Post on 17-Jan-2023
DIPARTIMENTO DI SCIENZE ECONOMICHE
n.142
Giulio Cifarelli and
Giovanna Paladino
THE INTERNATIONAL RESERVES GLUT: IS IT FOR REAL?
January 2006
UNIVERSITA’ DEGLI STUDI DI FIRENZE
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Direttore responsabile: Prof. Giovanni Andrea Cornia
Comitato di Redazione: Proff. Antonio Gay, Piero Innocenti, Alessandro
Pacciani, Piero Roggi
Coordinatore Scientifico: Dott. Nicola Doni
Pubblicazione depositata a norma di legge
The International Reserves Glut: Is It for Real?
Giulio Cifarelli* and Giovanna Paladino**
January 2006
Abstract
Monthly data from January 1985 to December 2004 are used to investigate reserves management in
ten Asiatic and Latin American countries. Idiosyncratic explanatory variables enter cointegration
relationships based on a stochastic buffer stock model, where a reserve variability measure is obtained
via conditional variance approaches. International factors influence the cointegration residuals
(representing the excess demands for reserves), which tend to co-move within and across
geographical areas. Principal components analysis is implemented then to associate their common
drivers with the US fed fund effective interest rate and real effective exchange rate. This two-step
approach sheds light on some controversial aspects of reserves and exchange rate management in
emerging markets such as “fear of floating” and mercantilist behavior. Our results suggest, contrary to
common belief, that the size of recent excess reserves holdings is probably overstated.
Keywords: Emerging Markets’ International Reserves, Cointegration Analysis, Principal Components Analysis. JEL Code: F310, F340, G150 _____________________________ * University of Florence, Economics Department, via delle Pandette 9, 50127 Florence, Italy. giulio.cifarelli@unifi.it ** LUISS University and Sanpaolo IMI Economic Research Dept., viale dell’Arte 25, 00144 Rome, Italy. giovanna.paladino@sanpaoloimi.com
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Introduction
The average reserve holdings of several emerging markets have risen in recent years,
irrespectively of their official exchange rate regime, both in absolute terms and as percentages
of GDP or of standard international trade and finance adequacy indicators, such as the level of
imports or the short term external debt. They have reached $1.8 trillion at the end of 2004, up
roughly $800 billion from 2002 and more than four times their level in 1994, exceeding the
potential requirements of any foreseeable shock. So what is the purpose of possessing
international reserves in excess of common measures of adequacy? This puzzle has attracted
considerable attention from both practitioners and academics. Precautionary behavior of
central banks, fear of the disruptive effects of an exchange rate depreciation (motivated by the
experience of the financial crises and sudden capital reversals of the 1990s), difficult access to
international capital markets and mercantilist export support are possible explanations set out
in a burgeoning literature.
In this paper monthly data from January 1985 to December 2004 are used to investigate the
national idiosyncratic and international determinants of reserve changes in five Asiatic and
five Latin American countries. Idiosyncratic explanatory variables are mostly associated with
the tenets of the benchmark buffer stock model of Frenkel and Jovanovic (1981) while
international explanatory factors reflect the pivotal role played by the U.S. monetary
authorities in emerging markets finance.
The use of a monthly frequency has a number of drawbacks due to the non stationary nature
of the time series and to the difficulty of estimating consistent cointegration relationships and
error correction structures. The twenty year data span, however, provides a large number of
observations and allows to estimate the relevant relationships for each country in isolation.
Most previous empirical studies (Flood and Marion, 2002, Aizenman and Marion, 2004,
among others) are performed with yearly data and rely on the panel data approach. The latter
posits that the specification of the relationship is the same over the cross section and that any
idiosyncratic effect is adequately captured by differences in the constant term. In the present
investigation, however, the specification of the demand for reserves, the sign and the
significance of the coefficients and the speed of convergence to equilibrium differ too much
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across the countries of the sample to be properly assessed using a pooled data procedure. A
different estimation methodology is called for.
The rapid growth of short term capital flows provides a sensible explanation for the relevance
of the precautionary demand for reserves of the buffer stock model. Capital flows to and from
emerging markets, however, have specific characteristics that go beyond the standard
approach. According to the buffer stock model international reserves are explained by
domestic factors only, reserve volatility and a national bond yield which reflects the
opportunity cost of holding them. This parameterization misses some relevant determinants of
reserve behavior. International financial flows involving emerging markets are highly volatile
and are affected by US monetary policy. Over the last two decades convincing evidence
shows that financial turbulence (and the ensuing impact on reserve holdings) is not restricted
to the epicentre of a crisis but tends, usually, to spread from country to country within and
even across geographical areas. The two-step estimation procedure implemented in this paper
is meant to capture these aspects of emerging markets finance.
Cointegration reserve relationships are independently estimated, at first, for each country. The
corresponding residual time series are assumed to quantify the fraction of the long run reserve
holdings which cannot be explained by the buffer stock model explanatory variables.1 Their
correlation is carefully analyzed in a second step and the relevance of common drivers is
assessed using principal components analysis. Geographical area co-movements are
identified; the behavior of the first principal component - which explains almost 40 percent of
the variability of the residuals of the Asiatic reserve cointegration relationships - turns out to
be affected by the U.S. federal funds and real effective exchange rate.
The signs of the estimated coefficients suggest that reserve residual co-movements reflect
both the generalized impact of U.S. monetary policy due to a “fear of floating” reaction
(Calvo and Reinhart, 2002) and a common desire to prevent real exchange rate appreciation.
The latter may be related to an export support mercantilist exchange rate policy (Aizenman
and Lee, 2005 ).
The paper improves upon previous research in the following aspects:
1 The two-step approach fits well with the model of Frenkel and Jovanovic which posits that observed reserves are proportional to optimal reserves – determined according to the buffer stock paradigm – up to an error term that is uncorrelated with the determinants of the latter. Optimal reserve holdings are the fitted values of the cointegration relationships.
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- the empirical section includes an accurate analysis of the time series; additive outliers that
may affect both the unit root tests and the cointegration results are disposed of using
approaches set forth by Perron and Rodríguez (2003) and Nielsen (2004).
- Reserves volatility clustering is modelled using conditional volatility techniques; the
IGARCH parameterizations produced by the data suggest that the volatility estimates in
previous research, that relied on unconditional variance estimation, may well be biased.
- The two-step estimation approach sheds light on some controversial aspects of exchange
rate management, such as “fear of floating” and mercantilist behavior. A distinction is
drawn between the national (idiosyncratic) and the international factors that affect reserve
holdings. The former enter the long run cointegration relationships while the latter
influence the disequilibrium residuals, which tend to co-move across countries. Principal
components analysis is then used to associate their common drivers to US monetary and
exchange rate policy.
- Contrary to common belief and, indeed, to the implications of standard adequacy
measures, our analysis suggests that current excess reserve holdings are not consistently
larger than their 1995-2004 averages.
The paper is structured as follows. Section 1 summarizes the theoretical and empirical
discussion on reserve adequacy rules and on optimal reserve holdings; section 2 measures
reserve excess demand, using rules of thumb and cointegration analysis; section 3 investigates
excess reserve co-movements and assesses the relevance of “fear of floating” and of
mercantilist export support policies. Section 4 concludes the paper.
1 The demand for international reserves
In this section the literature on the demand for international reserves is briefly summarized.
The structure of the dynamic stochastic model set forth by Frenkel and Jovanovic (1981) is
then examined in more detail as it constitutes the theoretical framework of the applied
analysis of the subsequent sections.
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1.1 From rules of thumb to optimal reserve management
An adequate stock of reserves is assumed to finance potential gaps between outlays and
receipts of foreign currency, smoothing out external payment imbalances and preventing in
that way an exchange rate crisis. It is typically determined by a ratio between the minimum
amount of reserves the authorities have to hold on average and a component of the balance of
payments. In the 1950s and 1960s the trade balance was the largest aggregate of the latter and,
according to standard Keynesian macroeconomics, reserves were geared to imports. Studies
by the IMF Staff (1958) and Triffin (1960) suggested that reserve adequacy required a
minimum average yearly reserve to import ratio of 30-35 percent.
