the exchange rate, the short-term interest rate, output, and in?ation
generally are dynamic, implying different short-, medium- and long-run multipliers.
The policy horizon may affect the relative weight if policy is concerned with several
horizons, thus, the weight for a single horizon may not be adequate.
Second, the temporal properties of the data themselves bear on the construction of
MCI andFCI. Inparticular, non-stationarityof the data(as ina series withdrift) mayaffect
the distribution of the error-terms in the associated model and affect the statistical
inference. Non-stationary data also may be cointegrated. The relevant equations should
include levels of the series and calculations of multipliers should account for those levels.
A central bank which displays insuf?cient concern about MCI would be tempted to let
it drift over time with the possibility of impelling its in?ation objective. Thus, the mixed
use of differences and levels affects the interpretation of the weights.
Third, the postulated exogeneity of the policy instruments and other variables
is potentially misleading. In the MCI and FCI itself, the weights are interpreted
as elasticities of aggregate demand with respect to the interest rate and the exchange
rate. This interpretation assumes no feedback from aggregate demand or in?ation
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onto exchange rates and interest rates over the relevant policy horizon. However, with
feedback, the potential impact of aggregating interest and exchange rate changes do
not re?ect the total effects these movements on aggregate demand.
Fourth, parameter constancy is critical to the interpretation of MCI and FCI.
Statistically, non-constant weights may arise empirically from mis-speci?ed dynamics
and improper treatment of non-stationarity, or incorrect exogeneity assumptions. Since
the MCI and FCI is designed for policy, it is important to establish the invariance of the
weights to changes in policy, although this conjectured invariance generally has not
been investigated empirically. The non-constant parameters estimation over different
sample periods would result in different estimates of the weights, and so different
choices of weights (Eika et al., 1996).
Fifth, as argued by Ericsson et al. (1998), the choice of model variables determines the
variables omitted from the model. Signi?cant omitted variables in the model’s
relationships may affect dynamics, cointegration, exogeneity, and parameter constancy
in the model. More generally, the use and interpretation of MCI and FCI in policy
assumes the existence of direct and unequivocal relationships between the variables
involved. Possible additional in?uences in those relationships can confound the strict
interpretation of MCI and FCI as an index of monetary conditions.
Sixth, the variables fromwhich the MCI or FCI is constructed may re?ect phenomena
other than just direct monetary policy, so movements in the MCI or FCI are not tied
unequivocally to changes in monetary stance. By following or targeting the MCI or FCI,
a central bank could be misled into adopting an overly tight or loose monetary policy,
simply because some external shock affected the exchange rate or the domestic
short-terminterest rate. The relative weight in the MCI and FCI is based on an estimated
empirical model, and so is subject to coef?cient uncertainty from that estimation
(Ericsson et al., 1998).
Finally, the technical needs to calculate MCI or FCI in real or nominal term.
Theoretically, it would seem preferable to express the MCI and FCI on the basis of real
variables as the real MCI and FCI take account of in?ation movements. It is also
generally believed that rational agents consider the real rather than nominal rates in
their consumption and investment decisions. On the other hand, economic behavior
often reacts on the basis of nominal interest rates in the short-run (Gerlach and Smets,
2000) but can suffer frommoney illusion if they consider the nominal rather than the real
variables in their decision making (Akerloff and Shiller, 2009; Fehr and Tyran, 2001;
Peeters, 1999).
There are several advantages and disadvantages between nominal and real
calculation of MCI and FCI. The advantage of nominal calculation is that it can be
calculated without delay on a daily basis and can provide timely indication of monetary
policy stance[2]. On the other hand, the real values use real variables, so that it provides
the most accurate picture of the current monetary policy stance. However, the
disadvantage of real MCI or FCI is that the calculation with a lag in order to obtain real
values of interest rates and real effective values of exchange rate.
6. Estimates of MCI and FCI for Malaysia
A starting point for constructing the MCI begins with the selection of interest rate and
exchange rate. The weighting of these two variables in MCI can be determined by
employing various econometric techniques such as:
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single equation of either price or output;
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trade elasticities approach; and
.
vector autoregressive (VAR) and Johansen’s cointegrating models.
However, in this study, we de?ne MCI as the weighted sum of changes in the exchange
rate (ER in logs) and in the interest rate (INTR in levels) from their levels in a chosen
base year. Following Freedman (1994, 1995), the formula for MCI is as follows:
MCI
t
¼ v
INT
½INT
t
2INT
b
? þv
ER
½logðER
t
Þ 2logðER
b
Þ? ð1Þ
where INT
t
and ER
t
are interest rate (overnight rate) and exchange rate at time t, at a
given base year, respectively. The most important factor is weights, v as the value of
these weights provides useful information regarding the relative importance of interest
rates and exchange rates. If MCI or FCI increases by one unit, it is equivalent to a one
percentage point increases of interest rate. In this context, it needs to be emphasised that
the level of the MCI depends on the base value, the chosen weights and the measures
of the interest rate and the exchange rate.
In light of the criticism on MCI, Gauthier et al. (2004) suggest some methods to
improve the construction of MCI:
.
weights are derived from reduced form IS-PC framework;
.
the weights are obtained by summing up the coef?cients on the lags variables as
well as by including individual lags in MCI to take into account the dynamics of
those variables over time; and
.
the weights is derived from the determinants of aggregate demand.
Following the methodology by Gauthier et al. (2004), we present a simple model which
is the equivalent of a conventional backward-looking aggregate demand:
p
t
¼
X
n
i¼1
b
1i
p
t2i
þ
X
b
i
y
t2j
þ1
t
ð2Þ
y
t
¼
X
n
t¼1
g
t
y
t21
þ
X
n
j¼1
l
j
rir
t2j
þ
X
n
k¼1
h
k
rer
t
þm
t
ð3Þ
where p
t
is equal to 100
*
[ln (CPI
t
/CPI
t212
)], where CPI is the consumer price index, and
the output gap, y
t
is the difference between actual and potential output, is calculated as
the percentage deviation of the natural logarithm of the monthly industrial production
from a Hodrick-Prescott trend with a smoothing parameter of 1,600. The ex-post real
short-term interest rate, rir
t
is measured as the real short-term money market rate. The
?nancial market (rsp) is proxied by, stock price index of Bursa Malaysia. We calculate
the long-term of the assets prices using the above Hodrick-Prescott ?lter methodology.
The sample covers the period from 1980 to 2004, the period before the introduction
of the new monetary policy framework in Malaysia. Figure 1 plots the component of
MCI, the interest rate, exchange rate and in?ation rate.
The parameter l gives the effect on aggregate demand of a one percentage point
increases of the interest rate, controlling for the effects of the interest rate impulse on
the exchange rate. The parameter h represents the corresponding effect of a 1 percent
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appreciation of the domestic currency. The relative MCI weight v is
^
l= ^ h, where
^
l and
^ h are the estimated coef?cients from equation (3).
