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9 8 2 3
Central Bank Intervention
and Overnight Uncovered
Interest Rate Parity
by Richard T. Baillie and
William P. Osterberg
FEDERAL RESERVE BANK
OF CLEVELAND
Working Paper 9823
Central Bank Intervention and Overnight Uncovered Interest Rate Parity
by Richard T. Baillie and William P. Osterberg
Richard T.Baillie is Professor of Economics at Michigan State University, East Lansing,
Michigan, and a visiting consultant with the Federal Reserve Bank of Cleveland.
William P. Osterberg is an Economist at the Federal Reserve Bank of Cleveland.
The authors thank Jennifer DeRudder for research assistance.
Working papers of the Federal Reserve Bank of Cleveland are preliminary materials
circulated to stimulate discussion and critical comment on research in progress. They
may not have been subject to the formal editorial review accorded official Federal Reserve
Bank of Cleveland publications. The views stated herein are those of the authors and are
not necessarily those of the Federal Reserve Bank of Cleveland or of the Board of
Governors of the Federal Reserve System
Working papers are now available electronically through the Cleveland Fed's home page
on the World Wide Web: http://www.clev.frb.org.
January 1999
Central Bank Intervention and Overnight Uncovered Interest Rate Parity
By Richard T. Baillie and William P. Osterberg
This paper considers the impact of U.S. and German central bank intervention on the risk premium in forward
foreign exchange markets. The model estimation is facilitated with the use of daily data on overnight
Eurocurrency deposit rates, so that the interest rate maturity time of one day matches the sampling interval of
the data. We also use the official net daily purchases and sales of dollars vis-à-vis the German Mark by the
Federal Reserve System and the Bundesbank. The model involves FIGARCH innovations to model the degree
of long term dependence in the volatility process. Some support is found for the intervention variables
affecting the risk premium as predicted by theory. The impact of intervention in the two years immediately
following the meltdown of the equity markets in October 1987 is particularly strong.
Keywords: Exchange Rates, Central Bank Intervention, Risk Premium, FIGARCH.
JEL Classification numbers: C22, E41, E31
CENTRAL BANK INTERVENTION AND
OVERNIGHT UNCOVERED INTEREST RATE PARITY
by
Richard T Baillie
(Michigan State University)
and
William P Osterberg*
(Federal Reserve Bank of Cleveland)
This version: December, 1998
Address for Correspondence:
Richard T Baillie, Department of
Economics, Michigan State University, East Lansing, MI 48824.
Phone: (517) 355-1864; email: baillie@pilot.msu.edu
*
The views in this paper do not necessarily reflect the
views of the Federal Reserve Bank of Cleveland, or of the Board of
Governors of the Federal Reserve System. The authors thank
Jennifer DeRudder for research assistance.
1
Abstract
This paper considers the impact of U.S. and German central
bank intervention on the risk premium in forward foreign exchange
markets.
The model estimation is facilitated with the use of
daily data on overnight Eurocurrency deposit rates, so that the
interest rate maturity time of one day matches the sampling
interval of the data.
We also use the official net daily
purchases and sales of dollars vis-%-vis the German Mark by the
Federal Reserve System and the Bundesbank.
The model involves
FIGARCH innovations to model the degree of long term dependence in
the volatility process.
Some support is found for the
intervention variables affecting the risk premium as predicted by
theory. The impact of intervention in the two years immediately
following the meltdown of the equity markets in October 1987 is
particularly strong.
Keywords: Exchange Rates, Central Bank Intervention, Risk Premium,
FIGARCH.
JEL Classification numbers: C22, E41, E31.
2
1. Introduction
While central bank intervention has at times been quite
substantial in the post Bretton Woods era, there continues to be
controversy over its effectiveness in achieving the policy goals
of either changing the level of nominal exchange rates or of
reducing volatility.