The relative marginalization of trade aggregates in the overall balance of payments accounts
brought about by the liberalization of capital flows in the 1980s and 1990s called for the
introduction of a more effective rule of thumb. It was suggested that since recent currency
crises tended to be associated with capital outflows rather than with trade financing, the size
of the reserves of emerging market economies be somehow related to their short run external
debt outstanding. The Asian crisis had shown that the countries that held large reserves had
been able to weather the turbulence better than the others.2
Two proposals for a new minimum reserve stock benchmark were set out in 1999,
respectively, by Pablo Guidotti, Argentina’s former Deputy Minister of Finance, and Alan
Greenspan, Chairman of the US Federal Reserve Board, both involving short term emerging
market debt.
Guidotti proposed, as an empirical rule of thumb for reserve stock adequacy, that a country be
able to satisfy its net external payments liabilities without additional foreign borrowing for up
to one year. The current account deficit is thus included in this reserve adequacy criterion
along with short term debt. In a similar way Greenspan suggested to calibrate reserve
adequacy on short term debt outstanding with maturity of less than one year. He introduced,
however, two additional conditions: (i) that the average maturity of a country’s external
2 A link has been identified in a recent IMF study (IMF, 2000) between short term debt over reserves and the likelihood of a crisis in a sample of emerging market economies. In the same way Early Warning System studies by Bussière and Mulder (1999), among others, have found that low reserve to short term external debt ratios increased the probability of a crisis.
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liabilities exceed a three year threshold and (ii) that the reserve authorities operate following a
“liquidity at risk” procedure.
A third reserve adequacy criterion is the reserve to broad money (M2) ratio. Kaminsky et al.
(1997), among others consider it an accurate predictor of crises. De Beaufort Wijnholds and
Kapteyn (2001) point to the usefulness of this ratio for assessing the relevance of internal
demand for foreign reserves due to possible capital exports by domestic residents.
In an authoritative study Heller (1966) went beyond reserve adequacy and analyzed
precautionary optimal reserve management by monetary authorities. International liquid
reserves allow to finance external imbalances and to avoid deflationary measures with a
relevant macroeconomic cost. Reserve holdings, however, have an opportunity cost given by
the difference between the social yield on capital invested and the (lower) yield of
international reserves. The optimal stock of reserves corresponds to the amount which
minimizes the sum of the cost of adjusting for and of financing the balance of payments
disequilibrium in such a way that the marginal cost of the former equals that of the latter. The
analysis of Heller is relevant also from a formal point of view.3 In spite of the shortcomings
pointed out by Hamada and Ueda (1977), it provides the basic original framework for most
subsequent theoretical and empirical research on optimal foreign reserve management.
1.2 The stochastic buffer stock model of the demand for reserves
Extending a previous model on transactions and precautionary demand for money, Frenkel
and Jovanovic (1981) set forth a stochastic reformulation of optimal reserve demand based on
the tenets of inventory management. Their model posits that changes in reserve holdings,
between restockings, be modelled by the following stochastic equation
dR dt dWt t= − +µ σ (1)
3 He assumes that the process of change in the stock of international reserves is a random walk and provides an algebraic formulation which links the optimal amount of reserves a country should hold to the propensity to import, the opportunity cost of holding reserves and to a proxy for the stability of its international accounts, as reflected by the average yearly past imbalances.
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where Wt is a Wiener process with mean zero and variance t. At any point in time the
distribution of the reserve holdings reads as
R R Wt t= − +0 µ σ (2)
where R0 is the optimal initial stock of reserves, µ is a drift parameter and σ is the standard
deviation of the Wiener reserve increment. Optimal reserve management involves the
selection of the cost minimizing stock of reserves once reserves have reached a lower bound,
set here to zero. Since reserve holdings follow a stochastic process, the authorities are
assumed to select the initial level of reserves R0 that minimizes total expected costs. Costs
here have two interrelated components : (i) the opportunity cost of reserve holdings and (ii)
the adjustment cost of reserve restocking whenever the latter have reached their lower bound.
The latter stems from the output (welfare) reduction brought about by the need to generate the
balance of payments surplus, which will generate the reserve build up.
Frenkel and Jovanovic assume that balances of payments tend on average to be in equilibrium
and that the reserve drift between restockings µ is zero. They obtain, after some algebraic
manipulation, the following second order Taylor series approximation of optimal initial
reserve holdings in logarithmic terms
log . log . logR c r0 05 0 25= + −σ (3)
The crucial additional assumption is then made that observable reserves Rt are proportional
to optimal (initial) reserves up to an error term that is uncorrelated with σ and r. The
following testable relationship is then derived
log log logR b b b r ut t t t= + + +0 1 2σ (4)
where it is assumed a priori that b1 0> and b2 0< .4
4 Flood and Marion (2002) argue that optimal and observed reserves are linked by the relationship R BR et
ut= −0 ,
where B is a country specific proportionality factor.
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Reserve holdings at time t are reduced if the opportunity cost rt rises and increased whenever
their volatility σ t rises. A higher volatility implies that holdings are likely to hit their lower
bound more frequently and require costly restockings that the authorities want to avoid.
The resurgence of interest for reserve hoarding management in the aftermath of the financial
crises of the 1990s has prompted various attempts to adapt precautionary demand modelling
to the financial characteristics of emerging market countries. The latter have to face a limited
access to international borrowing in periods of stress, an inefficient tax collection system and
- at times – a severe default risk. Reserves are assumed to have an insurance value. Aizenman
and Marion (2004), using a two period intertemporal consumer utility maximization model,
suggest that reserves reduce the cost of consumption smoothing between prosperous and bad
states of nature. In the latter the marginal cost of public funds would be much higher.
Subsequent studies focus on an output stabilization role of international reserves. Aizenman et
al. (2004) show that reserve holdings mitigate the probability of a banking crisis and thus
reduce the expected output costs of a sudden freeze of international capital inflows. Their
demand for reserves increases both with the expected output cost of a credit crisis and with
the effectiveness of reserves in reducing the probability of the crisis. As shown in Aizenman
and Lee (2005) a macro liquidity shock to an emerging market cannot be diversified away and
may force the liquidation of a first period investment if it exceeds the stock of reserves
outstanding, reducing second period output. Optimal reserve management diminishes
potential liquidation costs.
1.3 Empirical investigation on precautionary reserves management
A large body of empirical research relies on the buffer stock model paradigm, spanning more
than twenty years – see Bahmani-Oskooee and Brown (2002) for a comprehensive survey.
Reserves are typically linked to four regressors in a panel data study: a variability measure,
the marginal (or average) propensity to import, the level of imports and an opportunity cost
proxy.
It is generally found that net external payments variability exerts a positive and significant
impact on reserve holdings; the demand for the latter increases with the fluctuations in the
balance of payments, quantified in various ways by a variability index.
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The sign of the coefficient of the second regressor, the propensity to import, is more
controversial. On Keynesian grounds it should be negative; the larger the propensity to
import, the smaller the adjustment cost when expenditure reduction policies are called for and
the smaller the demand for reserves (Heller, 1966). If, however, the propensity to import is
assumed to quantify a country’s openness and vulnerability to external shocks, the sign of the
coefficient should be positive as more reserves are required whenever the propensity rises
(Aizenman and Lee, 2005).
The third regressor, the value of imports, is positively related to the demand for reserves. It is
believed to measure the effects of trade and is used as a scale variable.5. Indeed Heller (1968),
assumed that banks’ transactions demand for foreign exchange increased with the square root
of the level of transactions. De Beaufort Wijnholds and Kapteyn (2001) point out that the
presence of economies of scale (which justify the use of imports as scale variable) hinges on
the hypothesis that balance of payments disequilibria grow in proportion to international
transactions.
The opportunity cost of holding reserves has been measured in various ways. Frenkel and
Jovanovic (1981) used government bond yields and Edwards (1985), among many others,
spreads between domestic and corresponding US interest rates. The negative coefficient of the
opportunity cost regressor is seldom significantly different from zero, a finding that can be
attributed to the risk averse nature of central banks.
The choice of the variables entering the cointegration analysis performed in the paper - as the
first step of the estimation procedure - is broadly in line with the above mentioned research.
Long run reserve demand is assumed to be related to a variability index, the level of imports
and to a long term bond yield. The average propensity to import is dropped from the analysis.
The problems associated with openness and external vulnerability are dealt with in the second
step.
5 A positive coefficient, smaller than one in absolute value, would signal the presence of economies of scale. Real GDP, real GDP per capita or population size are also used as scaling variables. .
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2 Assessment of the excess demand
One of the main lessons to be drawn from the Asian and Latin American crises is that fear of
insolvency may trigger sudden and dangerous capital reversals even in countries that do not
seem to have - prima facie – serious debt sustainability problems. Reserve and debt
management are thus key factors in crisis prevention.6 Holding foreign assets is costly and
while the case for Central Bank reserve accumulation is evident, it is still not clear what is
their optimal level. Indeed, some countries seem to have an insatiable appetite for reserves as
if “Mrs Machlup’s wardrobe theory” were to hold.7.
