Estimation of De Facto Flexibility
Parameter and Basket Weights
in Evolving
Exchange Rate
Regimes
Jeffrey Frankel (HKS, Harvard University )
& Dan Xie (Peterson Inst. for Internatl. Econ.)
Annual Meeting of the American Economics Association, Atlanta.
Session on International Financial Markets, Ken West presiding;
January 4, 2010, 2:30; Atlanta Marriott Marquis, Marquis Ballroom – Salon C
1
As is by now well-known, the exchange rate regimes
that countries follow in practice (de facto) often depart
from the regimes that they announce officially (de jure).
• Many countries that say they float in fact intervene
heavily in the foreign exchange market. [1]
• Many countries that say they fix in fact devalue
when trouble arises.[2]
• Many countries that say they target a basket of
major currencies in fact fiddle with the weights.[3]
[1] “Fear of floating:” Calvo & Reinhart (2001, 2002); Reinhart (2000).
[2] “The mirage of fixed exchange rates:”
Obstfeld & Rogoff (1995). Klein & Marion (1997).
[3] Parameters kept secret: Frankel, Schmukler & Servén (2000).
2
Economists have offered de facto
classifications, placing countries
into the “true” categories
• Important examples include Ghosh, Gulde & Wolf
(2000), Reinhart & Rogoff (2004), Shambaugh
(2004a), and more to be cited.
• Tavlas, Dellas & Stockman (2008) survey the
literature.
• Unfortunately, these classification schemes
disagree with each other as much as they disagree
with the de jure classification! [1]
• => Something must be wrong.
[1] Bénassy-Quéré, et al (Table 5, 2004);
Frankel
(Table 1, 2004); and Shambaugh (2004b).3
Correlations Among Regime
Classification Schemes
IMF
GGW
LY-S
R-R
IMF
1.00
GGW
LY-S
R-R
(100.0)
0.60
1.00
(55.1)
(100.0)
0.28
0.13
1.00
(41.0)
(35.3)
(100.0)
0.33
(55.1)
0.34
0.41
1.00
(35.2)
(45.3)
(100.0)
(Frequency of outright coincidence, in %, given in parenthesis.)
GGW =Ghosh, Gulde & Wolf. LY-S = Levy-Yeyati & Sturzenegger. R-R = Reinhart & Rogoff
Sample: 47 countries.
From Frankel (2004).
4
Several things are wrong.
1) Attempts to infer statistically a country’s
flexibility from the variability of its
exchange rate alone ignore that some
countries experience greater shocks
than others.
•
That problem can be addressed by comparing
exchange rate variability to foreign exchange
reserve variability,
Calvo & Reinhart (2002); Levy-Yeyati & Sturzenegger
(2003, 05).
5
First approach to estimate de facto regimes:
estimate degree of flexibility,
typically presuming, e.g., anchor currency
•
•
=$
Calvo & Reinhart (2002):
Variability of Exchange Rate (E)
vs. Variability of Reserves.
Levy-Yeyati & Sturzenegger (2005):
cluster analysis based on Variability
of E & Δ E, and of Δ Reserves
6
This 1st approach can be phrased in
terms of Exchange Market Pressure:
– Define
EMP = Δ value of currency + Δ reserves.
– EMP represents shocks in demand for the
currency.
– Flexibility can be estimated
as the propensity of the central bank to let shocks
show up in the price of the currency (floating) ,
vs. the quantity of the currency (fixed),
or in between (intermediate exchange rate regime). 7
Several things are wrong, continued.
2) Those papers impose the choice of the
major currency around which the country in
question defines its value (often the $).
• It would be better to estimate endogenously
whether the anchor currency is the $, the €, some
other currency, or some basket of currencies.
• That problem has been addressed by a 2nd branch.
8
Second approach in the de facto regime
literature estimates implicit basket weights:
Regress Δvalue of local currency against
Δ values of major currencies.
•
First examples:
Frankel (1993) and Frankel & Wei (1994, 95).
•
More:
Bénassy-Quéré (1999), Ohno (1999), Frankel, Schmukler, Servén
& Fajnzylber (2001), Bénassy-Quéré, Coeuré, & Mignon (2004)….