A large literature has examined the
usefulness of intervention; see Edison (1993) and Almekinders
(1995). The general conclusion seems to be that the policy either
has consequences that vary with the sample period, effects that
are inconsistent with the theory, or ultimately has little impact
on nominal exchange rates. On the one hand, Dominguez and Frankel
(1993) and Ghosh (1992) find support for the effectiveness of
intervention. However, Baillie and Osterberg (1997a, 1997b) find
evidence that G3 intervention either has no statistical effect, or
that it has outcomes which are the opposite to those intended.
Econometric analysis of the impact of central bank
intervention is generally constrained by the availability of
official data. Clearly the nature of the volatility of asset price
markets and the likely short-lived nature of risk premia make it
desirable to use daily data or very high frequency data. Ideally,
we would utilize intraday data, as in the study by Goodhart and
Hesse (1993). In this study we use the officially recorded net
intervention by the Federal Reserve Bank over the previous 24
hours, and similar but confidential data kindly supplied by the
Bundesbank.
The appropriate sampling frequency of the data is related to
the hypothesized transmission mechanism of intervention. Studies
such as Ghosh (1992), Obstfeld (1989) and Humpage (1988) which
relate intervention to portfolio balances are limited to monthly
data and have been generally inconclusive as to the validity of
the portfolio balance effect.
The possibility that intervention
signals a change in monetary policy is examined by Dominguez
(1990) who uses weekly data on monetary surprises, exchange rates
3
and
intervention
credibility
of
and
finds
monetary
its
effectiveness
policy.
Conversely,
varies
if
with
an
the
!small
intervention can provide a signal of monetary policy (Klein
(1992)), then the impact of the signal might be largely unrelated
to fundamentals such as money supply.
Generally, studies that have examined the effects of daily
intervention on daily spot exchange rates have either found no
effects (e.g. Baillie and Osterberg (1997a)), or effects that are
extremely weak, e.g. Goodhart and Hesse (1993). A small number of
studies have assessed intervention s impact on the mean and/or
conditional variance of deviations from uncovered interest rate
parity
(UIRP).
Loopesko s
(1984)
approach
utilized
cumulative
intervention flows from the beginning of her sample period while
Dominguez (1992) analyzed the impact of daily flows. Humpage and
Osterberg (1992) tried both approaches in their analysis of the
conditional mean and conditional variance of deviations from UIRP.
One theoretical motivation for an impact of intervention on
UIRP is provided by Osterberg (1989) and Baillie and Osterberg
(1997b) who formulated a two country inter-temporal asset pricing
model
which
implied
that
central
bank
foreign
exchange
intervention affects the forward exchange risk premium. Baillie
and Osterberg (1997b) found empirical support for intervention
influencing the risk premium in the forward DM-$ and Yen-$
markets. Purchases of dollars by the Federal Reserve System were
found to be associated with excess $ denominated returns, and
furthermore, there was evidence that intervention increased rather
than reduced exchange rate volatility.
The Role of Intervention
The Federal Reserve System and the Bundesbank appear to
routinely sterilize their interventions so that the purchase
(sale) of foreign currency is offset by a corresponding sale
(purchase) of domestic government debt to eliminate the effects on
4
domestic money supply.
Clearly, unsterilized intervention is
equivalent to monetary policy and is more likely to directly
effect exchange rates. However, even sterilized intervention might
be linked to monetary policy.
The
signaling
literature
the
views
central
intervention
bank s
future
as
working
monetary
either
policy
or
by
by
operating via a portfolio-balance effect. The latter approach is
motivated by mean-variance optimization, where agents are
concerned with terminal wealth composed of domestic and foreign
currencies and bonds. Sterilized intervention will alter the
relative supplies of domestic money and bonds. With risk averse
investors who view domestic and foreign bonds as imperfect
substitutes, the impact of intervention will adjust the relative
rate of return by changing the exchange rate.
However, the
portfolio balance theory implies no impact of intervention on the
exchange rate when there is perfect substitutability of bonds
and/or Ricardian equivalence, so that consumers exactly anticipate
future taxes associated with government debt.
Any test of the
theory requires information on the relative supplies of the
assets.