Following the pioneering contribution of Alchian and Klein (1973) and more recently
Eika et al. (1996), Mayes and Viren (2000) and Goodhart and Hofmann (2001), we
formulate a formal model of the economy in order to show the importance of ?nancial
variables in the conduct of monetary policy. The simple model in equations (2)
and (3) which is the equivalent of a conventional backward-looking aggregate
demand-aggregate supply, but augmented with the asset markets (an extended version
of Rudebusch and Svensson (1999) and as suggested by Goodhart and Hofmann (2001)):
y
t
¼
X
n
t¼1
g
t
y
t21
þ
X
n
j¼1
l
j
rir
t2j
þ
X
n
k¼1
h
k
rer
t
þ
X
n
k¼1
f
k
rsp þm
t
ð4Þ
We use Granger-causality test to determine the predictive contents of MCI and FCI
against in?ation and output as follow:
Y
t
¼ C þa
1
Y
t21
þa
2
Y
t22
þ · · · þa
p
Y
t2p
þb
1
X
t21
þb
2
X
t22
þ · · · þb
p
X
t2p
þm
t
ð5Þ
the following restriction is imposed on the parameters:
b
1
¼ b
2
¼ · · · ¼ b
p
¼ 0 ð6Þ
If the MCI and FCI is a good predictor of the in?ation or output, the null hypothesis will be
rejected.
Table I presents the estimation results of the aggregate demand for deriving the
weights of MCI and FCI. The equations are estimated by ordinary least square (OLS).
Figure 1.
Macroeconomic variables
–2
0
2
4
6
8
10
12
14
1
9
8
2
:
1
1
M
1
9
8
3
:
1
1
M
1
9
8
4
:
1
1
M
1
9
8
5
:
1
1
M
1
9
8
6
:
1
1
M
1
9
8
7
:
1
1
M
1
9
8
8
:
1
1
M
1
9
8
9
:
1
1
M
1
9
9
0
:
1
1
M
1
9
9
1
:
1
1
M
1
9
9
2
:
1
1
M
1
9
9
3
:
1
1
M
1
9
9
4
:
1
1
M
1
9
9
5
:
1
1
M
1
9
9
6
:
1
1
M
1
9
9
7
:
1
1
M
1
9
9
8
:
1
1
M
1
9
9
9
:
1
1
M
2
0
0
0
:
1
1
M
2
0
0
1
:
1
1
M
2
0
0
2
:
1
1
M
2
0
0
3
:
1
1
M
2
0
0
4
:
1
1
M
0
20
40
60
80
100
120
140
160
180
Inflation Interest Rate Real Effective Exchange Rate
Measuring
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In order to obtain well behaved residuals, a number of impulse dummies, which are
mainly related to the oil price shocks and Asian ?nancial crisis, have also been
included. The lag orders is chosen by a general-to-speci?c modelling strategy and we
report t-statistics in parentheses. For each equation, we report the adjusted R
2
, White’s
(1982) test for heteroskedasticity (H) and a Lagrange-Multiplier (LM) test for serial
correlation. The diagnostic tests suggest that there is no evidence of misspeci?cation
in all estimated equations.
The estimation for the Phillips curve shows that the output gap is signi?cant
at 5 percent level. The coef?cient estimates suggest that an increase in the output gap
by one percentage point leads to an increase of the in?ation rate between 0.37 and
0.41 percentage points. Furthermore, the results for the IS-curve in equation (4) suggest
that the real interest rate, the real exchange rate, and real equity prices have a signi?cant
effect on the output gap. The real interest rate coef?cient estimate of 0.29 in the FCI
equation is signi?cantly smaller than the coef?cient estimates of 0.46 for MCI, while the
real share price has a relatively weak effect on output gaps with the coef?cient of 0.06.
Using the estimated coef?cients of interest rate and the exchange rate, the ratio
(weight) of MCI is 3.8:1. The estimated coef?cients suggest that a one percentage point
rises in real interest rate is roughly equivalent to four-percentage-point increase in real
effective exchange rate (REER) appreciation on real GDP growth. The result also
indicates that the interest rate channel is found to be more powerful than the exchange
rate channel for in?ation. This is consistent with the empirical ?ndings of relatively
high pass-through of interest rate in Malaysia (Zulkhibri, 2010) and a low exchange
rate pass-through to domestic prices in Malaysia.
In the case of FCI, using the estimated coef?cients of interest rate and stock prices,
generate the ratios (weights) of 3.4:1 for the exchange rate and 16.6:1 for the stock prices,
respectively. Although the impact of interest rate has slightly deteriorated with respect
For deriving MCI
weight
p
t
¼
ð2:20Þ
0:453p
t21
þ
ð2:30Þ
0:201p
t23
2
ð3:45Þ
0:124p
t24
þ
ð4:34Þ
0:412y
t22
þ
ð2:56Þ
0:455DCRISIS
R
2
¼ 0:73 H¼21.26 (0.34) LM¼7.73 (0.12)
y
t
¼
ð2:20Þ
0:863y
t22
þ
ð3:30Þ
0:113y
t28
2
ð2:25Þ
0:462rir
t21
þ
ð2:04Þ
0:164rer
t24
þ
ð3:56Þ
0:987DCRISIS
R
2
¼ 0:73 H¼23.36 (0.34) LM¼7.24 (0.12)
For deriving FCI
weight
p
t
¼
ð4:20Þ
0:371p
t21
þ
ð3:30Þ
0:265p
t23
2
ð3:45Þ
0:212p
t23
þ
ð3:34Þ
0:371y
t21
þ
ð2:56Þ
0:055DCRISIS
R
2
¼ 0:78 H¼24.66 (0.34) LM¼8.76 (0.12)
y
t
¼
ð8:20Þ
0:563y
t21
þ
ð3:30Þ
0:131y
t25
2
ð4:45Þ
0:291rir
t21
þ
ð2:34Þ
0:161rer
t24
þ
ð4:56Þ
0:063rsp
t21
þ
ð3:43Þ
0:987DCRISIS
R
2
¼ 0:86 H¼16.66 (0.34) LM¼10.76 (0.12)
Notes: The table reports the results of estimating equations (1) and (2); coef?cients estimates are
reported with t-statistic in parentheses;
R
2
is the adjusted coef?cient of determination; H is White’s
(1982) test for heteroskedasticity and LM is a Lagrange-Multiplier test for serial correlation up to order
of six; in parentheses, we report probability values for diagnostic test
Table I.
Regression results –
backward-looking
aggregate demand
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to the changes in the exchange rate, the impact of stock price seems to be very small. The
one percentage point rises in the real interest rate equivalent 16.6 percentage point
increase in real stock price on real GDP. It is interesting to note that the importance of the
REER relative to the real interest rate is relatively similar in magnitude for both the MCI
and FCI.
We compute the MCI index using a three-month interbank interest rate and the US
dollar/Malaysia ringgit exchange rate (Figure 2). The ?rst month of 1990, which is the
periodat whichthe level of economy operates at the long-run equilibriumis chosen as the
base period(the weights are takenfromequation(2)). The results of MCI index suggests a
distinct easingof monetary conditions in 1985-1988, re?ecting a weaker ringgit and relax
lending policy by banks, which reduce the real interest rate, and thereafter contribute to
higher economic growth. However, macroeconomic measures to curb credit supply
together with the interest rate hikes in 1991, resulted in tighter monetary conditions.