•
Example of China, post 7/05:
–
–
Eichengreen (2006) , Shah, Zeileis, & Patnaik (2005), Yamazaki (2006),
Ogawa (2006), Frankel-Wei (2006, 2007), Frankel (2009)
Findings:
•
•
•
RMB still pegged in 2005-06, with 95% weight on $.
Moved away from $ (weight on €) in 2007-08
Returned to approximate $ peg in mid 2008.
9
• Some currencies have basket anchors, often
with some flexibility that can be captured
either by a band or by leaning-against-thewind intervention.
• Most basket peggers keep the weights secret.
They want to preserve a degree of freedom
from prying eyes, whether to pursue
– a lower degree of de facto flexibility, as China,
– or a higher degree, as with most others.
10
Implicit basket weights method -regress Δvalue of local currency against
Δ values of major currencies -- continued.
• Null Hypotheses: Close fit => a peg.
• Coefficient of 1 on $ => $ peg.
• Or significant weights on other currencies
=> basket peg.
•
But if the test rejects tight basket peg,
what is the Alternative Hypothesis?
11
Several things are wrong, continued.
3) The 2nd approach (inferring the anchor
currency or basket) does not allow for
flexibility around that anchor.
• Inferring de facto weights and inferring de
facto flexibility are equally important,
• whereas most authors have hitherto done
only one or the other.
12
The synthesis technique
• A synthesis of the two approaches for statistically
estimating de facto exchange rate regimes:
(1) the technique that we have used in the past to
estimate implicit de facto weights
when the hypothesis is a basket peg with little flexibility. +
(2) the technique used by others to estimate de facto
exchange rate flexibility when the hypothesis is an anchor to
the $, but with variation around that anchor.
• A majority of currencies today follow
– variants of Band-Basket-Crawl or a managed float.
• => We need a technique that can cover both
dimensions: inferring weights and inferring flexibility.
13
Several things are wrong, continued.
4) All these approaches, even the synthesis technique
are plagued by the problem that many countries
frequently change regimes or (for those with
intermediate regimes) change parameters.
• E.g., Chile changed parameters 18 times in 18 years (1980s-90s)
• Year-by-year estimation won’t work,
because parameter changes come at irregular intervals.
• Chou test won’t work,
because one does not usually know the candidate dates.
• Solution:
Apply Bai-Perron econometric technique
for endogenous estimation of structural break point dates.
14
Statistical estimation of de facto exchange rate regimes
Estimation of implicit weights
in basket peg: Frankel (1993),
Frankel & Wei (1993, 94, 95);
Ohno (1999), F, Schmukler & Servén
(2000), Bénassy-Quéré (1999, 2006)…
Estimation of degree of
flexibility in managed float:
Calvo & Reinhart (2002);
Levi-Yeyati &
Sturzenegger (2003)…;
also Reinhart & Rogoff …
Application to RMB:
Eichengreen (06), Ogawa (06), Frankel&Wei (07)
Synthesis: “Estimation of De Facto Exchange Rate
Regimes: Synthesis of the Techniques for Inferring
Flexibility and Basket Weights” F & Wei (IMF SP 2008)
Application to RMB: Frankel (2009)
Econometric estimation of structural
break points: Bai & Perron (1998, 2003)
Allow for parameter variation: “Estimation of De Facto Flexibility Parameter
and Basket Weights in Evolving Exchange Rate Regimes” F & Xie (2010) 15
The technique that estimates basket weights
• Assuming the value of the home currency is determined by
a currency basket, how does one uncover the currency
composition & weights? This is a problem to which OLS is
unusually well suited.
We regress changes in the log of H, the value of the home
currency, against changes in the log values of the candidate
currencies.
• Algebraically, if the value of the home currency H is pegged
to the values of currencies X1, X2, … & Xn, with weights
equal to w1, w2, … & wn, then
Δ logH(t) =c+ ∑ w(j) [Δ logX(j)]
16
(1)
Δ log Ht
= c + ∑ w(j) [Δ logX(j)t ]
= c + β(1) Δ log $ t + β(2) Δ log ¥t
+ β(3) Δ log €t + α Δ log £t
•If the exchange rate is truly governed by a strict basket peg,
then we should recover the true weights, w(j), precisely;
and the equation should have a perfect fit.