The alternative view of intervention as a signal of the
central bank's future monetary policy implies that a sterilized
purchase of foreign currency is expected to lead to a depreciation
of the exchange rate if the foreign currency purchase is assumed
to signal a more expansionary domestic monetary policy. Klein and
Rosengren
(1991)
find
no
consistent
relationship
between
intervention and monetary policy and Kaminsky and Lewis (1996)
report that the impact of intervention on exchange rates has
sometimes been inconsistent with the implied monetary policy.
Humpage (1997) concludes that the US authorities in the 1990s had
no information superior to the market so that intervention could
not be viewed as signaling new information about monetary policy.
Dominguez and Frankel (1993) found inconclusive evidence on the
5
signalling and portfolio balance transmission mechanisms, while
Ghosh (1992) found that variables associated with the portfolio
balance approach appear to have little effect on spot exchange
rates.
Definitions
For subsequent analysis, we define St as spot exchange rate
in terms of DM-$ at time t, Ft is the forward exchange rate at
time t, for delivery at time t+k and Pt is the domestic price
level. The UIRP condition is,
(1)
Et[(Ft - St+k)/Pt+k] = 0,
since expected real returns in the forward market are zero.
taking a Taylor series expansion to second order terms,
(2)
Etst+k - ft = -(1/2)Vart(st+1) + Covt(st+1pt+1),
(3)
Et∆st+1 - (it* - it) = -(1/2)Vart(st+1) + Covt(st+1pt+1),
On
where lower case variables denote the logarithms of variables in
levels, and it is the dollar return on a risk free $ denominated
bond, and it* is the foreign currency return on a risk free bond
denominated in terms of the foreign currency.
Usually, the ex
post deviation from UIRP is expressed as,
yt = ∆st+k - (it* - it),
where the two terms on the right hand side of (2) are neglected.
Hence the country with the higher rate of interest is expected to
have the depreciating currency. The forward premium anomaly found
in many studies is that a regression of the form of,
st+k - st = + β(it* - it) + ut+k,
is found to have a negative slope coefficient, β, which implies
that the country with the higher rate of interest is expected to
6
have a currency appreciation.
A generalization of equation (2) is to specify real returns
over the current and future consumption stream, so that equation
(3) is modified to the discrete time asset pricing approach of
Lucas (1978) to be,
Et[(Ft - St+k)/Pt+k]U/(Ct+k)/U/(Ct) = 0,
(4)
where U/(Ct+1)/U/(Ct) is the marginal rate of substitution in terms
of utility derived from current and future consumption.
On
assuming
a
logarithmic
utility
function
with
a
constant
coefficient of relative risk aversion, (CRRA), denoted by γ, then
equation (4) can be expressed as,
(5)
= -(1/2)Vart(st+1) + Covt(st+1pt+1) + γCovt(st+1ct+1).
Etst+1 - ft
The last term, will be denoted by
ρt = γCovt(st+1ct+1) and is known
as a time dependent risk premium.
There are several possible
theoretical developments of this term.
For example, if each
country's consumption growth is assumed to be proportional to
world
income
growth
so
that
Ct
Ytγ,
the
CRRA
assumption, the risk premium term in (6) is ρt = Covt(st+1yt+1γ).
More
generally,
ρt
the
ρCovt(st+1qt+1),
risk
where
premium
qt
is
is
the
=
then
frequently
logarithm
of
under
expressed
the
as,
=
intertemporal
marginal rate of substitution.
It should be noted that in many previous studies of time
varying risk premium, e.g. Domowitz and Hakkio (1985), Hodrick
(1987, 1989) and Kaminsky and Peruga (1990), the xt variables
typically contain conditional variances and covariances of the
asset price vis-%-vis the forcing variables.
However, the fact
that goods market variables needed for the Lucas-Breeden model
such as relative prices, consumption levels, money supplies, etc
7
are generally only observed monthly and the fact that goods market
prices are very smooth compared to asset market prices makes
estimation and testing these relationships problematic. Also, as
noted by Baillie and Bollerslev (1989), exchange rates typically
possess little ARCH effects at the monthly level, so that studies
such as Kaminsky and Peruga (1990) have been unsuccessful in
collaborating
the
Lucas
Breeden
asset
pricing
model.