Looking at the path of FCI over time (Figure 3), ?nancial conditions tightened
sharply in 1984 and 1985, contributing to a slowdown in economic activity. As the FCI
improved, economic activity picked up again in 1986 and 1987. Economic activity was
robust as well as the ?nancial conditions remained accommodative in 1988-1989. The
FCI indicates a slight expansionary in the late 1990s and a strong expansionary
starting in 1990. The overall performance shows that the FCI can better explain the
behavior of in?ation from 1990 to 1992 than the MCI.
The recession of 1997-1998 coincided with another plunge in the FCI and output. The
fall in equity prices from the peak of bubble in 1997 until the trough in 1998, implies the
tightening of ?nancial conditions and a restrictive monetary policy stance. This is also
explained the persistent and low in?ation behavior since 1999. The brief rebound in the
economic activity in mid-1999, however, was not entirely unanticipated by the FCI.
As evidenced in Figure 1, the looseness of monetary policy in Malaysia has hardly
changed over the few years. All in all, the nominal MCI and FCI, taking into account the
very recent changes in domestic monetary conditions, reveals similar results derived
from the real MCI and FCI. The indices pointed to the fact that monetary easing
Figure 2.
Monetary Condition Index
in Malaysia: 1982-2004
–15
–10
–5
0
5
10
15
20
1
9
8
2
:
0
1
M
1
9
8
3
:
0
2
M
1
9
8
4
:
0
3
M
1
9
8
5
:
0
4
M
1
9
8
6
:
0
5
M
1
9
8
7
:
0
6
M
1
9
8
8
:
0
7
M
1
9
8
9
:
0
8
M
1
9
9
0
:
0
9
M
1
9
9
1
:
1
0
M
1
9
9
2
:
1
1
M
1
9
9
3
:
1
2
M
1
9
9
5
:
0
1
M
1
9
9
6
:
0
2
M
1
9
9
7
:
0
3
M
1
9
9
8
:
0
4
M
1
9
9
9
:
0
5
M
2
0
0
0
:
0
6
M
2
0
0
1
:
0
7
M
2
0
0
2
:
0
8
M
2
0
0
3
:
0
9
M
2
0
0
4
:
1
0
M
Tighter
Easier
MCI (real) MCI (nominal)
Measuring
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in Malaysia has to end in the near future and one should expect a rate hikes, given the
emerging risk to the in?ation in the economy.
To give the answer to the predictive contents of MCI and FCI, Table II reports the
Granger-causality test between in?ation and the MCI (or FCI). The Granger-causality test
rejects the null hypothesis that the MCI (or FCI) explainthe in?ationbehavior, whereas the
null hypothesis that the in?ation explains MCI (or FCI) cannot be rejected. The results
suggest that immediate sign of in?ationary pressure in the economy is followed by
adjustments in either interest rate or exchange rate depending on policy preferences. The
results are also consistent with the role of monetary policy in maintaining price stability.
7. Conclusion
The analysis provides a more uniform analysis of measuring the monetary conditions in
Malaysia. It is generally considered that a tightening in monetary policy slows demand in
the economy as credit becomes more expensive. The aim of such monetary policy
tightening is to reduce in?ation, but the unintended consequence will lead to a slowdown in
economic activities. It is a well-known feature of monetary policy operation that authorities
aim to exercise control over short-term interest rates by adjusting the of?cial rate.
The approach of the estimation of MCI and FCI is based on the conventional
backward-looking aggregate demand and is intended to address one or more criticisms
Figure 3.
Financial Condition Index
in Malaysia: 1980-2004
–15
–10
–5
0
5
10
15
20
1
9
8
1
:
0
1
M
1
9
8
2
:
0
1
M
1
9
8
3
:
0
1
M
1
9
8
4
:
0
1
M
1
9
8
5
:
0
1
M
1
9
8
6
:
0
1
M
1
9
8
7
:
0
1
M
1
9
8
8
:
0
1
M
1
9
8
9
:
0
1
M
1
9
9
0
:
0
1
M
1
9
9
1
:
0
1
M
1
9
9
2
:
0
1
M
1
9
9
3
:
0
1
M
1
9
9
4
:
0
1
M
1
9
9
5
:
0
1
M
1
9
9
6
:
0
1
M
1
9
9
7
:
0
1
M
1
9
9
8
:
0
1
M
1
9
9
9
:
0
1
M
2
0
0
0
:
0
1
M
2
0
0
1
:
0
1
M
2
0
0
2
:
0
1
M
2
0
0
3
:
0
1
M
FCI (real) FCI (nominal)
Tighter
Easier
H
0
Lag(s) F-statistic p-value
In?ation -/ !MCIs 12 1.931
*
0.031
MCIs -/ ! In?ation 12 1.388 0.171
In?ation -/ ! FCIs 12 2.034
*
0.022
FCIs -/ ! In?ation 12 1.109 0.353
Notes: Signi?cant at:
*
5 percent level; -/ ! denotes that X does not Granger cause Y; sample period
from 1982 to 2004
Table II.
Granger-causality test
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in the literature. The MCI and FCI are calculated in both real and nominal terms to
assess how “tight” or “loose” are the monetary conditions. The results suggest that
the despite its ability to explain the monetary conditions in Malaysia, the method in
determining the weights for each policy component of the index indicates some degree
of instability. As such, MCI and FCI do not offer a precise signal on the state of
monetary condition in Malaysia.
On the surface, the MCI or FCI seems to be straightforward, easy to understand and
timely to construct. The results also show that the movement in?ation induces the
movement in either interest rate or exchange rate. However, the diverging movements
in equity and housing prices have also raised concerns about the appropriate stance of
monetary policy when markets are moving in different directions (Goodhart and
Hofmann, 2001; Mayes and Viren, 2000). The uncertainty surrounding its construction
makes it an unreliable stand-alone indicator and further investigation is necessary to
identify the actual role of asset prices in the transmission mechanism of monetary
policy in Malaysia.
Notes
1. Four central banks, those for Canada, New Zealand, Norway and Sweden publish an MCI
and to varying degree, use their respective MCIs in the conduct of monetary policy.
Additionally, the International Monetary Fund (IMF) and the Organisation for Economic
Cooperation and Development (OECD) calculate MCIs for evaluating the monetary policies
of many countries.
2. It may be misleading, especially in periods of high in?ation.
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May, pp. 225-7.
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Central Banks, The Free Press, New York, NY.
About the author
Muhamed Zulkhibri Abdul Majid is an Economist currently working in the Economic Research
and Policy Department, Islamic Development Bank. Previously, he has been a Research Manager
in Central Bank of Malaysia and a Visiting Lecturer at the University Putra Malaysia.
His research concerns monetary, ?nancial economics, banking and applied econometrics. His
research papers have appeared (forthcoming) in various international journals including: Journal
of Asian Economics, Applied Financial Economics, Economics System, Emerging Markets
Review, Journal of King Abdul Aziz University: Islamic Economic, International Review of
Economics, Economic Change and Restructuring and Journal of Asia-Paci?c and Business.