In this section measurements of excess reserve demand are set forth for ten countries located
in Asia and in Latin America using two alternative approaches.
2.1 Stylized evidence from the rules of thumb
In the absence of a widely accepted theoretical approach to optimal reserve size, popular rules
of thumb, summarized in section 1.1 above, have supplied guidance to central bankers.
[Insert Table I]
The computation of five different reserve adequacy benchmarks for Argentina, Brazil, Chile,
Mexico, Venezuela, Indonesia, Korea, Malaysia, the Philippines and Singapore is set forth in
table I. The description and the sources of the time series used in the empirical analysis can be
found in Appendix I.
The first benchmark is measured in terms of month worth of imports. It can be seen –
according to a balance of trade and/or a consumption smoothing criterion – as a way to ensure
a certain degree of autonomy from scarce international borrowing. International reserves are
thus required to cover 4 to 6 months of imports. This index has lost most of its relevance over
time as emerging economies increasingly rely on private capital flows to balance their
external accounts. The Guidotti rule discussed above is motivated by this development and by
6 We have also seen in the recent crises that countries that had big reserves by and large did better in withstanding contagion than those with smaller reserves..” , (p.1-3). Fischer, S. (2001). ‘Opening Remarks’, IMF/World Bank International Reserves: Policy Issues Forum (Washington, DC, April 28). 7 Professor Machlup suggested that monetary authorities tended to maximize the stock of reserves as his wife was maximizing the amount of clothes in the wardrobe. .
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evidence that vulnerability, during the Asian crisis, was mainly related to a country’s asset
and liquidity management. It provides a benchmark based on a ratio of reserves to short term
debt and is meant to gauge the capability of a country to live without foreign borrowing for up
to a year (in that case the reserve adequacy ratio would be 100 per cent). The third column
expands the Guidotti rule and incorporates an additional cushion of 3 percent over and above
short term external debt in order to buy time for the required policy change before the
reserves to short term debt threshold is reached (Bird and Rajan, 2002).
De Beaufort Wijnholds and Kapteyn (2001) argue that a reserve to short term debt ratio may
fail to capture the threat of an “internal drain” due to capital exports by residents. Capital
flights are accounted for with greater accuracy by the measure based on broad money supply
(M2) of column four. The ratio of reserves to M2 should be close to 30 percent as only a
fraction of M2 may realistically be expected to be mobilized on short notice.
The last index too takes into account both internal and external capital drains. It is based on
the Greenspan “liquidity at risk” refinement of the Guidotti rule.8 We implement here the
version set out by De Beaufort Wijnholds and Kapteyn (2001). The denominator of the
Guidotti rule is modified introducing a “probability factor” which depends on a country risk
index weighted according to the exchange rate regime. The ratio is fully explained in the
notes to table I.
This table sets forth adequacy measures computed for the years 1985, 1995 and 2004. They
are underlined if the estimates exceed the corresponding adequacy benchmarks. The reserve
to import ratios record a net improvement over time, with the exception of Mexico and the
Philippines, where the increase is less relevant. As for the remaining indexes, they suggest
that all the countries in the sample currently hold reserves that are significantly above the
adequacy thresholds set at 100 percent and 30 percent for, respectively, the Guidotti and
Guidotti-Greenspan rules and for the reserve to M2 ratio. Our findings so far, in line with the
common wisdom, support the hypothesis of a generalized increase in reserve holdings from
1985 to 2004.
8 A. Greenspan (1999). ‘Currency Reserves and Debt’, remarks at the World Bank Conference on Trends in Reserve Management (Washington, DC, April 29).
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2.2 Long-run demand and reserves misalignment
Conventional adequacy rules are simplistic by construction and may underestimate emerging
markets reserve requirements. This section describes the econometric strategy used to
determine the optimal long run demand for reserves. This measure of reserve adequacy
evolves over time and provides a dynamic benchmark that can be used to assess the relevance
of overstocking. The optimal buffer stock model demand for reserves, discussed in section
1.2, is estimated using Johansen’s (1988, 1991) cointegrating approach. The data set spans the
January 1985 - December 2004 time period and encompasses some important episodes of
distress both in Asia and Latin America.
2.2.1 Stationarity and volatility analysis
Recent econometric findings summarized in Vogelsang (1999) have shown that additive
outliers introduce in the residuals of standard unit root test estimates a moving average
component with a negative coefficient which, in turn, inflates the size of the test and causes
over-rejection of the null hypothesis. The Latin American and Asiatic crises have brought
about long lasting changes in Central Bank behavior and the corresponding outliers in the
time series may well be considered additive in the sense of Hendry and Doornik (1994). We
have implemented the test procedure of Perron and Rodríguez (2003) and have identified
several additive outliers. The value of the test statistics that are significant at the 5 percent
level and the corresponding dates are set out in Appendix II. The unit root tests of table II are
thus performed using the statistic by Ng and Perron (2001), which is robust to size distortions
due to negative serial correlation of the residuals. With one single exception, they fail
systematically to reject the null of non stationarity.
[Insert table II]
Additive outliers may also distort inference on cointegration rank in finite samples (Franses
and Haldrup, 1994). Following the interpolation strategy suggested by Nielsen (2004), the
outlying observations are eliminated and replaced by an average of the respective adjoining
data. The smoothed time series will then be used in the cointegration analysis below.
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Reserve volatility plays a relevant role in models of optimal demand for foreign reserves and
has to be carefully estimated. Most previous empirical studies borrow a method originally
developed by Frenkel (1974) and estimate reserve volatility as a multiperiod rolling standard
deviation of (detrended) reserve changes.9 Our sample period includes periods of turbulence
and reserve changes display volatility clustering i.e. autoregressive conditional
heteroskedasticity. The presence of ARCH effects is corroborated by the serial correlation of
the squared reserve increments. Indeed, almost all Ljung Box Q-statistics in table A.II reject
the null of no ARCH at the standard levels of significance. Unbiased volatility estimates are
thus obtained using conditional measures, computed as the square root of the GARCH(1,1)
variance of monthly reserve changes. In the case of an asymmetric response to innovations,
the following Threshold GARCH(1,1) parameterization by Glosten et al. (1993) is used to
estimate the conditional variance
σ ω α βσ γt t t t tu S u21
21
21 1
2= + + +− − − − (5)
where ut is the reserve innovation, 2tσ is the corresponding conditional variance, γ is a
coefficient of asymmetry and St-1 is a dummy which takes value 1 if ut− <1 0 , and 0 otherwise.
[Insert Table III]
Table III reports the conditional variance model estimates. Symmetric GARCH(1,1)
parameterizations have a reasonably good fit in most countries, the only exception being
Venezuela and Malaysia, where significant asymmetry is detected and a TGARCH(1,1) is
called for. The corresponding γ coefficients are negative; the volatility tends to be smaller
when, because of negative shift in reserves, the currency is under pressure to depreciate.
(Central Banks seem thus to lean with the wind when the currency depreciates and against the
wind when it appreciates.) Interestingly, conditional variances turn out to have an
IGARCH(1,1) parameterization. Standard Wald tests fail to reject the null hypotheses that
1-α-β=0 and 1-(α+0.5γ)-β=0 in, respectively, the symmetric and asymmetric GARCH(1,1)
9 This variability proxy is problematic. As pointed out by Flood and Marion (2002, appendix II), reserve volatility and measurement errors in reserves may interact. Biased OLS estimates of the volatility coefficient will follow because of the skewness of the latter. It can be shown that the use of conditional variances drastically reduces the size of the bias.
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models. Conditional variances are thus non stationary in a weak sense.10 In this case the
unconditional variance of reserve innovations is not defined and the conditional GARCH
approach provides the only meaningful measure of reserve volatility.
2.2.2 Cointegration estimates of long run reserves demand
The empirical investigation is performed using the multivariate cointegration analysis of
Johansen (1988, 1991). The selection of this approach is not arbitrary. It is motivated by its
resilience in terms of power and of size to the conditional heteroskedasticity of the VECM
residuals detected by Boswijk et al. (2000).11
[Insert table IV]
The trace test statistics set forth in table IV identify a single cointegration relationship in each
of the ten countries. The treatment of the deterministic component is not homogeneous and
reflects the differing properties of the time series.