Consequently, it is attractive to estimate the model from daily
data where the effects of changes in the intervention variables
may be more clearly apparent.
Data
This study uses data provided by the Board of Governors of
the Federal Reserve System and the German Bundesbank from January
3, 1987 through January 22, 1993.
This includes the periods
around the Louvre Accord and the stock market crash in October,
1987. For each country, the data record in 100 million US dollar
units the actual net purchases of US dollars vis a vis the German
mark from close of business on day t-2 to close of business on day
t-1.
The exchange rates are recorded at 9:30am Paris time as
supplied to us by Olsen & Associates of Zurich, Switzerland. For
each bilateral exchange rate there are four intervention
variables: the purchases and sales of dollars by each country. The
intervention variables are aligned so as to be predetermined with
respect to the change in the exchange rate from day t to t+1.
We use a unique data set on overnight Eurocurrency deposit
rates obtained from the Paris market through DRIFACS, with the
ultimate source being Credit Lyonnais, Paris.
The use of this
data, which are essentially interest rates of one day maturity
time, allows us to avoid many of the econometric problems
associated with forward rates with overlapping contracts, which
reduce the efficiency in tests of unbiased expectations of the
forward rate. With one month forward contracts it is important to
elaborately match in accordance with the settlement conventions as
8
described by Riehl and Rogriguez (1977), Levine (1989) and Bekaert
and Hodrick (1993).
Figure 1 shows the movement in the DM/$ rate and the
overnight interest rates during the sample period. The U.S. rate
exceeded that of Germany until Fall 1990. UIRP requires that
higher(lower) U.S. rates be offset by an expected decline(rise) in
the DM/$. However, causality could run in either direction. The
DM/$ began a steady decline from over 2 in June 1989. Figure 2
shows the logarithmic analogues which together comprise the
deviation from UIRP. Clearly, and not surprisingly, the volatility
in the exchange rate exceeds that of the interest rates. Figure 3
shows the logarithmic change in the exchange rate together with
U.S. intervention vis-%-vis the DM, with positive intervention
indicating purchases of dollars.
It is not clear from Figure 3 whether U.S. intervention was
intended to affect either the level or the volatility of the DM/$.
There were only two possibly distinct intervals of intervention
activity-the period around the Louvre Accord of February 20-21,
1987 and the equity market crash of October 1987.
An Econometric Model for UIRP
An
econometric
model
for
the
ex
post
deviations
from
uncovered interest rate parity is given by,
(6)
yt =(∆st+1 - it* + it) = b/xt + εt+1,
(7)
εt =
(8)
σ2t = ω + βσ2t-1 + (1 - βL - (1 - ϕL)(1 - L)δ)ε2t,
ξtσt,
where ξt is i.i.d.(0,1) process, b are a k dimensional vector of
predetermined variables, and σ2t is a time-varying, positive and
measurable function of the information set at time t-1.
9
Hence the
conditional variance σ2t, is represented by a FIGARCH (Fractionally
Integrated AutoRegressive Conditional Heteroskedastic) models as
developed by Baillie, Bollerslev and Mikkelsen (1996).
The above
model is the FIGARCH(1,δ,1) process and generates the type of very
slow decay which are frequently observed in the autocorrelations
of squared returns, absolute returns and the power transformations
of
returns;
see
Granger (1996).
(9)
Ding,
Granger
and
Engle
(1993)
and
Ding
and
The general FIGARCH(p,δ,q) process is given by,
[1 - β(L)]σ2t = ω + [1 - β(L) - ϕ(L)(1 - L)δ]y2t,
where β(L) = β1L + .... + βpLp,
ϕ(L) = 1 - α(L) - β(L),
and α(L) =
q
1L + .....+ qL ; while # denotes the long memory, or fractional
parameter and is defined for
0 < δ < 1.
There are some further
important restrictions on the parameters; namely that ω > 0 and
that all the roots of [1 - β(L)] and ϕ(L) must lie outside the
unit circle.