He holds a B.Commerce in Accounting and Finance from University of Birmingham, UK and a
PhD in Monetary Economics from University of Nottingham, UK. He is also a Certi?ed Financial
Planner. Muhamed Zulkhibri Abdul Majid can be contacted at: [email protected]
Measuring
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doc_178564368.pdf
The purpose of the paper is to gauge the usefulness of the Monetary Condition Index
(MCI) and Financial Condition Index (FCI) for the conduct of monetary policy in Malaysia.
Journal of Financial Economic Policy
Measuring monetary conditions in a small open economy: the case of Malaysia
Muhamed Zulkhibri Abdul Majid
Article information:
To cite this document:
Muhamed Zulkhibri Abdul Majid, (2012),"Measuring monetary conditions in a small open economy: the
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Measuring monetary conditions
in a small open economy: the case
of Malaysia
Muhamed Zulkhibri Abdul Majid
Economic Research and Policy Department, Islamic Development Bank,
Jeddah, Saudi Arabia
Abstract
Purpose – The purpose of the paper is to gauge the usefulness of the Monetary Condition Index
(MCI) and Financial Condition Index (FCI) for the conduct of monetary policy in Malaysia.
Design/methodology/approach – The MCI is constructed as the weighted sum of changes in
the exchange rate and interest rate from their levels in a chosen base year. The weights are obtained
by summing up the coef?cients on the lags variables from estimating the determinants of
backward-looking aggregate demand.
Findings – The paper ?nds that the movement in?ation induces the movement in either interest rate
or exchange rate. The result also indicates that the interest rate channel is found to be more powerful
than the exchange rate channel. The method in determining the weights for each policy component of
the index however indicates some degree of instability due to some external shock affected the
exchange rate or the domestic short-term interest rate.
Originality/value – In a small open economy with deregulated markets, it is crucial to assess
the combined effect of interest rate and exchange rate on monetary conditions and the conduct of
monetary policy. Despite the index ability to explain monetary conditions in Malaysia, the estimate
of MCI and FCI should be used cautiously. The index does not offer a precise signal on the state
of monetary condition in Malaysia.
Keywords Malaysia, National economy, Monetary policy, Monetary condition index
Paper type Research paper
1. Introduction
Implicit in any monetary policy action or inaction, is an expectation of how the future
will unfold, that is, a forecast [. . .] The belief that some formal set of rules for policy
implementation can effectively eliminate that problem is, in my judgment an illusion. There is
no way to avoid making a forecast, explicitly or implicitly (Greenspan, 1994).
One way of measuring changes in monetary conditions is by estimating the Monetary
Conditions Index (MCI). The MCI, the weighted average of the short-term interest rate
and the exchange rate, is commonly used, at least in open economies as a composite
measure of the stance of monetary policy (Goodhart and Hoffman, 2001)[1]. The index
has several attractive features due to its simple motivation:
.
it is easyto calculate andintuitivelyappealingoperational target for monetarypolicy;
.
it generalizes interest-rate targeting to include effects of exchange rates on an
open economy;
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
All ?ndings, interpretations, and conclusions are solely of the authors’ opinion and do not
necessarily represent the views of the institutions.
JFEP
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218
Journal of Financial Economic Policy
Vol. 4 No. 3, 2012
pp. 218-231
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381211245953
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it serves as a model-based policy guide between formal model forecasts; and
.
it captures both domestic and foreign in?uences on the general monetary
conditions of a country.
In some central banks, it is use as a predictor for future inaction in order to take policy
decisions. The index was ?rst implemented in March 1990 by the New Zealand Central
Bank and the Canadian Central Bank, which is known as the biggest contributor
to the methodology to calculate MCI index. It then followed by Bank of England
(October, 1992), Sweden ( January, 1993), Finland (February, 1993), Australia
(April, 1993) and Spain (summer, 1994). In developing countries, countries like
Colombia, Thailand, Chile, Mexico and Turkey also calculate their own MCI while
other institutions use this index to compare monetary policy across countries.
The purpose of this paper is twofold:
(1) To provide empirical estimate of the MCI for Malaysia. As the role of interest
rates and the exchange rate in the economy risen over time, it is crucial to assess
the combined effect of policy variables when movements in relative interest
rates cannot fully explain movements in the exchange rate.
(2) To evaluate the concept of Financial Condition Index (FCI) to capture to
movement of asset prices and the way it is constructed for assessing monetary
conditions.
Therefore, there is a rising need to assess the variations regarding misalignment in the
asset markets and the sensitivity of the monetary authorities to respond.
This paper is set out as follows: Section 2 gives an overview of the monetary policy
framework in Malaysia. Sections 3 and 4 brie?y review related literature on MCI and
FCI, respectively. Section 5 provides some criticisms on the use and the construction of
MCI and FCI in practice. Section 6 outlines the methodology and estimation of MCI and
FCI. Finally, Section 7 provides concluding remarks.
2. Monetary policy framework in Malaysia
Malaysia’s monetary policy framework is of?cially set out in the Central Bank
of Malaysia Act (CBA) 1958. The principal objectives of the central bank, Bank Negara
Malaysia (BNM), which are to issue currency, to keep reserves for safeguarding the
value of the currency, to act as a banker and ?nancial adviser to the government, and to
promote monetary stability and a sound ?nancial structure. The CBA however was
amended in 2003 to include additional objectives: to promote the reliable, ef?cient and
smooth operation of national payment and settlement systems. The ultimate policy
goal for BNM is economic growth and price stability (Bank Negara Malaysia, 2004).
However, the CBA does not identify any priority between its objectives and provides
little guidance on how to reconcile con?icting objectives.
In Malaysia, the BNM’s Annual Report provides a general description of monetary
policy, while the CBA outlines monetary policy objectives from which targets can be
derived. In the past, it has been argued that the lack of clear and published annual
targets substantially diminished monetary policy transparency and effectiveness.
In order to allay this concern, the BNM has stepped up its efforts, although with the
initial minimum disclosure, to enhance transparency by improving its communication
strategy and enhancing the dissemination of information to its stakeholders.
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The main operating target for the BNM is the interest rate. In April 2004, the BNM
replaced the three-month intervention rate with the overnight policy rate (OPR) as the
main indicator of its monetary policy stance. The OPR has a dual role:
(1) as a signaling device to indicate the monetary policy stance; and
(2) as a target rate for the day-today liquidity operations of the central bank.
Liquidity management is aimed at ensuring the appropriate level of liquidity that
would in?uence the overnight inter-bank rate to move closer to the OPR. Any changes
in the OPR are announced in the monetary policy statement (MPS), which is issued on
the same day as the corresponding Monetary Policy Committee (MPC) meeting.
In term of the exchange rate regime, from September 1998 to July 2005, the ringgit
was pegged to the US dollar. The policy of non-internationalisation of the ringgit
allowed Malaysia to set domestic interest rates (limiting access to ringgit offshore
operations), while keeping the exchange rate stable. The BNM does not use the
exchange rate as an instrument of policy and does not have a target for the exchange
rate. The overriding objective of the exchange rate policy is the “promotion of
exchange rate stability against the currencies of Malaysia’s major trading partners”
(Bank Negara Malaysia, 2005).