17
The question of the numeraire
• Methodology question: how to define “value” of each currency.[1]
• In a true basket peg, the choice of numeraire currency is
immaterial; we estimate the weights accurately regardless. [2]
• In practice, few countries take their basket pegs literally enough
to produce such a tight fit. One must then think about nonbasket factors in the regression (EMP, the trend term, error term):
Are they better measured in terms of one numeraire or another?
• We choose as numeraire the SDR.
• F&Wei checked how much difference numeraire choice makes.
– by trying the Swiss franc as a robustness check
– and in Monte Carlo studies
[1] Frankel(1993) used purchasing power over a consumer basket of domestic goods as numeraire; Frankel-Wei
(1995) used the SDR; Frankel-Wei (1994, 06), Ohno (1999), and Eichengreen (2006) used the Swiss franc; BénassyQuéré (1999), the $; Frankel, Schmukler and Luis Servén (2000), a GDP-weighted basket of 5 major currencies; and
Yamazaki (2006), the Canadian $.
18
[2] assuming weights add to1, and no error term, constant term, or other non-currency variable.
Distillation of technique to infer flexibility
• When a shock increases international demand for korona,
do the authorities allow it to show up as an appreciation,
or as a rise in reserves?
• We frame the issue in terms of Exchange Market Pressure
(EMP), defined as % increase in the value of the currency
plus increase in reserves (as share of monetary base).
• EMP variable appears on the RHS of the equation.
The % rise in the value of the currency appears on the left.
– A coefficient of 0 on EMP signifies a fixed E
(no changes in the value of the currency),
– a coefficient of 1 signifies a freely floating rate
(no changes in reserves) and
– a coefficient somewhere in between indicates
a correspondingly flexible/stable intermediate regime.
19
Synthesis equation
Δ logH(t) = c + ∑ w(j) Δ[logX(j, t)]
+ ß {Δ EMP(t)} + u(t)
(2)
where
Δ EMP(t) ≡ Δ[logH(t)] + [ΔRes(t) / MB(t)].
We impose ∑ w(j) = 1, implemented by treating £ as the last currency.
20
Synthesis equation
Δ log H t
= c + ∑w(j) [Δ logXt] + δ { ΔEMPt } + ut
(3’)
= c + w(1) Δ log $ t + w (2) Δ log €t
+ w (3) Δ log ¥t + w (4) Δ log £t +
+ δ { Δ EMP t } + u t .
21
Now we introduce Bai-Perron technique
for endogenous estimation
of m possible structural break points
k
 log H t  ci   wi , j  log X i , j ,t  i  EMPt  ut
j 1
(6)
t  Ti1  1,..., Ti ; T0  0; Tm1  T ; i  1,..., m  1
22
Illustration using 5 currencies
• These are 5 emerging market currencies of
interest all of which now make available their
data on reserves on a weekly basis
(which is necessary to get good estimates, if structural
changes happen as often as yearly)
• Mexico (monetary base is also available weekly)
• Chile, Russia, Thailand, India
(although reserves available weekly, denominator must
be interpolated from monthly monetary base data)
23
Overview of findings
• For all five, the estimates suggest managed
floats during most of the period 1999-2009.
• This was a new development for emerging
markets.
• Most of the countries had some variety of a peg
before the currency crises of the 1990s.
• But the Bai-Perron test shows statistically
significant structural breaks for every currency,
• even when the threshold is set high, at the 1%
level of statistical significance.
24
Table 1A reports estimation for the
Mexican peso
• 5 structural breaks
• The peso is known as a floater.
• To the extent Mexico intervenes to reduce exchange rate
variation, $ is the primary anchor, but there also appears
to have been some weight on € starting in 2003.
• Aug.2006 - Dec.2008, coefficient on EMP is essentially
0, surprisingly, suggesting heavier intervention around a
$ target.
• But in the period starting Dec.2008, the peso once again
moved away from the currency to the north, as the worst
phase of the global liquidity crisis hit and $ appreciated.