By straightforward algebra, the process can also be
expanded as the infinite order ARCH model,
2
2
(10) σ t = ω/[1 - β(1)] + λ(L)y t,
where
(11) λ(L) = {1 - [1 - β(L)]-1ϕ(L)(1 - L)δ}.
The key property of the above FIGARCH model, which distinguishes
it from alternatives is that it again implies very slow hyperbolic
rate of decay on the impulse response weights λk kd-1 in equations
(10) and (11), which is essentially the "long memory" property, or
"Hurst effect". Many well known ARCH models are special cases of
the FIGARCH representation. For example, when d = 0, p = q = 1,
then equation (8) reduces to the GARCH(1,1) model. When d = p = q
10
= 1, in equation (8) realizes the Integrated GARCH, or IGARCH(1,1)
model, and implies complete persistence of the conditional
variance to a shock in squared returns.
The attraction of the
flexibility of the FIGARCH process is that intermediate ranges of
persistence can be introduced by having d in the range, 0 < d < 1.
In many practical situations quite low order models are adequate,
such as the FIGARCH(1,d,1) model.
An even simpler model to be
applied later is the FIGARCH(1,d,0) process,
(1 - βL)σ2t = ω + [1 - βL - (1 - L)d]y2t.
(12)
For this model the impulse response weights in (6) are σ2t = ω/(1 β)
+
2
λ(L)y t,
and
it
can
be
shown
that
λk
=
Γ(k+d-
1)/{Γ(k)Γ(d)}[(1-β)-(1-d)/k], where Γ(.) is the gamma function.
For large lag k,
d-1
λk = [(1-β)/Γ(d)]k ,
which generates the same
slow hyperbolic rate of decay on the impulse response weights of
the conditional variance σ2t.
(6)
In this study the estimation of the system of equations in
through (8) is facilitated by means of Quasi Maximum
Likelihood Estimation (QMLE); see Bollerslev and Wooldridge
(1988). The procedure uses conventional non-linear procedures to
maximize the Gaussian log likelihood function,
(13) log(θ) = -(T/2)log(2Π) - (1/2)Σt=1,T[log(σ2t + ε2tσ-2t],
with respect to a specified vector of parameters, θ.
It should be
noted that the numerical procedures are quite general and can be
readily extended to models such as the regression model with ARMA
disturbances and FIGARCH volatility process.
Since most return
series are not well described by the conditional normal density in
(14); subsequent inference using robust standard errors, is based
upon noting that the limiting distribution of the QMLE are given
11
by,
(15)
where
T1/2(θT - θ0) < N{0, A(θ0)-1B(θ0)A(θ0)-1},
A(.)
and
B(.)
represent
the
Hessian
and
outer
product
gradient respectively; and θ0 denotes the true parameter values.
Results
Some tests of uncovered interest rate parity are obtained by
regressing the spot rate returns on the interest rate differential
and also the same menu of predetermined variables as in equation
(6). Table 1 presents the results estimating the model when four
intervention variables (buying and selling of dollars vis-%-vis
the DM by both countries) are in the conditional mean and when a
GARCH(1,1) formulation is employed for the conditional variance.
Results for equation (6) are the same with a FIGARCH specification
or when day-of-the-week dummies are included in the conditional
mean as is discussed later. In all cases the estimate of the
coefficient associated with the interest rate differential was
negative in accord with the average value of -0.88 found in 75
separate studies by Froot and Thaler (1990).
However, with the
overnight Euro deposit rate data, the .95 percentile confidence
intervals around the estimated value of b were sufficient to
include the value of b = 1. Since the main focus in this study is
that of the risk premium the value b = 1 is maintained throughout
so that the dependent variable is the ex post return over
uncovered interest rate parity,
yt = (∆st+1 - it* + it). Since the
sampling interval of one day exactly matches the maturity time of
the forward contract, the yt series appears uncorrelated.