3. The literature on the MCI
The MCIs have become popular in several countries over the past few years as a way
of interpreting the stance of monetary policy and its effect on the economy. There
exist excellent sources which explain the speci?cation and construction of MCI
(Ericsson et al., 1998; Freedman, 1995). Much of the attention is focused to the role of
MCI in monetary policy conduct rather than the construction and usefulness of MCI.
The Bank of Canada pioneered the use of this concept in the early 1990s and used the
MCI as an operational target, setting a short-termtarget path that is compatible with the
ultimate in?ation target. As Freedman (1994) explains, “it is not an intermediate target
but it is simply a provisional reference path that shows the direction to take in the
short-run”. The central bank of Norway has constructed monetary conditions indices
but these are merely synthetic indicators and do not any operational role, while the MCI
indices of the Sveriges Riksbank and the Suomen Pankki are used as leading indicators
of in?ation for monitoring the in?ation targets.
The construction of MCI has been operationalised around the world in several ways.
Freedman (1994, 1995) de?nes it as an indicator to assist the central bank in tracing the
transmission process from policy instruments to ultimate target. Bo?nger (2001) builds
a model where MCI helps to minimize the loss function of the central bank subject to
internal and external equilibrium conditions, which yields a unique solution for real
interest and exchange rate. Stevens (1998) considers MCI as a hybrid of instrument and
target of a central bank, as in a free ?oating regime no direct control exists over the
exchange rate. Mayes and Viren (2000) stress the importance of profound knowledge of
empirical interactions between the interest and exchange rate.
According to Frochen (1996), MCI cannot be treated as a synthetic indicator of
monetary policy actions because it takes market-based variables into account and
market is also pricing in its own expectation and perception. This point of viewseems to
have been widely accepted in subsequent literature. Siklos (2000) emphasizes the role
of MCI in communication, as a feedback from the ?nancial markets towards
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the central bank. Blot and Levieuge (2008) examine the usefulness of MCI in G-7
countries to explain future economic activity. These ?ndings suggest that informational
content of MCI is very sporadic, in which the past evolution of exchange rate, interest
rate and asset prices affect the economic activities with different impact and timing. In a
similar line of research, Esteves (2003) calculates a MCI for the Portuguese economy. The
results suggest that despite all the simplifying assumptions underlying its construction,
the dynamic versions of MCI may be helpful in explaining the contribution of monetary
conditions of the Portuguese economy.
Despite the extensive literature examining the role of MCI in developed countries, the
evidence from developing countries is scarce. Hyder and Khan (2006) discuss how
changes in interest rate and exchange rate are used for assessing the overall monetary
policy stance in Pakistan. The constructed MCI indicates that Pakistan has eight tight
and six soft periods of monetary stance during March 1991 to April. Knedlik (2005)
estimates the weights of the MCI for South Africa to track the development of monetary
conditions in post-apartheid era. The results suggest that changes of real interest
rates have a 1.9 times higher in?uence on monetary conditions than changes in
real exchange rates.
4. Extended MCI: the Financial Condition Index
Taking into account the increasing debate over the role played by asset prices in
monetary transmission mechanism, through wealth effects and balance sheet effects,
many central banks and institutions have developed FCI in recent years. Policy-makers
and international institutions often use FCI in their assessment of the monetary policy
stance with different de?nition of FCIs and methodologies. Some researchers compute
FCIs that measure the tightness/accommodativeness of ?nancial factors relative to their
historical average in terms of an effective policy rate (Guichard and Turner, 2008), while
others measure the estimated contribution to growth from ?nancial shocks in a given
quarter (Swiston, 2008).
Financial conditions can be de?ned as the current state of ?nancial variables that
in?uence economic behavior and future state of the economy. In theory, such ?nancial
variables may include anything that characterises the supply or demand of ?nancial
instruments relevant for economic activity. This list might comprise a wide arrayof asset
prices and quantities (both stocks and ?ows), as well as indicators of potential asset
supply and demand. Ideally, the FCI measures the ?nancial shocks (exogenous shifts in
?nancial conditions that in?uence or otherwise predict future economic activity).
The construction of FCIs relies on few different approaches. Goodhart and Hofmann
(2001) propose two different methodologies:
(1) simulate a large-scale macro-econometric model and implement a system with
reduced-form aggregate demand equations; and
(2) a principal components methodology, which extracts a common factor from a
group of several ?nancial variables.
This common factor captures the greatest common variation in the variables and
is either used as the FCI or is added to the central bank policy rate to make up the FCI.
In most cases, ?nancial condition indices are based on the current value of
?nancial variables, but some take into account the lagged ?nancial variables in the
estimation.
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Gauthier et al. (2004) estimate the FCIs for Canada based on three approaches:
(1) IS-curve-based model;
(2) generalised impulse response functions; and
(3) factor analysis, to address one or more criticisms applied to MCIs and FCIs.
In addition to short- and long-term interest rates and the exchange rate, the results
suggest that housing prices, equity prices and bond yield risk-premia are signi?cant
in explaining output. In a similar line of research, Lack (2003) examines the role of
housing and stock prices in the monetary transmission mechanism in Switzerland by
expanding the MCI into FCI. The results suggest that housing prices increase the
predictive power for in?ation of the new FCIs compared to traditional MCIs.
Montagnoli and Napolitano (2006) investigate the role of asset prices in the conduct
of monetary policy in the USA, Canada, the euro area and the UK and construct FCI for
the four countries using the Kalman ?lter algorithm. The results using the Taylor rules
equation suggest that the FCI enter positively and statistically signi?cant into the
Federal Reserve Bank, Bank of England and Bank of Canada interest rate setting. This
gives a positive view for the use of the FCI as an important short-term indicator to
guide the conduct of monetary policy in three out of four countries analyzed.
In practice, central banks, international organisations such as IMF and OECD and
?nancial institutions such as Deutsche Bank, Goldman Sachs and J.P. Morgan resort to
MCI and FCI as a single simple indicator for measuring monetary conditions. Also, there
are several well-established FCIs constructed for the USA such as Bloomberg FCI, the
Citi FCI and Kansas City Federal Reserve Financial Stress Index while there are limited
numbers of FCIs constructed for other developing countries. These indices are based on
a wide range of construction methodologies and ?nancial variables.
5. MCI and FCI: some criticisms
Notwithstanding the intuitive attraction of MCI and FCI, substantive limitations in
the use of the index arise from tactical dif?culties, the choice of weights and variables,
the underlying model’s assumptions, and the associated uncertainty of the estimated
relative weight (Batini and Turnbull, 2000). First, the relationships between the policy
instruments, the exchange rate, the short-term interest rate, output, and in?ation
generally are dynamic, implying different short-, medium- and long-run multipliers.
The policy horizon may affect the relative weight if policy is concerned with several
horizons, thus, the weight for a single horizon may not be adequate.