25
Table 1A. Identifying Break Points in Mexican Exchange Rate Regime
M1:1999-M7:2009
(1)
(2)
(3)
(4)
(5)
(6)
1/21/19999/2/2001
9/9/20013/18/2003
3/25/20037/29/2006
8/5/20061/28/2008
2/4/200812/15/2008
12/22/20087/29/2009
0.92***
0.88***
0.62***
1.11***
0.96***
0.20
(0.09)
(0.12)
(0.07)
(0.10)
(0.19)
(0.22)
0.14
-0.09
0.30***
0.20*
0.51***
0.51***
(0.08)
(0.14)
(0.09)
(0.11)
(0.16)
(0.18)
-0.05
0.22***
0.08
-0.34***
-0.33**
0.18
(0.06)
(0.07)
(0.06)
(0.06)
(0.12)
(0.13)
0.14***
0.32***
0.17***
0.02
0.07
0.28***
(0.03)
(0.03)
(0.03)
(0.02)
(0.07)
(0.04)
0.00
-0.00***
-0.00*
-0.00
-0.00
0.00
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
Observations
131
78
168
76
46
29
R-squared
0.62
0.86
0.69
0.67
0.54
0.78
Br. Pound
-0.01
-0.01
-0.01
0.02
-0.14
0.11
VARIABLES
US dollar
euro
Jpn yen
△EMP
Constant
26
Tables 1B-1E
• Chile (with 3 estimated structural breaks) appears a
managed floater throughout.
– The anchor is exclusively the $ in some periods, but puts significant
weight on the € in other periods.
• Russia (3 structural breaks) is similar,
except that the $ weight is always significantly less than 1.
• For Thailand (3 structural breaks), the $ share in the
anchor basket is slightly > .6, but usually significantly < 1.
– The € and ¥ show weights of about .2 each Jan.1999-Sept. 2006.
• India (5 structural breaks) apparently fixed its exchange
rate during two of the sub-periods, but pursued a
managed float in the other four sub-periods.
– $ was always the most important of the anchor currencies, but the € was
also significant in four out of six sub-periods, and the ¥ in two.
27
Future research
• Results for other currencies will be
published in other papers
– Often requiring weekly interpolation between
monthly reserve figures.
– Including our China updates
– And true basket/band/crawl currencies
• Econometric extension: use Threshold
Autoregression for target zones.
28
Conclusion: It is harder to classify
regimes than one would think
• It is genuinely difficult to classify most countries’
de facto regimes: intermediate regimes that change
over time.
• Need techniques
– that allow for intermediate regimes (managed floating and
basket anchors)
– and that allow the parameters to change over time.
• Jarko Fidrmuc allows parameters to evolve gradually over
time, by means of a Kalman filter (session Jan. 5).
29
Bottom line(s)
• The new synthesis technique is necessary
to discern exchange rate regimes where
both the anchor weights and the flexibility
parameter are unknown.
• Weekly data are necessary to capture the
frequency with which many countries’
exchange rate regimes evolve.
30
31
Appendix 0: preliminary look at the data
• First set of countries examined:
– 9 small countries that have been officially
identified by the IMF as following basket pegs:
Latvia, Papua New Guinea, Botswana, Vanuatu, Fiji,
W.Samoa, Malta & the Seychelles.
– 4 known floaters:
Australia, Canada and Japan.
– 3 peggers of special interest:
China, Hong Kong & Malaysia.
32
Variances of Δ E & Δ Reserves are computed
• within the period 1980-2007,
• for 7-year intervals
– The aim in choosing this interval: long enough to generate
reliable parameter estimates, and yet not so long as inevitably to
include major changes in each country’s exchange rate regime.
– All changes are logarithmic, throughout this research.
– We try subtracting imputed interest earnings from
reported Δ Reserves to get intervention.
33
Figure 1: Comparison of Reserve Variability
Vs. Exchange Rate Variability
1980-2007, in 7-year intervals
Variance of ∆ log Reserves*
of
.06
vuv80
png94
scr80
wst81
bwp80
.04
scr94
scr87
scr01
.02
cad80
png80
cad87
0
hkd94
myr01
hkd01
cny05
0
aud80
png87
fiji80
fiji87
png01
vuv87
aud94 aud01
nok94
vuv94
cad94
nok80
aud87
wst88
lvl03
nok87
myr87myr80
fiji01
nok01
lvl96wst02
vuv01
fiji94
wst95
bwp87
mtl87 jpy87
mtl94
mtl01
mtl80
cad01
bwp94
jpy01
.0005
jpy80
jpy94
myr94
bwp01
.001
.0015
Variance of ∆ log Exchange Rate (in US$)
Country from year t
.002
to year t+6
Shocks line
34
Floating/Fix line
Lessons from Figure 1
1. The folly of judging a country’s exchange rate
regime – the extent to which it seeks to stabilize
the value of its currency – by looking simply at
variation in the exchange rate.