Table V presents the cointegration equation estimates and the corresponding error correction
coefficients obtained with the FIML procedure of Johansen and Juselius (1990). The short
term components of the VECM are not set forth here for lack of space. The long run reserve
demand relationship is formulated as
log log log logR t M rt t t t t− − − − − =ρ ρ β σ β β ε0 1 1 2 3 (6)
where σ t is the fitted value of a preliminary IGARCH conditional volatility estimate of the
reserve change, Mt is the volume of imports, rt is the domestic (US dollar denominated)
government bond yield and t is a time trend.
[Insert table V]
10 In an IGARCH(1,1) the conditional variance is strictly stationary even if the model lacks unconditional moments and is thus covariance non stationary (Nelson, 1990 ). 11 They perform a Monte-Carlo comparison of the Johansen LR cointegration test and of alternative LM approaches that try to exploit the non-normality of the residuals in the case of Gaussian and non-Gaussian distributions of the VECM innovations. The Gaussian QLR test of Johansen loses power in the presence of skewness and of flat tailedness. It turns out, however, to outperform the alternative cointegration tests when the innovations exhibit, a major problem in our empirical analysis, GARCH(1,1) volatility clustering (see table 1, pages 15-16). The sizes of the cointegration tests are not seriously affected by non-Gaussianity but for the rather unrealistic case of an ARCH(1) parameterization of persistent conditional volatility.
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The estimates are far from homogeneous across countries.12 The computed values of the
coefficients of reserve volatility and of the opportunity cost proxy corroborate the
specification of the buffer stock model only in the case of the Asiatic countries. A rise in
interest rates is associated with an increase in reserve holdings in three of the five Latin
American countries, possibly reflecting - as suggested by Aportela et al. (2005) - the effect of
foreign capital inflows sterilization policies by local monetary authorities. Reserve volatility
too does not fit well with the model in Latin America: with the exception of Venezuela it is
either irrelevant or has a negative impact on the demand for reserves. Only the coefficient of
the volume of imports is significant and has the appropriate sign in most countries of the
sample. With few exceptions, its size fails to support the hypothesis of economies of scale in
the use of reserves, a finding that may be due to the impact of the introduction of reserve
adequacy rules geared on imports.
The error correction terms, finally, show that the speed of convergence of actual reserves to
their long run equilibrium level is higher in Latin American countries. Following the literature
(see Bahmani-Oskooee and Brown, 2002) we may attribute this result to the lower degree of
flexibility of the exchange rate regimes adopted – de jure or de facto – by the Asiatic
countries in the time period spanned by this research.
3 Excess reserve co-movements and international capital markets integration
Standardized cointegration residuals in figures 1 and 2 show that the amount of recent excess
reserve holdings may well be overstated. Contrary to common belief - and, indeed, to the
implications of the adequacy thresholds of table I - emerging market countries’ Central Banks
do not seem to have modified their behavior in recent years since excess reserve holdings are
not consistently larger than their 1995-2004 averages.
[Insert Figure I]
[Insert Figure II]
Having singled out the idiosyncratic factors that determine, in each country, the precautionary
demand for reserves, we investigate the co-movements among the cointegration residuals in 12 The differences involve the sign, absolute value and significance of the coefficients of most regressors and are not restricted to the constant term. These findings suggest that a panel data procedure would be misleading if applied to our data set.
15
order to assess the relevance of common international drivers on reserve overstocking.
Factors that influence international capital and trade flows are likely to affect
contemporaneously the entire set of countries of the sample. The size of the cross country
correlation coefficients reported in table VI is indicative of a common element among the
estimates of excess reserve holdings.
[Insert Table VI]
3.1 Principal components analysis
Principal components analysis (PCA) allows to investigate the pattern of the co-movements in
reserve misalignments. Exploiting the potential information redundancy in multivariate data
sets, PCA reduces the dimensionality of the data with minimal loss of information. It
transforms a set of N correlated variables (the Johansen cointegration residuals) into a smaller
subset of M ≤ N uncorrelated variables (principal components) that are orthogonal linear
combinations of the original ones. The first component will have the maximum possible
variance, the second the maximum possible variance among the linear combinations
uncorrelated with the first principal component and so on.
Let N,...,i,yi 1= be a Tx1 vector of cointegration residuals and iiii )yy(x σ−= be the
corresponding standardized residual vector where iy and iσ are the unconditional sample
mean and standard deviation. ix is a column of the TxN matrix, X, of standardized excess
reserve time series. Principal components analysis is based on the eigenvalue eigenvector
decomposition of the (correlation) matrix T/X'X=Σ .
The principal components transformation of X reads as
Γ= XZ (7)
where Z is a TxN matrix of principal components, each column of which, z j (j=1,…, N), is a
Tx1 principal component vector.
The jth principal component
16
z Xj j= γ (7’)
has zero mean and variance λ j . With the latter appropriately normalized it is possible to
measure the fraction of the variance of the original data explained by the corresponding
principal component. Similarly, the sum of the first M normalized eigenvalues indicates how
much variation is explained by the first M principal components.
Consistent estimation of the correlation matrix and of the corresponding principal components
requires that the time series be stationary. This is the case here since cointegration residuals
are stationary by construction.
PCA, implemented on excess reserve holdings, shows that the first principal component may
explain up to 38 percent of the variability of the residuals of the reserve cointegration
relationships in the case of Asia. The first three principal components explain 60 to 85 percent
of the excess reserve variability in the single area and cross area estimates.
[Insert Table VII]
3.2 Fear of floating and mercantilist factors affecting reserve holdings
The remaining issue is whether there are international drivers able to explain the behavior of
the identified common patterns (principal components) among excess reserve holdings. This
section presents the economic rationale and the empirical evidence supporting the hypothesis
that the US policy rate and the US dollar real effective exchange rate are key international
factors driving Central Banks’ decisions to accumulate reserves beyond their optimal level.
Recent global economic integration has offered interesting financing opportunities but has
also increased the exposure of emerging countries to contagion and to inflation pass through.
Several studies suggest that capital flows are driven by common international factors. As
shown by Calvo et al. (1996) and Mody et al. (2001), among others, shifts in US monetary
policy influence emerging markets financial liquidity. A tight US monetary policy makes
17
investment in these countries less attractive, raising debt price. The corresponding increase in
the rate of interest differential results in cross border financial flows.13
The extent to which the local monetary authorities react to changes in the US interest rate
depends, in principle, on the nature of the exchange rate arrangements. Under a pegged
exchange rate regime the reaction would be strong, in order to avoid the insurgence of a risk
premium. Under floating regimes, changes in international interest rates could be
accommodated through exchange rate movements. Frankel (1999), however, found that also
in free floating countries (such as Brazil and Mexico) an increase in the fed fund rate brings
about a more than proportional increase in the domestic interest rate. The latter is due to the
large effect of interest rate differentials on capital outflows and to the required large premium
for devaluation and default risk.
This picture is not exhaustive since the monetary policy framework matters as well. Under an
inflation targeting regime, even in the case of free floating exchange rates, an increase in the
US interest rate may cause a depreciation of the national currency (because of lower capital
inflows or capital reversals towards higher US risk adjusted returns) and the authorities will
be willing to reduce liquidity, trading off economic growth with inflation. This fear of floating
can be faced, alternatively, increasing the foreign reserve holdings above precautionary levels.
In general, a large buffer for future exchange market intervention reduces the degree of
external vulnerability to contagion. International linkages among monetary policies may thus
influence the strategies of Central Banks directly through the interest rate and indirectly via
reserve holding decisions. If, according to the empirical evidence mentioned above, a US
monetary policy tightening has an adverse effect on emerging market financial stability, we
expect a positive relation between the US fed fund effective rate and reserve overstocking.
Purchases of foreign currency during a period of upward pressure on the domestic exchange
rate and rather limited intervention on the downside are consistent with an attempt to avoid a
deterioration of national competitiveness i.e. with a deep-rooted mercantilist desire to
maintain an undervalued exchange rate. This explanation agrees with the suggestion of
Dooley at al. (2003) that emerging countries build up reserves in order to support their
exports. A sensible development strategy might then require a distortion in the real exchange 13 See Arora and Cerisola (2001) and Uribe and Yue (2003). It is also believed that US monetary policy plays a relevant role in triggering financial and banking crises since a rise in industrial country interest rates worsens the conditions for the access of emerging markets to offshore funds.
18
rate in order to channel domestic investment towards export industries and a process of
reserve accumulation, which would appear sub-optimal, is in reality an element of an optimal
investment strategy.