Second, the temporal properties of the data themselves bear on the construction of
MCI andFCI. Inparticular, non-stationarityof the data(as ina series withdrift) mayaffect
the distribution of the error-terms in the associated model and affect the statistical
inference. Non-stationary data also may be cointegrated. The relevant equations should
include levels of the series and calculations of multipliers should account for those levels.
A central bank which displays insuf?cient concern about MCI would be tempted to let
it drift over time with the possibility of impelling its in?ation objective. Thus, the mixed
use of differences and levels affects the interpretation of the weights.
Third, the postulated exogeneity of the policy instruments and other variables
is potentially misleading. In the MCI and FCI itself, the weights are interpreted
as elasticities of aggregate demand with respect to the interest rate and the exchange
rate. This interpretation assumes no feedback from aggregate demand or in?ation
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onto exchange rates and interest rates over the relevant policy horizon. However, with
feedback, the potential impact of aggregating interest and exchange rate changes do
not re?ect the total effects these movements on aggregate demand.
Fourth, parameter constancy is critical to the interpretation of MCI and FCI.
Statistically, non-constant weights may arise empirically from mis-speci?ed dynamics
and improper treatment of non-stationarity, or incorrect exogeneity assumptions. Since
the MCI and FCI is designed for policy, it is important to establish the invariance of the
weights to changes in policy, although this conjectured invariance generally has not
been investigated empirically. The non-constant parameters estimation over different
sample periods would result in different estimates of the weights, and so different
choices of weights (Eika et al., 1996).
Fifth, as argued by Ericsson et al. (1998), the choice of model variables determines the
variables omitted from the model. Signi?cant omitted variables in the model’s
relationships may affect dynamics, cointegration, exogeneity, and parameter constancy
in the model. More generally, the use and interpretation of MCI and FCI in policy
assumes the existence of direct and unequivocal relationships between the variables
involved. Possible additional in?uences in those relationships can confound the strict
interpretation of MCI and FCI as an index of monetary conditions.
Sixth, the variables fromwhich the MCI or FCI is constructed may re?ect phenomena
other than just direct monetary policy, so movements in the MCI or FCI are not tied
unequivocally to changes in monetary stance. By following or targeting the MCI or FCI,
a central bank could be misled into adopting an overly tight or loose monetary policy,
simply because some external shock affected the exchange rate or the domestic
short-terminterest rate. The relative weight in the MCI and FCI is based on an estimated
empirical model, and so is subject to coef?cient uncertainty from that estimation
(Ericsson et al., 1998).
Finally, the technical needs to calculate MCI or FCI in real or nominal term.
Theoretically, it would seem preferable to express the MCI and FCI on the basis of real
variables as the real MCI and FCI take account of in?ation movements. It is also
generally believed that rational agents consider the real rather than nominal rates in
their consumption and investment decisions. On the other hand, economic behavior
often reacts on the basis of nominal interest rates in the short-run (Gerlach and Smets,
2000) but can suffer frommoney illusion if they consider the nominal rather than the real
variables in their decision making (Akerloff and Shiller, 2009; Fehr and Tyran, 2001;
Peeters, 1999).
There are several advantages and disadvantages between nominal and real
calculation of MCI and FCI. The advantage of nominal calculation is that it can be
calculated without delay on a daily basis and can provide timely indication of monetary
policy stance[2]. On the other hand, the real values use real variables, so that it provides
the most accurate picture of the current monetary policy stance. However, the
disadvantage of real MCI or FCI is that the calculation with a lag in order to obtain real
values of interest rates and real effective values of exchange rate.
6. Estimates of MCI and FCI for Malaysia
A starting point for constructing the MCI begins with the selection of interest rate and
exchange rate. The weighting of these two variables in MCI can be determined by
employing various econometric techniques such as:
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single equation of either price or output;
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trade elasticities approach; and
.
vector autoregressive (VAR) and Johansen’s cointegrating models.
However, in this study, we de?ne MCI as the weighted sum of changes in the exchange
rate (ER in logs) and in the interest rate (INTR in levels) from their levels in a chosen
base year. Following Freedman (1994, 1995), the formula for MCI is as follows:
MCI
t
¼ v
INT
½INT
t
2INT
b
? þv
ER
½logðER
t
Þ 2logðER
b
Þ? ð1Þ
where INT
t
and ER
t
are interest rate (overnight rate) and exchange rate at time t, at a
given base year, respectively. The most important factor is weights, v as the value of
these weights provides useful information regarding the relative importance of interest
rates and exchange rates. If MCI or FCI increases by one unit, it is equivalent to a one
percentage point increases of interest rate. In this context, it needs to be emphasised that
the level of the MCI depends on the base value, the chosen weights and the measures
of the interest rate and the exchange rate.
In light of the criticism on MCI, Gauthier et al. (2004) suggest some methods to
improve the construction of MCI:
.
weights are derived from reduced form IS-PC framework;
.
the weights are obtained by summing up the coef?cients on the lags variables as
well as by including individual lags in MCI to take into account the dynamics of
those variables over time; and
.
the weights is derived from the determinants of aggregate demand.
Following the methodology by Gauthier et al. (2004), we present a simple model which
is the equivalent of a conventional backward-looking aggregate demand:
p
t
¼
X
n
i¼1
b
1i
p
t2i
þ
X
b
i
y
t2j
þ1
t
ð2Þ
y
t
¼
X
n
t¼1
g
t
y
t21
þ
X
n
j¼1
l
j
rir
t2j
þ
X
n
k¼1
h
k
rer
t
þm
t
ð3Þ
where p
t
is equal to 100
*
[ln (CPI
t
/CPI
t212
)], where CPI is the consumer price index, and
the output gap, y
t
is the difference between actual and potential output, is calculated as
the percentage deviation of the natural logarithm of the monthly industrial production
from a Hodrick-Prescott trend with a smoothing parameter of 1,600. The ex-post real
short-term interest rate, rir
t
is measured as the real short-term money market rate. The
?nancial market (rsp) is proxied by, stock price index of Bursa Malaysia. We calculate
the long-term of the assets prices using the above Hodrick-Prescott ?lter methodology.
The sample covers the period from 1980 to 2004, the period before the introduction
of the new monetary policy framework in Malaysia. Figure 1 plots the component of
MCI, the interest rate, exchange rate and in?ation rate.
The parameter l gives the effect on aggregate demand of a one percentage point
increases of the interest rate, controlling for the effects of the interest rate impulse on
the exchange rate. The parameter h represents the corresponding effect of a 1 percent
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appreciation of the domestic currency. The relative MCI weight v is
^
l= ^ h, where
^
l and
^ h are the estimated coef?cients from equation (3).