E.g. Var(ΔE) for 1980-86 A$ > 2001-07 ¥.
But not because the A$ more flexible.
It is rather because Australia was hit by much larger shocks.
One must focus on Var(ΔE) relative to Var(ΔRes).
2. Countries that specialize in mineral products
tend to have larger shocks.
35
Lessons from Figure 1,
continued
3. Even countries that float use FX reserves
actively. E.g., Canada in the 1980s.
4. A currency with a firm peg (e.g., Hong
Kong) can experience low variability of
reserves, because it has low variability of
shocks.
36
Appendix 1
Testing out the synthesis technique,
first on some known $ peggers
• RMB
(Table 2.5):
– a perfect peg to the dollar during 2001-04
($ coefficient =.99, flexibility coefficient
insignificantly different from 0, & R2=.99).
– In 2005-07 the EMP coefficient suggested that only
90% of increased demand for the currency shows up
in reserves, rather than 100%;
but the $ weight & R2 were as high as ever.
• Hong Kong $ (Table 2.8):
– close to full weight on US$,
0 flexibility, & perfect fit.
37
A commodity-producing pegger
• Kuwaiti dinar shows a firm peg
throughout most of the period:
a near-zero flexibility parameter, & R2 > .9
(IV estimates in Table 3.5; IV= price of oil).
• A small weight was assigned to other currencies
in the 1980s basket,
• but in the 2nd half of the sample, the anchor was
usually a simple $ peg.
38
A first official basket pegger
which is on a path to the €
• The Latvian lat
(Table 2.10)
– Flexibility is low during the 1990s, and has disappeared
altogether since 2000. R2 > .9 during 1996-2003.
– The combination of low flexibility coefficient and a high R2
during 2000-03 suggests a particularly tight basket peg
during these years.
– Initially the estimated weights include
$-weight .4
¥-weight .3; though both decline over time.
DM-weight .3 until 1999,
– then transferred to €: .2 in 2000-03 and .5 in 2004-07.
39
A 2nd official basket pegger
also on a path to the €
• The Maltese lira
(Table 2.12)
– a tight peg during 1984-1991 and 2004-07
(low flexibility coefficient & high R2).
– During 1980-2003, weight on the $ is .2 -.4.
– During 1980-1995, the European currencies
garner .3-.4, the £ .2-.3 & the ¥ .1.
– At the end of the sample period, the weight on the
€ rises almost to .9.
40
3rd official basket pegger
• Norwegian kroner (Table 2.14)
– The estimates show heavy intervention.
– Weights are initially .3 on the $ and .4 on European
currencies (+ perhaps a little weight on ¥ & £ ).
– But the weight on the European currencies rises at the
expense of the $, until the latter part of the sample period
shows full weight on the € and none on the $.
41
4th official basket pegger
• Seychelles rupee (Table 2.17)
– confirms its official classification,
particularly in 1984-1995: not only
is the flexibility coefficient essentially 0, but R2 > .97.
– Estimated weights: .4 on the $,
.3 on the European currencies,
.2 on the ¥ and
.1 on the £.
– After 2004, the $ weight suddenly shoots up to .9 .
42
2 Pacific basket peggers
• Vanuatu (Table 2.19)
– low exchange rate flexibility and a fairly close fit.
– roughly comparable weights on the $ , ¥, €, and £ .
• Western Samoa (Table 2.20)
–
–
–
–
heavy intervention during the first 3 sub-periods,
around a basket that weights the $ most , and the ¥ 2nd.
More flexibility after 1992.
Weights in the reference basket during 2000-2003 are similar,
except the € now receives a large significant weight (.4).