The depreciation of the US real effective exchange rate is used here as a synthetic (global)
measure of domestic foreign exchange pressure due to trade. Thus a depreciation, i.e. a
negative shift, in the US real effective exchange rate has to be associated with an increase in
excess reserve holdings. Emerging market Central Banks buy foreign assets and sell domestic
ones in order to avoid a reduction of domestic competitiveness through an undesired
exchange rate appreciation.14
In this section empirical evidence on the impact of the selected international factors is
provided by regressing the first principal component of the interregional and of the two
regional sets of countries on the US fed fund effective rate and on the US dollar real effective
exchange rate. The following relationship is estimated
PC i REER et t US t US t1 = + + +ϖ φ ϕlog log, , (8)
where PC t1 is the first principal component, USti , is the detrended fed fund effective interest
rate and REERt,US, the real effective exchange rate, is obtained using CPI deflators. The time
series are stationary and the estimation is performed in levels.15.
[Insert table VIII]
Table VIII contains GMM estimates of equation (8) with heteroskedasticity and serial
correlation consistent standard errors. We find evidence of a relevant impact of the above
mentioned factors on excess reserve accumulation decisions. The coefficients are significant
14 Past negative experience may play a relevant role here. Most periods of turbulence over the last two decades started with currency crises in emerging markets with pegged exchange rate regimes and were triggered by a US dollar appreciation (Whitt, 1999). 15 The ADF tests of the USti , and REERt,US time series can be summarized as follows
USti , REERt,US
ADF(c, t, lag) -3.07**(0,0,10) -3,68* (c,t,2) c: constant; t: determinitic trend; *: 5 percent
significance level;**: 1 percent significance level The three principal components are stationary by construction, being linear combinations of stationary variables. This property of the time series is corroborated by ADF test statistics, not reported here for lack of space .
19
and have the expected sign. The adjusted R2 statistics are, moreover, relatively large and
explain up to 45 percent of the total variance of the first principal component of the excess
reserve co-movements of the interregional system.
4 Conclusion
Large-scale accumulation of foreign reserves by emerging market economies has recently
attracted considerable attention. Most rules of thumb point out that reserve demand is
excessive with respect to common adequacy levels. These rules, however, are simplistic by
construction and tend to underestimate actual reserve requirements. This paper proposes an
alternative assessment of reserve adequacy based on long run estimates of the buffer stock
model for ten emerging countries, located in Asia and Latin America, over the 1985-2004
time period. Optimal reserves are the fitted values of cointegration relationships obtained with
the Johansen procedure and the corresponding cointegration residuals quantify excess reserve
holdings. Our results suggest that the current size of excess reserves is probably overstated in
the literature.
Over-accumulation may be attributed to Governments’ “fear of floating” and/or to a
mercantilist rationale. In the first case foreign reserves are stocked to reduce vulnerability to
external shocks in countries with uncertain and pro-cyclical access to global financial
markets. Capacity to draw on reserves smoothes consumption in the presence of external
shocks and reduces vulnerability to creditor runs arising from currency and maturity
mismatches. Mercantilist reserve accumulation is driven by a desire to maintain competitive
exchange rates and, hence, economic growth. (The thesis by Dooley et al. 2003 assumes that a
strategy of deliberately under-valuing the exchange rate to promote exports can deliver long
run benefits.) The relevance of these interpretations is corroborated by our empirical findings.
Cointegration residual co-movements - filtered using principal components analysis - are
investigated to detect the impact of common international factors such as the US fed fund rate
and the US dollar real effective exchange rate. The former represents the US monetary policy
and is an identified driver of international capital flows, while the latter quantifies adverse
external shocks on emerging markets competitiveness.
20
The US interest rate exerts a significant positive effect on excess reserve demand as central
bankers try to reduce their exposure to sudden capital reversals. In the same way, a
depreciation of the US dollar real effective exchange rate brings about an increase in excess
demand as central bankers react to the negative impact on competitiveness of an appreciation
of the domestic currency.
Central bankers' behavior is far from irrational and is explained by idiosyncratic and common
economic factors. Reserves are accumulated above buffer stock precautionary levels in order
to weather financial crises and to foster export led economic growth.
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APPENDIX I
Data description
Time series are monthly and cover the period between January 1985 and December 2004.
- Reserves excluding gold are series l1da quoted in US dollars from the IMF International Financial
Statistics Data Base. International reserves do not include gold because of valuation problems and of
the modest amount of the precious metal in the EME reserve stocks.
- Interest rates are the money market rates in series 60b for Argentina, Brazil, Indonesia, Korea,
Malaysia, Mexico, and Singapore. Treasury bill rate in series 60c was used for the Philippines. The
deposit rate in series 60l was used for Chile and Venezuela. They are all from the IMF International
Financial Statistics Data Base.
- Imports are series 71da (cif quoted in US dollars) from the IMF International Financial Statistics Data
Base.
- M2 is the sum of M1 series 35a and Quasi money 34a. The M2 series are quoted in national currency
and were converted into US dollars using the end of period exchange rate. They are all obtained from
the IMF International Financial Statistics Data Base.
- Real effective exchange rate for the US is USI..RECE - CPI BASED SADJ from the IMF
International Financial Statistics Data Base.
- US short interest rate, federal fund effective interest rate is from the Federal Reserve database.
- Short term external debt series are taken from the IIF data base but for the Singaporean one which is
computed adding item G-H-I taken from the BIS/IMF/World Bank /OECD data base.
24
APPENDIX II Perron and Rodríguez outlier detection test Perron and Rodríguez (2003) propose the following test for multiple outlier detection based on first differences of the data. Estimate the following regression by OLS
[ ]∆y D T D Tt ao t ao t t= − +−δ υ( ) ( ) 1 (II.1) where yt is the time series under examination, Tao is the date at which a single outlier occurs with magnitude δ , D Tao t( ) = 1 if t Tao= (0, otherwise) and D Tao t( ) − =1 1 , if t Tao= +1 (0, otherwise). If the data in levels are trending a constant should be added as a regressor. Compute the t-statistic t Td ao( ) for testing δ = 0 in (II.1). If τ δd
SUPTao aot T= | ( )| , the absolute value of the largest of the t-statistics, is greater than the Perron Rodríguez
threshold values reported in table IV, page 203, the corresponding observation is an outlier and is dropped from the time series. The procedure is then repeated and continues until the test statistic becomes insignificant.
Table A.1 - OUTLIER DETECTION TESTS
Abolute value t- statistics++
Corresponding period
Absolute value t- statistics++
Corresponding period
Reserves Domestic Short Term Interest Rate Argentina C
5.78 1989m06
Argentina C
4.19 3.92 5.04 4.01 7.75 7.57
1989m05 1989m12 1990m01 1990m02 1990m03 1990m04
Brazil
4.49 1990m03 Brazil C
Chile
4.50 8.50 4.11
1985m11 1985m12 1988m01
Chile C
5.56 3.76 3.91
1992m03 1993m12 2001m03
Mexico C
5.70 4.38 6.77 6.60
1990m02 1990m03 1995m01 1995m02
Mexico C
4.00 1995m03
Venezuela Venezuela 3.75 2002m03 Indonesia C
5.00 4.35
1987m07 1990m05
Indonesia
4.65 6.17 4.29 4.16 5.12
1997m07 1997m08 2002m02 2004m09 2004m10
Korea C
4.31 5.29 5.88 3.71
1986m01 1987m11 1987m12 1997m12
Korea C
3.87 1994m08
Malaysia C
5.75 1994m01 Malaysia
4.10 6.51 5.87 3.86 5.22
1986m10 1986m11 1987m03 1997m06 1997m07
Philippines 6.15 3.96
1989m12 1991m01
Philippines C
4.76 4.20 4.84
1986m02 1987m02 2000m11
Singapore 4.25 1998m01 Singapore C
7.05 4.63
1995m05 1999m04
25
Reserve Volatility Value of Imports Argentina Argentina 4.48 1990m02 Brazil 4.13
5.55 5.70 3.74
1995 m07 1998 m08 1998m10 2002m06
Brazil 4.36 6.05
1996m12 1997m01
Chile
3.96 3.84 4.66 4.24
1985m12 1999m07 2002m04 2002m05
Chile 3.92 3.75
1985m09 2002m01
Mexico
3.75 3.77 4.02
1987m05 1993m11 1994m05
Mexico C
3.74 1995m05
Venezuela
4.38 4.47 4.05 4.09 5.35 5.34
1990m12 1991m01 1991m12 1992m01 1997m08 1997m09
Venezuela
4.18 1994m02
Indonesia 4.38 2000m08 Indonesia C
3.86 4.53
1986m12 1990m04
Korea C
4.77 3.88 3.99 3.94
1997m11 1997m12 2003m05 2003m06
Korea C
4.00 5.19 4.46
1985m12 1986m12 1987m01
Malaysia
3.81 4.23 5.31 4.93
1990m10 1990m11 1992m08 1992m09
Malaysia
3.79 1997m02
Philippines 4.74 3.76 4.20 3.95
1997m07 1997m08 2000m03 2000m04
Philippines
3.97 1998m05
Singapore C
4.97 1995m03 Singapore C
4.26 4.99 4.06
1997m02 1998m04 1998m05
Notes. ++ : Perron and Rodríguez (2003) critical values with constant: 4.20(1%), 3.75 (5%), 3.56 (10%); with constant and trend: 4.19(1%), 3.74 (5%), 3.55 (10%); C: constant term in equation (II.1).
