Download Exchange rate exposure among European firms

Accounting and Finance 45 (2005) 479–497
Exchange rate exposure among European firms:
evidence from France, Germany and the UK
William Reesa , Sanjay Unnib
a
University of Amsterdam Business school, 1018 WP Amsterdam, the Netherlands
b
Finance & Damages Group, LECG LLC, Emergville, 94608, USA
Abstract
We investigate the pre-Euro exposure to exchange rate changes of large firms in the
UK, France and Germany. We find that the exchange rate sensitivity is considerably
stronger than previously thought. In all three countries, firms typically gain value
when their local currency depreciates against the US dollar, yet most UK firms lose
value when sterling depreciates against the European currency unit. We also document
the existence of an intriguing intervalling effect in the measurement of exchange
rate exposure, which suggests that share prices might exhibit a delayed response to
information, and prevents us from making robust generalizations concerning other
exchange rate sensitivities.
Key words: Exchange rate exposure; Hedging; Intervalling effects
JEL classification: F31; F23
doi: 10.1111/j.1467-629X.2005.00154.x
1. Introduction
Drawing upon the duality between exposure and hedging, Adler and Dumas (1984)
have argued that the firm’s exposure to a particular currency can be measured as the
amount of the currency an investor would have to sell forward to minimize the variance of a stock-currency hedge portfolio. This definition is appealing at two levels.
Conceptually, it accords with our intuition about exposure because, when hedging
to the full extent of exposure, the investor’s portfolio returns become statistically
We gratefully acknowledge Robert Faff, Darryl Holden, Jeroen Ligterink, Anthony Santomero,
Piet Sercu, Richard Stapleton and an anonymous referee for their suggestions. Steven Tokar
provided excellent research assistance on earlier drafts of the present paper.
Received 15 September 2004; accepted 25 February 2005 by Robert Faff (Editor).
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independent of exchange rate movements. Empirically, this definition offers the convenience of allowing exposure to be measured as the coefficients in a linear regression
of stock returns on rates of change in exchange rates.
The present paper provides evidence on the direction and magnitude of currency
exposure for a sample of large European firms, drawn from the UK, France and
Germany, over a period ranging from 1 January 1987 to 31 December 1998. This
allows us to examine exchange rate risk in three open, closely integrated, medium
sized economies where large firms can be expected to exhibit relatively high levels
of multinational activity and, hence, exposure. Our sample period ends sufficiently
far in advance of the impending introduction of the euro to be free of contamination
from that event.1
We find that firms in all three economies gained market value when their local
currency depreciated against the US dollar. However, a significant majority of UK
and German firms actually lost value when their currencies depreciated against the
European currency unit (ECU), and German firms were similarly hurt by a depreciation of the deutschmark against the yen. These conflicting patterns of exposure
suggest that a substantial number of European firms are exposed to foreign currencies
not just through their revenues but also through their costs of production or their costs
of capital.
Previous studies of exchange rate exposure have found, at best, mixed evidence of
exchange rate exposure and provide relatively little evidence on exposure in major
European economies. Jorion (1990), Booth and Rotenberg (1990), Bodnar and Gentry
(1993), Bartov and Bodnar (1994), Choi and Prasad (1995) and Chow et al. (1997a,b)
have focused on the exposure of North American firms. Bodnar and Gentry (1993),
Di Iorio and Faff (2001), Dominguez and Tesar (2001), Doidge et al. (2002), Chow
and Chen (1998) and He and Ng (1998) have examined the exposure in various
economies outside the USA. Studies of European firms are relatively scarce. Glaum
et al. (2000) examine the German market and Nydahl (1999) analyses Swedish firms.
Of course, Doidge et al. (2002) include results for the main European economies
and Dominguez and Tesar (2001) also include results for the countries we examine.2
Although Glaum et al. (2000) and Nydahl (1999) report relatively large incidences
of exchange rate exposure, 31 and 40 per cent, respectively, neither of the broader
international studies report pervasive exchange rate exposure in Europe. We find
more widespread exposure than any of the earlier studies and attribute the differing
results to the more robust estimation approach we adopt.
In addition, some previous papers have ignored important methodological issues in
the measurement of exposure. First, currency risk has typically been measured with
1 Of course that begs the question of the impact of the euro on exchange rate risk. We now have
evidence available for over 3 years; that is, just about, sufficient to allow reasonably robust
estimation of the exchange rate risk. Comparison of the new circumstances with the results
presented here might well provide interesting insights in future research.
2 Doidge et al. (2000) include 18 countries in their analysis and those from Europe are Belgium,
Denmark, France, Germany, the Netherlands, Spain, Switzerland and the UK.
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respect to a single composite index of exchange rates, therefore failing to capture
the possibility that firms might have conflicting directions of exposure to different
currencies. Stulz and Williamson (1997), who examine the impact of multiple exchange rates on a single firm’s returns, Dominguez and Tesar (2001), who include the
local currency to dollar rate and Di Iorio and Faff (2001), who test the Australian dollar
to US dollar and separately the Australian dollar to the yen exchange rates, appear to
be relatively rare exceptions. Second, exposure estimates have typically been based
on a single, non-overlapping series of returns within the sample period, therefore
failing to exploit the full price and currency information contained in this period.
Chow et al. (1997b), Chow and Chen (1998) and Di Iorio and Faff (2001) are again
relatively unusual exceptions. We also base our estimates on overlapping returns data,
which offer more efficient estimates of exposure (Boudoukh and Richardson, 1993).