43
A BBC country,
rare in that it announced explicitly the parameters:
basket weights, band width and rate of crawl.
• Chile in the 1980s & 1990s (Table 2.4)
– R2 > .9.
– The $ weight is always high, but others enter too.
– Significant downward crawl 1980-99.
• Estimates qualitatively capture Chile’s
– shift from $ anchor alone in the 1980s,
to a basket starting in 1992.
– move to full floating in 1999.
44
Chile,
continued
• But the estimates do not correspond perfectly to the
policy shifts of 1992 & 99
• Possible explanations for gap between official regime and
estimates include:
– De facto  de jure
– Parameter changes more frequent than the 4-year sub-periods.
• The Chilean authorities announced 18 changes in regime
parameters (weights, width, and rate of crawl) during the 18year period 1982 -1999.
• The difficulty is that we have only monthly data on reserves,
for most countries => it is not possible to estimate
meaningful parameter values if they change every year or45so.
Floaters
• Australian $ (Table 2.1)
– The coefficient on EMP shows
less flexibility than one would have
expected, given that the currency is
thought to have floated throughout this period.
– Perhaps the problem is endogeneity of EMP.
• World commodity prices are a natural IV. (Table 3.1)
• For each sub-period, the estimated flexibility coefficient is indeed higher than
it was under OLS, but still far below 1.
46
Appendix 2: Current applications
using higher-frequency data
• (I) RMB
– “New Estimation of China’s Exchange Rate Regime,”
Pacific Ec.Rev., 2009.
– Updated through early 2009, on my weblog
http://content.ksg.harvard.edu/blog/jeff_frankels_weblog/2009/03/11/the-rmb-has-now-moved-back-to-the-dollar/ .
• (II) Estimation to allow for frequent parameter shifts
– Results for 13 countries offering weekly reserve data,
• Therefore allowing estimation intervals shorter than 1 year.
– Econometric techniques to estimate parameter shifts
endogenously.
47
(2.I) Estimation of RMB with updated
technique and data (through early 2008)
• This approach reveals that the RMB
basket had loosened link to the $ by late
2006, and switched substantial weight
onto the € by mid 2007.
• An implication is that the appreciation of
the RMB against the dollar observed
during this period was due to the
appreciation of the € against the $, not to
any upward trend in the RMB relative to its
basket.
48
Table 1: Evolution of RMB Basket Weights from 10-22-2006,
3-month windows of daily data, ending on the month shown
COEFFICIENT
12/2006
3/2007
2/2008
9/2008
11/2008
usd
1.005***
0.814***
0.878***
0.992***
0.971***
(0.038)
(0.035)
(0.041)
(0.027)
(0.039)
0.006
0.068**
0.019
0.049**
0.070**
(0.038)
(0.027)
(0.026)
(0.020)
(0.028)
-0.023
0.020*
0.044***
-0.030
-0.022
(0.035)
(0.011)
(0.017)
(0.019)
(0.027)
0.000**
0.000
0.001***
0.000
0.000
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
61
64
61
60
18
R-squared
0.95
0.94
0.96
1.00
1.00
krw
0.011
0.098
0.059
-0.011
-0.019
eur
jpy
Constant
Observations
49
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Table 2: Rolling 12-month regressions of value of RMB
Δ (EMP) defined as [res(t)-res(t-1)]/mb(t-1) + [exr(t)-exr(t-1)]/exr(t-1)
12-month windows, ending on the month shown
COEFFICIENT
usd
jpy
eur
Δ emp
Constant
06M11
07M2
07M3
08M3
08M5
0.909***
0.756***
0.756***
0.613***
0.597***
(0.147)
(0.105)
(0.067)
(0.171)
(0.130)
-0.015
-0.095
-0.140
0.059
0.030
(0.098)
(0.085)
(0.089)
(0.081)
(0.083)
0.029
0.116
0.169**
0.357**
0.397***
(0.117)
(0.096)
(0.068)
(0.143)
(0.105)
0.137
0.179***
0.187***
0.290***
0.249**
(0.100)
(0.047)
(0.029)
(0.076)
(0.097)
-0.001
-0.003*
-0.004***
-0.006
-0.004
(0.001)
(0.003)
(0.003)
12
12
12
0.975
0.967
0.966
0.215
-0.030
-0.024
(0.003)
Observations
12
R-squared
0.984
krw
0.077
(0.002)
12
0.967
50
In 2008, however, RMB policy changed again.