APPENDIX III Analysis of the conditional heteroskedasticity of the international reserves first difference time series
Table A.II - CONDITIONAL HETEROSKEDASTICITY TESTS
LB Q(j)
Argentina
Brazil
Chile
Mexico
Venezuela
Indonesia
Korea
Malaysia
Philippines
Singapore
3 63.91**
0.34
5.61*
20.63**
6.39*
33.11**
27.95**
36.07**
5.11*
52.08**
5 71.50**
6.41
8.68*
33.23**
8.12*
39.30**
35.00**
36.73**
18.05**
91.76**
7 83.22**
22.53**
11.19*
40.54**
8.97
39.76**
43.91**
37.86**
19.51**
135.56**
Notes. LB Q(j): Ljung Box Q-statistic for the null of no autocorrelation up to order j; *: rejected at the 5% level; **: rejected at the 1% level. The reserve first differences have been filtered, if necessary, in order to eliminate serial correlation. The number of degrees of freedom of the χ2 distribution has been correspondingly adjusted.
Table I - RESERVE ADEQUACY MEASURES - RULES OF THUMB
Reserves/ Imports
(months) Guidotti Rule
Reserves /STDEB (percentage)
Guidotti Rule Augmented Reserves/(STDEB*(1+3%))
(percentage)
Capital Flow approach Reserves/M2 (percentage)
Guidotti-Greenspan Rule Reserves/ (STDEB+M2∗ ∗δ ρ )
(percentage)
1985 1995 2004 1985 1995 2004 1985 1995 2004 1985 1995 2004 1995 low δ highδ
2004 low δ high δ
Argentina 10 9 10 42 74 105 41 72 102 NA 27 41 88 75 Brazil 9 11 10 83 91 232 81 88 225 NA 25 30 194 167 Chile 10 11 8 55 265 244 53 258 237 41 55 43 242 222 230 218 Mexico 3 3 4 48 38 186 46 37 181 14 23 33 37 35 167 151 Venezuela 15 6 13 89 110 331 86 107 321 42 53 79 99 90 258 212 Indonesia 6 4 8 91 43 205 88 42 199 24 14 31 37 33 147 114 Korea 1 3 11 16 60 332 15 58 322 9 16 37 55 51 301 276 Malaysia 5 4 8 NA 309 386 NA 300 375 24 32 53 228 181 319 272 Philippines 1 3 4 7 104 144 7 101 140 7 17 27 90 80 127 113 Singapore 6 7 8 NA 38 156 NA 37 151 96 95 89 38 37 152 147
Notes. δ is the percentage of broad money (M2) assumed by De Beaufort Wijnholds and Kapteyn (2001) to represent the amount of capital outflows stemming from residents. It ranges from 5 to 10 percent in the case of countries with independently floating regimes and from 10 to 20 percent in the case of countries with managed floating or fixed exchange rate regimes. ρ is the country risk score index of the Economist Intelligence Unit. STDEB indicates the short term external debt in millions of US dollars.
27
Table II – UNIT ROOT TESTS
NP* NP Reserves Domestic Short Term Interest Rate
Argentina C, t, lag 0
-1.35 I(1) Argentina C, t, lag 9
-1.81 I(1)
Brazil C, lag 1
-0.22 I(1) Brazil C, lag 9
-1.15 I(1)
Chile C, lag 1
1.00 I(1) Chile C, t, lag 11
-1.40 I(1)
Mexico C, t, lag 0
-2.69 I(1) Mexico C, t, lag 13
-2.72 I(1)
Venezuela C, t, lag 1
-1.61 I(1) Venezuela C, lag 0
-0.56 I(1)
Indonesia C, t, lag 2
-1.65 I(1) Indonesia C, lag 1
-1.77 I(1)
Korea C, t, lag 2
-1.81 I(1) Korea C, lag 1
-0.96 I(1)
Malaysia C, t, lag 2
-1.73 I(1) Malaysia C, lag 7
-1.53 I(1)
Philippines C, lag 14
0.86 I(1) Philippines C, t, lag 3
-2.60 I(1)
Singapore C, lag 12
0.79 I(1) Singapore C, t, lag 2
-2.64 I(1)
Reserve Volatility Value of Imports
Argentina C, lag 1
-1.19 I(1) Argentina C, lag 12
-0.82 I(1)
Brazil C, lag 1
-0.23 I(1) Brazil C, lag 13
0.77 I(1)
Chile C, lag 1
-0.30 I(1) Chile C, lag 14
0.47 I(1)
Mexico C, lag 1
-1.13 I(1) Mexico C, t, lag 12
-2.34 I(1)
Venezuela C, lag 5
-0.03 I(1) Venezuela C, lag 1
-3.58 I(0)
Indonesia C, lag 0
0.52 I(1) Indonesia C, lag 2
0.42 I(1)
Korea C, lag 1
0.07 I(1) Korea C, t, lag 12
-2.49 I(1)
Malaysia C, lag 2
-0.24 I(1) Malaysia C, lag 13
0.28 I(1)
Philippines C, lag 1
-0.40 I(1) Philippines C, t, lag 13
-1.47 I(1)
Singapore C, t, lag 0
-2.07
I(1) Singapore C, t, lag 14
-1.97 I(1)
Notes. *: Ng Perron (2001) unit root test. The GLS-detrended autoregressive spectral density estimator of the frequency zero spectrum uses the modified AIC to select the number of lags. Critical values with constant, C, and trend, t: -3.42(1%),-2.91(5%); with constant without trend: -2.58(1%),-1.98(5%).
28
Table III - VOLATILITY OF RESERVES
CONDITIONAL VARIANCE PARAMETERIZATION σ ω α βσ γt t t t tu S u2
12
12
1 12= + + +− − − − (5)
ω α β γ LLF Standardized Residuals W Sk. Kurt. JB Argentina 15858.5
(10651.8) 0.19 (0.06)
0.81 (0.04)
-1947.14 0.166 4.101 13.22 [0.001]
0.007 [0.932]
Brazil 22515.9 (29745.5)
0.15 (0.08)
0.88 (0.04)
-2131.25 -0.471 7.759 234.38 [0.000]
0.407 [0.523]
Chile 1609.8 (1579.0)
0.05 (0.04)
0.94 (0.05)
-1700.80 0.428 5.532 71.18 [0.000]
0.307 [0.579]
Mexico* 135721.8 (116109.3)
0.10 (0.07)
0.87 (0.07)
-2046.56 0.184 5.605 69.20 [0.000]
0.265 [0.606]
Venezuela 3267.4 (2152.0)
0.12 (0.05)
0.93 (0.02)
-0.11 (0.06)
-1833.59 0.164 3.971 10.42 [0.005]
0.008 [0.930]
Indonesia 5058.6
(3448.3) 0.07 (0.04)
0.93 (0.04)
-1836.16 0.327 4.994 43.87 [0.000]
0.067 [0.796]
Korea 10640.3 (7788.8)
0.14 (0.04)
0.88 (0.03)
-1992.78 -0.303 4.983 42.65 [0.000]
0.991 [0.319]
Malaysia° 6747.6 (4755.4)
0.20 (0.08)
0.92 (0.02)
-0.21 (0.08)
-1907.20 -0.475 9.149 385.59 [0.000]
0.560 [0.454]
Philippines+ 1270.9 (1242.2)
0.06 (0.03)
0.94 (0.03)
-1718.19 0.185 4.545 25.04 [0.000]
0.071 [0.790]
Singapore 572.4 (2046.6)
0.18 (0.07)
0.86 (0.05)
-1923.75 0.209 3.794 7.99 [0.018]
2.561 [0.109]
Notes. Sk.: Skewness; Kurt.: Kurtosis; JB: Jarque-Bera test statistic; W: Wald χ2 test of the null hypothesis that 1-α-β=0 in the GARCH(1,1) estimates and of the null hypothesis 1-(α+0.5γ)-β=0 in the TGARCH(1,1) ones; *: the conditional distribution of the residuals is modeled as a t-distribution with degree of freedom 2.91 (standard error 0.7674); °: the conditional distribution of the residuals is modeled as a GED with tail parameter 0.8641 (standard error 0.1141); + : the conditional distribution of the residuals is modeled as a GED with tail parameter 1.1471 (standard error 0.1591). In the remaining estimates the standard errors of the coefficients are robust to heteroskedasticity.