Following the pioneering contribution of Alchian and Klein (1973) and more recently
Eika et al. (1996), Mayes and Viren (2000) and Goodhart and Hofmann (2001), we
formulate a formal model of the economy in order to show the importance of ?nancial
variables in the conduct of monetary policy. The simple model in equations (2)
and (3) which is the equivalent of a conventional backward-looking aggregate
demand-aggregate supply, but augmented with the asset markets (an extended version
of Rudebusch and Svensson (1999) and as suggested by Goodhart and Hofmann (2001)):
y
t
¼
X
n
t¼1
g
t
y
t21
þ
X
n
j¼1
l
j
rir
t2j
þ
X
n
k¼1
h
k
rer
t
þ
X
n
k¼1
f
k
rsp þm
t
ð4Þ
We use Granger-causality test to determine the predictive contents of MCI and FCI
against in?ation and output as follow:
Y
t
¼ C þa
1
Y
t21
þa
2
Y
t22
þ · · · þa
p
Y
t2p
þb
1
X
t21
þb
2
X
t22
þ · · · þb
p
X
t2p
þm
t
ð5Þ
the following restriction is imposed on the parameters:
b
1
¼ b
2
¼ · · · ¼ b
p
¼ 0 ð6Þ
If the MCI and FCI is a good predictor of the in?ation or output, the null hypothesis will be
rejected.
Table I presents the estimation results of the aggregate demand for deriving the
weights of MCI and FCI. The equations are estimated by ordinary least square (OLS).
Figure 1.
Macroeconomic variables
–2
0
2
4
6
8
10
12
14
1
9
8
2
:
1
1
M
1
9
8
3
:
1
1
M
1
9
8
4
:
1
1
M
1
9
8
5
:
1
1
M
1
9
8
6
:
1
1
M
1
9
8
7
:
1
1
M
1
9
8
8
:
1
1
M
1
9
8
9
:
1
1
M
1
9
9
0
:
1
1
M
1
9
9
1
:
1
1
M
1
9
9
2
:
1
1
M
1
9
9
3
:
1
1
M
1
9
9
4
:
1
1
M
1
9
9
5
:
1
1
M
1
9
9
6
:
1
1
M
1
9
9
7
:
1
1
M
1
9
9
8
:
1
1
M
1
9
9
9
:
1
1
M
2
0
0
0
:
1
1
M
2
0
0
1
:
1
1
M
2
0
0
2
:
1
1
M
2
0
0
3
:
1
1
M
2
0
0
4
:
1
1
M
0
20
40
60
80
100
120
140
160
180
Inflation Interest Rate Real Effective Exchange Rate
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In order to obtain well behaved residuals, a number of impulse dummies, which are
mainly related to the oil price shocks and Asian ?nancial crisis, have also been
included. The lag orders is chosen by a general-to-speci?c modelling strategy and we
report t-statistics in parentheses. For each equation, we report the adjusted R
2
, White’s
(1982) test for heteroskedasticity (H) and a Lagrange-Multiplier (LM) test for serial
correlation. The diagnostic tests suggest that there is no evidence of misspeci?cation
in all estimated equations.
The estimation for the Phillips curve shows that the output gap is signi?cant
at 5 percent level. The coef?cient estimates suggest that an increase in the output gap
by one percentage point leads to an increase of the in?ation rate between 0.37 and
0.41 percentage points. Furthermore, the results for the IS-curve in equation (4) suggest
that the real interest rate, the real exchange rate, and real equity prices have a signi?cant
effect on the output gap. The real interest rate coef?cient estimate of 0.29 in the FCI
equation is signi?cantly smaller than the coef?cient estimates of 0.46 for MCI, while the
real share price has a relatively weak effect on output gaps with the coef?cient of 0.06.
Using the estimated coef?cients of interest rate and the exchange rate, the ratio
(weight) of MCI is 3.8:1. The estimated coef?cients suggest that a one percentage point
rises in real interest rate is roughly equivalent to four-percentage-point increase in real
effective exchange rate (REER) appreciation on real GDP growth. The result also
indicates that the interest rate channel is found to be more powerful than the exchange
rate channel for in?ation. This is consistent with the empirical ?ndings of relatively
high pass-through of interest rate in Malaysia (Zulkhibri, 2010) and a low exchange
rate pass-through to domestic prices in Malaysia.
In the case of FCI, using the estimated coef?cients of interest rate and stock prices,
generate the ratios (weights) of 3.4:1 for the exchange rate and 16.6:1 for the stock prices,
respectively. Although the impact of interest rate has slightly deteriorated with respect
For deriving MCI
weight
p
t
¼
ð2:20Þ
0:453p
t21
þ
ð2:30Þ
0:201p
t23
2
ð3:45Þ
0:124p
t24
þ
ð4:34Þ
0:412y
t22
þ
ð2:56Þ
0:455DCRISIS
R
2
¼ 0:73 H¼21.26 (0.34) LM¼7.73 (0.12)
y
t
¼
ð2:20Þ
0:863y
t22
þ
ð3:30Þ
0:113y
t28
2
ð2:25Þ
0:462rir
t21
þ
ð2:04Þ
0:164rer
t24
þ
ð3:56Þ
0:987DCRISIS
R
2
¼ 0:73 H¼23.36 (0.34) LM¼7.24 (0.12)
For deriving FCI
weight
p
t
¼
ð4:20Þ
0:371p
t21
þ
ð3:30Þ
0:265p
t23
2
ð3:45Þ
0:212p
t23
þ
ð3:34Þ
0:371y
t21
þ
ð2:56Þ
0:055DCRISIS
R
2
¼ 0:78 H¼24.66 (0.34) LM¼8.76 (0.12)
y
t
¼
ð8:20Þ
0:563y
t21
þ
ð3:30Þ
0:131y
t25
2
ð4:45Þ
0:291rir
t21
þ
ð2:34Þ
0:161rer
t24
þ
ð4:56Þ
0:063rsp
t21
þ
ð3:43Þ
0:987DCRISIS
R
2
¼ 0:86 H¼16.66 (0.34) LM¼10.76 (0.12)
Notes: The table reports the results of estimating equations (1) and (2); coef?cients estimates are
reported with t-statistic in parentheses;
R
2
is the adjusted coef?cient of determination; H is White’s
(1982) test for heteroskedasticity and LM is a Lagrange-Multiplier test for serial correlation up to order
of six; in parentheses, we report probability values for diagnostic test
Table I.
Regression results –
backward-looking
aggregate demand
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to the changes in the exchange rate, the impact of stock price seems to be very small. The
one percentage point rises in the real interest rate equivalent 16.6 percentage point
increase in real stock price on real GDP. It is interesting to note that the importance of the
REER relative to the real interest rate is relatively similar in magnitude for both the MCI
and FCI.
We compute the MCI index using a three-month interbank interest rate and the US
dollar/Malaysia ringgit exchange rate (Figure 2). The ?rst month of 1990, which is the
periodat whichthe level of economy operates at the long-run equilibriumis chosen as the
base period(the weights are takenfromequation(2)). The results of MCI index suggests a
distinct easingof monetary conditions in 1985-1988, re?ecting a weaker ringgit and relax
lending policy by banks, which reduce the real interest rate, and thereafter contribute to
higher economic growth. However, macroeconomic measures to curb credit supply
together with the interest rate hikes in 1991, resulted in tighter monetary conditions.
Looking at the path of FCI over time (Figure 3), ?nancial conditions tightened
sharply in 1984 and 1985, contributing to a slowdown in economic activity. As the FCI
improved, economic activity picked up again in 1986 and 1987. Economic activity was
robust as well as the ?nancial conditions remained accommodative in 1988-1989. The
FCI indicates a slight expansionary in the late 1990s and a strong expansionary
starting in 1990. The overall performance shows that the FCI can better explain the
behavior of in?ation from 1990 to 1992 than the MCI.