• The appreciation of the previous year had
put unwelcome pressure on exporters.
• Chinese leaders changed policy
– Naughton (2008).
– Observing that putting half-weight on the €
during a downward $ trend had led to
appreciation,
– in mid-2008, they decided to switch back
virtually to a $ peg.
– During the most recent period, September
2008-February 2009, estimates show that all
the weight has once again fallen on the $.
51
The weights in the RMB basket
shifted
toward € in 2007,
back to $ in 2008.
52
RMB was roughly flat against
basket of ½-$ + ½-€ in 2007;
$ in 2008 (like 2005) .
0.16
Appreciation vs. $ was due to weight
on € during period of $ weakening
0.15
0.14
RMB valued in terms of $
RMB valued in terms of ½$+½€
0.12
basket
0.11
0.1
0.09
RMB valued in terms of €
EUR/RMB
USD/RMB
½$+½€ basket
4/1/2009
2/1/2009
12/1/2008
10/1/2008
8/1/2008
6/1/2008
4/1/2008
2/1/2008
12/1/2007
10/1/2007
8/1/2007
6/1/2007
4/1/2007
2/1/2007
12/1/2006
10/1/2006
8/1/2006
6/1/2006
4/1/2006
2/1/2006
12/1/2005
10/1/2005
8/1/2005
0.08
6/1/2005
/RMB
0.13
53
Ironically…
• The $ has appreciated vs. the € since 2008.
– So the $-pegged RMB is stronger
than if the basket had been retained !
– Yet US Congressmen are still agitating for a
more flexible exchange rate -• not realizing that, recently, a more flexible exchange
rate would have meant a weaker RMB !
• especially since the PBoC lost reserves in January &
February 2009.
54
(2.II) Results for 13 countries that
offer weekly reserve data (1991-2008):
• Argentina, Brazil, Canada, Chile,
Colombia, India, Indonesia, Mexico, Peru,
Russia, Thailand, Turkey & Venezuela.
• E.g.,
– Colombia: during 2008, $ weight fell &
flexibility increased.
– Turkey during 2008 & 2008 moved from high
euro weight with low flexibility to low euro
weight with high flexibility.
55
Colombia: Evolution of Basket Weights,
Monthly Regressions with Daily data, 2008
(2)
(4)
(5)
(8)
(9)
(10)
VARIABLES
2/2008
4/2008
5/2008
8/2008
9/2008
10/2008
JPY
-0.028
0.023
0.227
0.028
-0.139
0.319**
(0.093)
(0.063)
(0.198)
(0.134)
(0.164)
(0.121)
0.480**
0.334***
0.019
-0.073
0.218
-0.294
(0.165)
(0.060)
(0.340)
(0.173)
(0.233)
(0.230)
0.602**
0.522***
0.226
0.774***
0.738**
0.886**
(0.274)
(0.091)
(0.277)
(0.180)
(0.343)
(0.347)
USD
EUR
Δ(emp)
Observations
GBP
0.160
0.447*** 0.688*** 0.931***
0.858*** 0.650***
(0.110)
(0.049)
(0.091)
(0.062)
(0.076)
(0.203)
21
22
14
15
20
21
-0.054
0.121
0.528
0.270
0.183
0.089
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
56
Turkey: Evolution of Basket Weights,
Quarterly Regressions with Weekly data
(1)
VARIABLES
JPY
USD
EUR
Δ(emp)
Observations
GBP
(3)
(5)
(7)
7/2007
1/2008
7/2008
-0.660
-0.250
-0.758**
0.899
(0.329)
(0.166)
(0.255)
(0.803)
0.397
-0.036
0.912
-0.961
(0.930)
(0.302)
(0.500)
(0.920)
0.906
1.931***
0.566
-0.088
(0.863)
(0.260)
(0.371)
(0.738)
0.149
0.231***
0.798***
0.825**
(0.083)
(0.056)
(0.204)
(0.279)
10
14
13
10
1/2007
0.357
-0.644
0.281
1.151
57
58
Appendix 3: For countries without
weekly reserve data, we can interpolate
between months to take advantage of
high-frequency exchange rate data
• giving enough observations per year to allow
the use of more sophisticated econometric
techniques that estimate endogenously the dates at
which parameters shift.