29
Table IV - JOHANSEN COINTEGRATION TESTS
TRACE TEST STATISTICS List of variables in the VAR: Log (Reserves) = log Rt ; Log (Volatility of Reserves)= logσ t ; Log (Imports) =log M t ;
Log (Domestic Interest Rate)= log rt Hypothesized No. of
Cointegration Relationships
Trace Statitics 0.05 percent Critical Value
Deterministic Trend Assumption
No. of Lags in VAR
Argentina None at most 1 at most 2 at most 3
67.29* 30.57 15.35 3.05
62.99 42.44 25.32 12.25
Restricted linear
deterministic trend
4
Brazil None at most 1 at most 2 at most 3
57.34* 27.52 11.62 2.48
53.12 34.91 19.96 9.24
Restricted constant
2
Chile None at most 1 at most 2 at most 3
57.91* 28.65 8.75 2.79
53.12 34.91 19.96 9.24
Restricted constant
6
Mexico None at most 1 at most 2 at most 3
53.41* 26.27 12.64 3.25
47.21 29.68 15.41 3.76
Linear deterministic Trend
15
Venezuela None at most 1 at most 2
34.95* 17.79 8.09
34.91 19.96 9.24
Restricted constant
2
Indonesia None at most 1 at most 2 at most 3
56.32* 33.20 18.79 6.89
53.12 34.91 19.96 9.24
Restricted constant
2
Korea None at most 1 at most 2 at most 3
55.62* 26.41 12.03 2.64
53.12 34.91 19.96 9.24
Restricted constant
2
Malaysia None at most 1 at most 2 at most 3
58.81* 25.81 11.53 3.19
53.12 34.91 19.96 9.24
Restricted constant
5
Philippines None at most 1 at most 2 at most 3
50.43* 26.46 11.18 1.96
47.21 29.68 15.41 3.76
Linear deterministic
trend
3
Singapore None at most 1 at most 2 at most 3
63.34* 31.44 13.31 3.58
62.99 42.44 25.32 12.25
Restricted linear
deterministic trend
5
Notes. *: denotes rejection of the null hypothesis at the 5 percent level .
30
Table V - JOHANSEN COINTEGRATION EQUATION ESTIMATES
log log log logR t M rt t t t t t− − − − − =ρ ρ β σ β β ε0 1 2 3 (6)
Tests on cointegration residuals
ρ0 ρ1 β1 β2 β3
E.C.
LR Test* Normality
Test 1° eq.**
Cond. Het. Q-stat.
Joint VAR Serial Corr.
LM Test°
Argentina 58.82 -0.023 (0.01)
--
-6.73 (1.38)
-2.56 (0.44)
-0.007 (0.002)
H0 1 0:β = 0.0086 [0.92]
4.76 [0.09]
4.9 (lag 1) [0.03]
14.1 (lag 3) [0.00]
25.9 (lag 1) [0.05] 31.3 (lag 3)
[0.01]
Brazil 7.92 (1.94) -- -- -2.11
(0.21) -0.21 (0.05)
-0.03 (0.01)
H0 1 0:β = 0.0058 [0.80]
32.09 [0.00]
0.3 (lag 1) [0.60]
1.1 (lag 3) [0.78]
12.1 (lag 1) [0.73] 16.8 (lag 3) [0.39]
Chile -7.85 (3.25) -- 2.02
(0.77) -1.79 (0.37)
-0.37 (0.20)
-0.02 (0.004) 2.32
[0.31]
2.1 (lag 1) [0.14] 11.1 (lag 3) [0.03]
20.2 (lag 1) [0.21] 24.7 (lag 3)
[0.07]
Mexico
-14.9
--
1.25 (0.27)
-0.63 (0.16)
0.45 (0.17)
-0.06 (0.02)
2.27 [0.32]
6.2 (lag 1) [0.01] 6.3 (lag 3)
[0.09]
5.7 (lag 1) [0.46] 18.3 (lag 3) [0.30]
Venezuela -4.09 (0.99)
--
-0.80 (0.19) -- 0.24
(0.11) -0.027 (0.018) 0.42
[0.80]
6.8 (lag 1) [0.01]
14.5 (lag 3) [0.00]
6.7 (lag 1) [0.67] 7.1 (lag 3) [0.62]
Indonesia
3.09 (1.24)
-- -0.29 (0.13)
-1.16 (0.19)
-- -0.04 (0.009)
H0 3 0:β = 0.044 [0.833]
24.66 [0.00]
0.8 (lag 1) [0.37]
16.9 (lag 3) [0.00]
18.6 (lag 1) [0.28]
23.6 (lag 3) [0.10]
Korea -3.59 (2.74) -- -- -1.09
(0.26) 1.23
(0.29) -0.02
(0.005)
H0 1 0:β = 1.21
[0.270]
99.15 [0.00]
0.1 (lag 1) [0.70]
0.2 (lag 3) [0.96]
18.3 (lag 1) [0.30]
17.2 (lag 3) [0.37]
Malaysia -2.68 (0.05) -- -0.69
(0.11) -0.41 (0.10)
0.55 (0.09)
0.02 (0.014) 11.49
[0.00]
0.9 (lag 1) [0.34]
2.2 (lag 3) [0.52]
11.4 (lag 1) [0.78]
21.7 (lag 3) [0.15]
Philippines 0.56 -- -2.30 (0.32) -- 1.83
(0.27) 0.02
(0.018)
H0 2 0:β = 0.07
[0.78]
24.67 [0.00]
6.6 (lag 1) [0.01]
19.4 (lag 3) [0.00]
12.6 (lag 1) [0.70]
27.1 (lag 3) [0.04]
Singapore -0.56 0.005 (0.001)
-0.23 (0.07)
-1.18 (0.10)
0.40 (0.07)
-0.02 (0.009) 1.83
[0.40]
4.4 (lag 1) [0.04]
5.3 (lag 3) [0.14]
25.8 (lag 1) [0.06]
12.2 (lag 3) [0.73]
Notes. E.C.: error correction coefficient; *: Likelihood Ratio test of the cointegration vector restrictions; **: Jarque-Bera normality test; °: Johansen (1995) VECM residuals autocorrelation LM test; standard errors are in parentheses and probability values in square brackets.
31
Table VI - CORRELATION MATRICES OF THE JOHANSEN COINT. RESIDUALS ASIA
Indonesia Korea Philippines Malaysia Singapore
Indonesia 1.00 -0.62 -0.27 0.03 -0.04 Korea -0.61 1.00 0.09 0.22 0.32
Philippines -0.27 0.08 1.00 0.11 -0.10 Malaysia 0.03 0.22 0.11 1.00 0.49 Singapore -0.04 0.32 -0.10 0.49 1.00
LATIN AMERICA
Argentina Brazil Chile Mexico Venezuela
Argentina 1.00 0.35 0.12 0.12 0.18 Brazil 0.35 1.00 0.21 0.01 0.27 Chile 0.12 0.21 1.00 0.03 0.37
Mexico 0.12 0.01 0.03 1.00 -0.01 Venezuela 0.18 0.27 0.37 -0.01 1.00
CROSS AREA ASIA-LATIN AMERICA
Argentina Brazil Chile Mexico Venezuela
Indonesia 0.23 0.56 0.23 -0.12 0.20 Korea -0.21 -0.37 0.09 -0.19 -0.09
Philippines 0.12 0.27 0.18 -0.35 0.39 Malaysia 0.14 -0.05 -0.35 0.01 0.22 Singapore -0.12 0.07 0.34 -0.17 0.31
Table VII - PRINCIPAL COMPONENTS ANALYSIS
ASIA AND LATIN AMERICA
Comp 1 Comp 2 Comp 3
Eigenvalue 2.51 2.15 1.44 Cumulative prop. of variance explained 0.25 0.47 0.61
LATIN AMERICA Comp 1 Comp 2 Comp 3
Eigenvalue 1.76 1.06 0.94 Cumulative prop.of variance explained 0.35 0.56 0.75
ASIA
Comp 1 Comp 2 Comp 3
Eigenvalue 1.89 1.39 0.97 Cumulative prop. of variance explained 0.38 0.66 0.85
32
Table VIII - DETERMINANTS OF THE EXCESS DEMAND OF RESERVES
PC i REER et t US t US t1 = + + +ϖ φ ϕlog log, , (8)
Dependent Variable ϖ φ ϕ R 2
PC1 Interregional system 42.83 (5.78)
0.98 (0.27)
-9.49 (0.28) 0.45
PC1 Latin America 20.76 (5.33)
0.69 (0.27)
-4.60 (1.17) 0.17
PC1 Asia 14.86 (5.88)
1.30 (0.36)
-3.29 (1.31) 0.21
Notes. Autocorrelation and heteroskedasticity consistent standard errors in parentheses.