The recession of 1997-1998 coincided with another plunge in the FCI and output. The
fall in equity prices from the peak of bubble in 1997 until the trough in 1998, implies the
tightening of ?nancial conditions and a restrictive monetary policy stance. This is also
explained the persistent and low in?ation behavior since 1999. The brief rebound in the
economic activity in mid-1999, however, was not entirely unanticipated by the FCI.
As evidenced in Figure 1, the looseness of monetary policy in Malaysia has hardly
changed over the few years. All in all, the nominal MCI and FCI, taking into account the
very recent changes in domestic monetary conditions, reveals similar results derived
from the real MCI and FCI. The indices pointed to the fact that monetary easing
Figure 2.
Monetary Condition Index
in Malaysia: 1982-2004
–15
–10
–5
0
5
10
15
20
1
9
8
2
:
0
1
M
1
9
8
3
:
0
2
M
1
9
8
4
:
0
3
M
1
9
8
5
:
0
4
M
1
9
8
6
:
0
5
M
1
9
8
7
:
0
6
M
1
9
8
8
:
0
7
M
1
9
8
9
:
0
8
M
1
9
9
0
:
0
9
M
1
9
9
1
:
1
0
M
1
9
9
2
:
1
1
M
1
9
9
3
:
1
2
M
1
9
9
5
:
0
1
M
1
9
9
6
:
0
2
M
1
9
9
7
:
0
3
M
1
9
9
8
:
0
4
M
1
9
9
9
:
0
5
M
2
0
0
0
:
0
6
M
2
0
0
1
:
0
7
M
2
0
0
2
:
0
8
M
2
0
0
3
:
0
9
M
2
0
0
4
:
1
0
M
Tighter
Easier
MCI (real) MCI (nominal)
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in Malaysia has to end in the near future and one should expect a rate hikes, given the
emerging risk to the in?ation in the economy.
To give the answer to the predictive contents of MCI and FCI, Table II reports the
Granger-causality test between in?ation and the MCI (or FCI). The Granger-causality test
rejects the null hypothesis that the MCI (or FCI) explainthe in?ationbehavior, whereas the
null hypothesis that the in?ation explains MCI (or FCI) cannot be rejected. The results
suggest that immediate sign of in?ationary pressure in the economy is followed by
adjustments in either interest rate or exchange rate depending on policy preferences. The
results are also consistent with the role of monetary policy in maintaining price stability.
7. Conclusion
The analysis provides a more uniform analysis of measuring the monetary conditions in
Malaysia. It is generally considered that a tightening in monetary policy slows demand in
the economy as credit becomes more expensive. The aim of such monetary policy
tightening is to reduce in?ation, but the unintended consequence will lead to a slowdown in
economic activities. It is a well-known feature of monetary policy operation that authorities
aim to exercise control over short-term interest rates by adjusting the of?cial rate.
The approach of the estimation of MCI and FCI is based on the conventional
backward-looking aggregate demand and is intended to address one or more criticisms
Figure 3.
Financial Condition Index
in Malaysia: 1980-2004
–15
–10
–5
0
5
10
15
20
1
9
8
1
:
0
1
M
1
9
8
2
:
0
1
M
1
9
8
3
:
0
1
M
1
9
8
4
:
0
1
M
1
9
8
5
:
0
1
M
1
9
8
6
:
0
1
M
1
9
8
7
:
0
1
M
1
9
8
8
:
0
1
M
1
9
8
9
:
0
1
M
1
9
9
0
:
0
1
M
1
9
9
1
:
0
1
M
1
9
9
2
:
0
1
M
1
9
9
3
:
0
1
M
1
9
9
4
:
0
1
M
1
9
9
5
:
0
1
M
1
9
9
6
:
0
1
M
1
9
9
7
:
0
1
M
1
9
9
8
:
0
1
M
1
9
9
9
:
0
1
M
2
0
0
0
:
0
1
M
2
0
0
1
:
0
1
M
2
0
0
2
:
0
1
M
2
0
0
3
:
0
1
M
FCI (real) FCI (nominal)
Tighter
Easier
H
0
Lag(s) F-statistic p-value
In?ation -/ !MCIs 12 1.931
*
0.031
MCIs -/ ! In?ation 12 1.388 0.171
In?ation -/ ! FCIs 12 2.034
*
0.022
FCIs -/ ! In?ation 12 1.109 0.353
Notes: Signi?cant at:
*
5 percent level; -/ ! denotes that X does not Granger cause Y; sample period
from 1982 to 2004
Table II.
Granger-causality test
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in the literature. The MCI and FCI are calculated in both real and nominal terms to
assess how “tight” or “loose” are the monetary conditions. The results suggest that
the despite its ability to explain the monetary conditions in Malaysia, the method in
determining the weights for each policy component of the index indicates some degree
of instability. As such, MCI and FCI do not offer a precise signal on the state of
monetary condition in Malaysia.
On the surface, the MCI or FCI seems to be straightforward, easy to understand and
timely to construct. The results also show that the movement in?ation induces the
movement in either interest rate or exchange rate. However, the diverging movements
in equity and housing prices have also raised concerns about the appropriate stance of
monetary policy when markets are moving in different directions (Goodhart and
Hofmann, 2001; Mayes and Viren, 2000). The uncertainty surrounding its construction
makes it an unreliable stand-alone indicator and further investigation is necessary to
identify the actual role of asset prices in the transmission mechanism of monetary
policy in Malaysia.
Notes
1. Four central banks, those for Canada, New Zealand, Norway and Sweden publish an MCI
and to varying degree, use their respective MCIs in the conduct of monetary policy.
Additionally, the International Monetary Fund (IMF) and the Organisation for Economic
Cooperation and Development (OECD) calculate MCIs for evaluating the monetary policies
of many countries.
2. It may be misleading, especially in periods of high in?ation.
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May, pp. 225-7.
Milman, G.J. (1995), The Vandal’s Crown: How Rebel Currency Traders Overthrew the World’s
Central Banks, The Free Press, New York, NY.
About the author
Muhamed Zulkhibri Abdul Majid is an Economist currently working in the Economic Research
and Policy Department, Islamic Development Bank. Previously, he has been a Research Manager
in Central Bank of Malaysia and a Visiting Lecturer at the University Putra Malaysia.
His research concerns monetary, ?nancial economics, banking and applied econometrics. His
research papers have appeared (forthcoming) in various international journals including: Journal
of Asian Economics, Applied Financial Economics, Economics System, Emerging Markets
Review, Journal of King Abdul Aziz University: Islamic Economic, International Review of
Economics, Economic Change and Restructuring and Journal of Asia-Paci?c and Business.
He holds a B.Commerce in Accounting and Finance from University of Birmingham, UK and a
PhD in Monetary Economics from University of Nottingham, UK. He is also a Certi?ed Financial
Planner. Muhamed Zulkhibri Abdul Majid can be contacted at: [email protected]
Measuring
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