• Application to China
– (next slide)
– reinforces conclusion:
RMB shifted back to $ peg 9/15/08 (through March 09)
59
Identifying
Break
Points in
China’s
Exchange
Rate
Regime
(1)
(2)
(3)
(4)
(5)
(6)
VARIABLES
1/6/20057/15/2005
7/29/20054/27/2007
5/4/200711/16/2007
11/23/20079/8/2008
9/15/200812/8/2008
12/15/20083/11/2009
US dollar
1.000***
0.893***
0.596***
0.685***
0.965***
0.929***
(0.000)
(0.030)
(0.066)
(0.091)
(0.058)
0.000
0.046*
0.241***
0.128
0.037
(0.000)
(0.025)
(0.050)
(0.082)
(0.049)
-0.000
0.014
0.059**
-0.065**
0.010
(0.000)
(0.013)
(0.022)
(0.025)
(0.021)
0.000
0.034
0.185***
0.165
0.042
(0.000)
(0.024)
(0.052)
(0.125)
(0.063)
-0.000
0.000
0.000
-0.000
0.000
(0.000)
(0.000)
(0.000)
(0.001)
(0.000)
Observations
28
92
29
42
13
13
Korean won
-0.000
0.047
0.254
0.015
-0.027
0.023
R-squared
1.000
0.979
0.929
0.990
0.999
0.999
euro
Jpn yen
With weekly
exchange rate
data and
monthly
reserve data
(interpolations
are made to
get weekly
reserve data)
Δemp
Constant
(0.101)
0.087
(0.077)
0.063
(0.038)
0.129**
(0.060)
0.000
(0.001)
60
Note: *** p<0.01, ** p<0.05, * p<0.1 Robust standard errors in parentheses
Appendix 4: Monte Carlo study
on fabricated currency regimes
• Two kinds of flexibility
– Leaning ½ -way against the wind of EMP fluctuations
(Table 8.1)
– Or else constrained to remain in a 5% band
(Table 8.2)
• Two anchors
– $ peg
– Basket: 1/3 $, 1/3 €, 1/3 ¥
• The synthesis technique generally gives the right
answer.
61
Monte Carlo exchange rate under
simulated basket+band regime
-.45
-.4
-.35
-.3
-.25
-.2
(with parameters from Papual New Guinea)
240
260
280
300
320
340
T
mcsa
upperb
mcsa_non
lowerb
62
Appendix 5 -- One concern: endogeneity of
the exchange market pressure variable
• One would prefer to observe changes in the
international demand for the home currency known to
originate in exogenous shocks.
• In the case of countries that specialize in the
production of mineral or agricultural commodities,
there is a ready-made IV: changes in the price of the
commodity on world markets.
• Accordingly, Tables 3 repeat the synthesis estimation
technique, but for the commodity producers it uses
changes in the world price of the commodity in
question as an IV for changes in EMP.
63
To address endogeneity of EMP, we
use commodity prices as IV
• Malaysian ringgit
(Table 2.11 )
OLS.
– Only in 1996-99 is there evidence of
exchange rate flexibility (Asia crisis ).
– During 2000-03 there is a perfect peg to the $
(coefficient $ R2 both =1).
– In 2004-07 the peg is still fairly strong, but here the weight of
the US$ falls to .6, partially replaced by the Singapore
$ (weight = .4) .
• IV = prices of tin & semiconductors (Table 3.6)
– Again, a perfect $ peg during 2000-03,
– followed by shift to a basket consisting of an average of the US
$ + the Singapore $.
64
Recurrent finding: IV estimate on EMP is
higher than OLS estimate
(but lower in significance)
• Floaters: IV estimates for Canadian $, as with A$,
show flexibility parameters in each sub-period
higher than they were under OLS, but surprisingly
insignificant statistically.
• IV also raises flexibility coefficient for Intermediate
regimes:
– Thailand (Table 3.11) IV = price of rice
– W.Samoa (Table 3.12) IV = price of coconuts.
65