33
Figure I. Reserve overstocking - Latin America Normalized four quarter moving averages of the cointegration residuals (y axis in standard deviations)
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
95 96 97 98 99 00 01 02 03 04
EXCESS_ID
-2
-1
0
1
2
3
4
95 96 97 98 99 00 01 02 03 04
EXCESS_KO
-2
-1
0
1
2
3
95 96 97 98 99 00 01 02 03 04
EXCESS_MY
-3
-2
-1
0
1
2
95 96 97 98 99 00 01 02 03 04
EXCESS_PH
-3
-2
-1
0
1
2
3
95 96 97 98 99 00 01 02 03 04
EXCESS_SP
-2
-1
0
1
2
3
95 96 97 98 99 00 01 02 03 04
EXCESS_AR
-3
-2
-1
0
1
2
95 96 97 98 99 00 01 02 03 04
EXCESS_BR
-2
-1
0
1
2
3
95 96 97 98 99 00 01 02 03 04
EXCESS_CL
-2
-1
0
1
2
3
95 96 97 98 99 00 01 02 03 04
EXCESS_MX
-3
-2
-1
0
1
2
95 96 97 98 99 00 01 02 03 04
EXCESS_VE
34
Figure II. Reserve overstocking - Asia Normalized four quarter moving averages of the cointegration residuals (y axis in standard deviations)
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
95 96 97 98 99 00 01 02 03 04
EXCESS_ID
-2
-1
0
1
2
3
4
95 96 97 98 99 00 01 02 03 04
EXCESS_KO
-2
-1
0
1
2
3
95 96 97 98 99 00 01 02 03 04
EXCESS_MY
-3
-2
-1
0
1
2
95 96 97 98 99 00 01 02 03 04
EXCESS_PH
-3
-2
-1
0
1
2
3
95 96 97 98 99 00 01 02 03 04
EXCESS_SP
35
PUBBLICAZIONI DEL DIPARTIMENTO
COLLANA STUDI E DISCUSSIONI – ULTIMI NUMERI USCITI
129 Alessandro PETRETTO The Impact of Vertical Fiscal Competition on the Tax Structure of a Federation with a System of Equalisation Transfers, September 2002.
130 Valentina CIVANO Modelli di Supporto Decisionale: Sostenibilità e Conflitti Ambientali, October 2002.
131 Alberto ZANNI Le Dimensioni della Moneta. Alcune Interpretazioni e una Proposta, Dicembre 2002.
132 Andrea MANGANI, Brand Stretching and Vertical Product Differentiation, Dicembre 2002.
133 Giulio CIFARELLI, Giovanna PALADINO, The Impact of the Argentine Default on Volatility Co-Movements in Emerging Bond Markets, March 2003.
134 Vinicio GUIDI, Modelli di Equilibrio Economico Generale e Beni Infiniti: una Rassegna, Marzo 2003.
135 Lisa GRAZZINI, Different Roles for Taxation in Imperfectly Competitive Economies, April 2003.
136 Anna SANZ DE GALDEANO, Daniela VURI, Does Parental Divorce Affect Adolescents' Cognitive Development ? Evidence from Panel Data, January 2004.
137 Giulio CIFARELLI, Yes, Implied Volatilities Are Not Informationally Efficient. An Empirical Estimate Using Optionson Interest Rate Futures Contracts, February 2004.
138 Lisa GRAZZINI, Alessandro PETRETTO, Federalism versus Centralism: Will Capital Taxes Be Too High or Too Low?, March 2004.
139 Annalisa LUPORINI, Relative Performance Evaluation in a Multi-Plant Firm, December 2004
140 Mario BIGGERI, Growth with Development: Informal Sector and Human Development in Low-income Sub-Saharan Economies December 2004
141 Mario BIGGERI, The Capability Approach and Children Well-Being, December 2004
142 Giulio CIFARELLI, Giovanna PALADINO, The International Reserves Glut:Is It for Real?, January 2006
36
COLLANA MATERIALI DIDATTICI - NUMERI USCITI 1. Vinicio GUIDI, Consumo, produzione, equilibrio generale, Ottobre 1984. 2. Alessandro PETRETTO, Elementi di teoria generale della finanza pubblica,
Gennaio 1985. 3. Giorgio LOMBARDO, Aspetti teorici ed implicazioni dell'innovazione
finanziaria, Gennaio 1985. 4. Piero INNOCENTI, L'industria nell'area fiorentina, Ottobre 1985. 5. Alberto ZANNI, Alcuni strumenti matematici preliminari al primo corso di
economia politica. 6. MODELLI A EQUAZIONI SIMULTANEE, Appunti delle lezioni del Prof.
Pietro Balestra raccolti da Massimo Canalicchio, Marco Fizialetti, Silvia Giovannini, Francisco Incerpi, Maria Lo Monaco, Andrea Manuelli, Andrea Pani, Riccardo Perugi, Costantino Romagnoli, Paolo Zacchia
7. Gary PHILLIPS, Simultaneous equation estimation, Ottobre 1987. 8. Alberto ZANNI, Patrimonio dei singoli, patrimonio nazionale e fondo delle
anticipazioni capitalistiche, Febbraio 1988. 9. Alberto ZANNI, Bilancia dei pagamenti, multilateralismo ed equilibrio
intervalutario di Cournot-Pareto, Dicembre 1988. 10. Maria TINACCI MOSSELLO, Argomenti di geografia economica,
Dicembre 1988. 11. Piero TANI e Donatella BIOZZI, Schemi e grafici dalle lezioni del corso di
Economia politica I a.a. 1988-89. 12. Marco ELLER VAINICHER, Distribuzione del reddito, crescita e moneta
nella posizione dei governatori della Banca d'Italia durante il quarantennio repubblicano, Gennaio 1989.
13. Roberta FERRONATO, Potere, organizzazione e politica economica, Aprile 1989.
14. Franco VOLPI, Teoria e politica dello sviluppo economico - parte prima - Introduzione al sottosviluppo, Aprile 1989.
15. Paolo ZACCHIA, Esercizi di Microeconomia, Maggio 1989. 16. Piero TANI, Esercizi di Microeconomia I, Gennaio 1991. 17 Anna PETTINI, Elementi di microeconomia per economia pubblica,
Novembre 1994. 18 Piero TANI, Schemi e grafici alle lezioni del corso di Istituzioni di Economia
I, a.a. 1994/95, Novembre 1994. 19 Piero TANI, Schemi e grafici dalle lezioni del corso di Istituzioni di
Economia I, a.a. 1996/97, Marzo 1997.
37
COLLANA CONFERENZE - NUMERI USCITI
1. Michael BACHARACH, Time and Decision - Notes for Two Lectures, Firenze, 7-8 January 1987, (Papers on Game Theory, n.4), May 1988.
2. Brian J. LOASBY, Knowledge and Organisation - Marshall's Theory of Economic Progress, Firenze, 8-9 ottobre 1987, (Marshallian Studies, n.3) July 1988.
3. Jean-François MERTENS, Equilibrium and Rationality: Context and History-Dependence, (Papers on Game Theory, n.7), January 1990.
4. Maria Teresa COSTA CAMPI, Sviluppo, crisi e ripresa dell'economia spagnola (1960-1988), Marzo 1990.