Consequently,
model,
table
2
reports
estimates
of
the
following
(6 ) [st+1-(it* -it)]= µ + γ1UStb + γ2USts + γ3Gtb + γ4Gts + Σj=1,4λjDjt +
12
εt,
(7)
εt =
(8)
σ2t = ω + βσ2t-1 + (1 - βL - (1 - ϕL)(1 - L)δ)ε2t,
ξtσt,
where ξt is the i.i.d.(0,1) process, σ2t is the conditional variance
process, Djt denote day of the week dummy variables. Baillie and
Bollersev (1989), Hsieh (1989), and McFarland, et al. (1982) have
discussed the possibility of day-of-the-week effects in the
conditional mean and conditional volatility of daily exchange rate
returns.
UStb denotes the Federal Reserve Bank buying dollars, USts
denotes the Federal Reserve Bank selling dollars, and Gtb and Gts
denotes corresponding actions by the German Bundesbank. The
introduction of intervention in this manner is consistent with
viewing intervention as providing a signal of policy. U.S.
purchases of dollars, by this reasoning, should signal that the
authorities have information, presumably about policy, which once
known, would boost the DM/$. This applies to the impact of
Bundesbank dollar purchase as well. Thus we would expect estimates
of both γ1 and γ3
to be positive and for estimated γ2 and γ4
to be
negative.
Table presents results from estimating the system (6 ), (7),
and
(8).
Diagnostic
tests
indicate
the
success
of
the
FIGARCH(1,d,1) specification in modeling the conditional variance
of the deviations from UIRP and in offering a significant
improvement over alternatives such as GARCH. None of the daily
dummies (Monday through Thursday) were significant in the
conditional mean. Of the four intervention variables, only German
buying has a significant impact with the opposite sign from that
implied by the signaling hypothesis. This is consistent with
German dollar purchases reducing DM/$.
The second column of Table 2 indicates the results for a
subperiod that spans just after the October 1987 crash through the
13
end of 1989. The coefficient on German purchases continues to be
negative but U.S. buying has a positive impact.
Conclusion
This
paper
has
presented
investigation into the impact
preliminary
results
of U.S. and German
of
an
dollar
intervention vis-%-vis the DM on the deviation from uncovered
interest rate parity (UIRP). We view intervention as possibly
signaling future policy so that dollar purchases should increase
the deviation from UIRP. The approach adopted differs from
previous work by utilizing overnight Eurocurrency rate data
matched exactly to exchange rates. A FIGARCH formulation of the
conditional variance and thus the standard errors is implemented
with QMLE.
We find that German dollar purchases decreased the deviation
from UIRP, a result consistent with our previous finding (Baillie
and Osterberg [1997a]) that it decreased the DM/$ during August 6,
1985 through March 1, 1990. This result holds up in an examination
of October 20, 1987 through 1989 subperiod during which, however,
we find a positive impact of U.S. dollar purchases. This latter
result is consistent with our analysis of the risk in the forward
market (Baillie and Osterberg[1997b]). The same conclusion results
when we account for possible differences between coordinated and
unilateral interventions.
In general, these results are negative for the signalling
hypothesis. However, additional results have confirmed similar
findings for analyzing the impact of cumulative intervention.
Further work might take account of market conditions and policy
intentions during specific subperiods. For example, intervention
at times might be intended to !lean against the wind , or to reduce
volatility.
14
Table 1: Estimation of the model:
∆st+1 = µ + b(it* - it) + γ1UStb + γ2USts + γ3Gtb + γ4Gts + εt,
εt =
ξtσt,
σ2t = ω + αε2t-1 + βσ2t-1
Parameters
µ
-0.256(0.203)
b
-1.813(2.210)
γ1
0.009(0.007)
γ2
-0.003(0.007)
γ3
-0.011(0.005)
γ4
0.005(0.005)
ω
1.728(0.626)
α
0.081(0.015)
β
0.893(0.019)
m3
m4
Q20
Q220
0.232
4.430
14.88
19.14
ln(j)-5029.86
Key: There are T = 1,463 observations from January 5, 1987
through January 22, 1993.
15
Table 2: Estimation of the model:
[∆st+1-(it*-it)] = µ + γ1UStb + γ2USts + γ3Gtb + γ4Gts + Σj=1,4λjDjt + εt,
εt =
ξtσt,
σ t = ω + βσ2t-1 + [1 - βL - (1 - ϕL)1 - L)δ]ε2t,
2
Parameters
Full sample
Oct 20,1987
-Dec 31, 1989.
--------------------------------------------------------µ
-0.408(0.391)
0.640(0.690)
γ1
0.009(0.007)
0.022(0.009)
γ2
-0.004(0.007)
-0.010(0.005)
γ3
-.010(.005)
-0.014(0.005)
γ4
0.005(0.005)
λ1
0.382(0.681)
-1.826(.926)
λ2
0.080(0.558)
-0.935(0.904)
λ3
0.287(0.545)
0.863(0.885)
λ4
0.323(0.526)
-1.647(0.979)
δ
0.525(0.144)
0.624(0.290)
ω
1.682(0.804)
1.815(1.544)
β
0.671(0.103)
0.713(0.176)
ϕ
0.182(0.069)
0.042(0.140)
m3
m4
Q20
Q202
ln(j)
0.17
4.21
15.43
19.37
0.007(0.004)
0.12
3.74
15.16
14.93
-5028.89
-1783.370
Key: There are T = 1,463 observations for January 5, 1987
through January 22, 1993. In the reduced sample, T = 530.
Asymptotic robust standard errors are in parentheses.
The Qm
statistic is the Ljung-Box test for autocorrelation based on the
first m autocorrelations of the standardized residuals. Qm2 is the
16
Ljung-Box test for ARCH effects based on the first m lags of the
autocorrelations of the squared standardized residuals.
17
References
Almekinders, G J (1995), Foreign exchange intervention:
and evidence, Brookfield, Vt.: Elgar.
Theory
Baillie, R T and T Bollerslev (1989), "The message in daily
exchnage rates: a conditonal variance tale", Journal of
Business and Economic Statistics,
Baillie, R T and W P Osterberg (1997a), Why do central banks
intervene?, Journal of International Money and Finance, 16,
909-919.
Baillie, R T and W P Osterberg (1997b), Central bank intervention
and risk in the forward market, Journal of International
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20
1993
1992
1992
1992
1992
1992
1991
1991
1991
1991
German overnight rate
1991
1991
1990
1990
1990
1990
1990
1990
1989
1989
1989
1989
1989
1988
1988
1988
1988
1988
1988
2/23/87
1987
1987
1987
1987
1987
1987
Percent
DM/$
DM/$
FIGURE 1: U.S. AND GERMAN OVERNIGHT RATES AND DM/$ EXCHANGE RATE
12
2.2
10
2
8
1.8
6
1.6
4
1.4
U.S. overnight rate
2
10/20/87
1.2
1993
1992
1992
1992
1992
1992
1991
1991
1991
1991
1991
1991
1990
1990
1990
1990
1990
1990
1989
1989
1989
1989
1989
1988
1988
1988
1988
1988
1988
2/23/87
1987
1987
1987
1987
1987
1987
Change in exchange rate
50
40
0.05
10
0
0
-0.05
-10
-20
-0.1
-30
-0.15
10/20/87
-40
-0.2
Interest rate differential
FIGURE 2: 1000(∆ln dm/$), 1000(ln(1+German rate/36000)-ln(1+U.S. rate/36000))
0.2
0.15
30
0.1
20
1992
1992
1992
1992
1992
1992
1991
1991
1991
1991
1991
1991
1990
1990
1990
1990
1990
1990
1989
1989
1989
1989
1989
1989
1988
1988
1988
1988
1988
1988
2/23/87
1987
1987
1987
1987
1987
1987
Change in exchange rate
60
900
40
600
20
300
0
0
-20
-300
-40
-600
-60
10/20/87
-900
Millions of U.S. dollars
FIGURE 3: 1000(∆ln dm/$), U.S. Intervention vis-à-vis dm
@FedFRASER