Recent research indicates that long-horizon regressions involving overlapping returns series can spuriously indicate a ‘significant’ relationship even when the underlying variables are economically unrelated (see, e.g., Valkanov, 2003; Torous
et al., 2005). This bias arises when the horizon over which returns are measured
is a non-trivial fraction of the total sample. To avoid this bias, we use 12 years of
daily data (amounting to 3131 observations) and examine returns horizons no greater
than 80 days in length, ensuring that even the longest horizon analysed in our study
amounts only to approximately 2.5 per cent of the entire sample.
Finally, exposure is usually estimated with monthly returns, suggesting that a
monthly horizon is sufficient for the impact of currency shocks to be impounded
into stock returns. A few papers have experimented with different intervals. Bartov
and Bodnar (1994) use lagged changes in the exchange rate variable and discover
enhanced evidence of foreign exchange exposure. More usually those researchers
who have addressed this issue have extended the interval. Chow et al. (1997b), Chow
and Chen (1998) and Di Iorio and Faff (2001) have all experimented with varying
the interval. Generally, the estimates of foreign exchange exposure are greater for
longer intervals than for the shorter ones. We also find that the magnitude of exposure
changes significantly as the returns horizon is extended from 1 to 80 trading days
(approximately one-quarter of a year). Moreover, the pattern of this change is not
necessarily monotonic; for many firms exposure changes in U-shaped or humpshaped patterns with the returns horizon. In a few cases, exposure even changes sign
as the horizon is lengthened.
If currency information is impounded only gradually, and perhaps imperfectly,
into stock prices, the true exchange rate exposure of a firm is the level to which
its exposure coefficients converge as the returns horizon is extended asymptotically.
Although this idea contradicts the traditional view of market efficiency, it is consistent with mounting evidence that investors absorb the full implication of corporate
news only with delay. In their wide-ranging survey of this published literature, Daniel
et al. (1998) cite more than 20 studies that document initial underreaction and subsequent long-term adjustment to announcements ranging from tender offers and analyst recommendations to dividend initiations and omissions and seasoned equity. We
have experimented with asymptotic estimates, but this necessarily incorporates long
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interval estimates, and, given the reservations concerning such estimates alluded to
earlier, we leave this analysis to subsequent research.
In Section 2, we present the framework within which we estimate exchange rate exposure. Section 3 reports the results of our estimation, and Section 4 offers concluding
observations on directions for future research.
2. Estimating exchange rate exposure
2.1. Estimation framework
The value exposure of a firm over a particular horizon is defined as the marginal
impact of an exchange rate movement upon the returns of the firm over this horizon,
and is estimated in a linear regression framework. We estimate the exposure of each
firm to three exchange rates; specifically, the exchange rates of their home currencies
with respect to the ECU, the yen and the US dollar.
Because exchange rates and interest rates are closely linked through currency
markets, it is possible that an apparent statistical relationship between firm returns
and exchange rates really reflects the firm’s underlying exposure to interest rates.
As has been noted by Flannery and James (1984) and others, an unexpected change
in interest rates can affect the firm either positively or negatively, depending on the
relative durations of its assets and liabilities. To control for the firm’s interest rate
exposure, we include in our regressions the percentage change in the short-term
interest rate over the horizon being considered for exposure.
Finally, following Chow et al. (1997a,b), we control for the influence of other
economy-wide risk factors by including in our regressions a measure of the term
premium on risk-free debt and the dividend yield on a broad market index. Fama and
French (1989) have shown that these variables follow the same cyclical patterns as
those found in stock returns. Therefore, they are effective proxies for the unobservable
cyclical factors that influence stock returns.
For each firm in our sample, we estimate currency exposures over a J-period
horizon from the following regression:
Rt,J = b0,J + be,J · Et,J + by,J · Yt,J + bu,J · Ut,J + bINT,J · INT t,J
+ bTPR,J · TPRt + bDYL,J · DYLt + ut,J ,
(1)
where Rt,J is the return on the firm over the horizon (t, t + J), Et,J is the percentage
change in the exchange rate of the ECU with respect to the home currency of the firm
over this horizon, Yt,J is the percentage change in the yen exchange rate and Ut,J is
the percentage change in the US dollar exchange rate. The coefficients of these three
variables measure the firm’s exposure to the ECU, yen and US dollar, respectively.
The remaining variables serve to isolate the true impact of exchange rates on the
firm’s returns. INT t,J is the percentage change in the short-term interest rate over the
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horizon (t, t + J), TPR is the term-premium on default-free debt at date t and DYL is
the dividend yield on a market index at date t, all taken from the firm’s home market.
An alternative way to capture the systematic factors represented by TPR and
DYL would be through a residual market return, constructed by orthogonalizing a
broad market return with respect to the exchange rates and interest rate variables
already included in the regression. Intuitively, this residual market return captures the
influence of systematic factors not already included in the regression. This intuition
has been formalized by Wei (1988), who shows that under certain assumptions about
error terms, a residual market return ensures the exactness of a linear factor model
when some factors are unobservable. In terms of the residual market return, the
regression for estimating currency exposure becomes:
Rt,J = b0,J + be,J · Et,J + by,J · Yt,J + bu,J · Ut,J + bINT,J · INT t,J
+ bM,J · R̂mt,J + ut,J ,
(2)
where R̂mt is the return on a broad market index, orthogonalized with respect to Et,J ,
Yt,j , Ut,J and INT t,J .
As a robustness check on our results using equation (1), we re-estimated currency
exposures using equation (2). The estimates of exposure coefficients proved to be
strikingly similar for both models. Therefore, we have reported the results only for
the first equation.
2.2. Sample and data
Our analysis focuses on the currency exposure of 90 large European firms from
1 January 1987 to 31 December 1998. Of these firms, 30 are from the UK, 30 are
from France and 30 are from Germany. These firms are drawn from the constituents
of the British FT 30 Index, the French CAC 40 Index and the German DAX Index as
of December 1999.
The J-period return on a sample firm, Rt,J , is computed as an arithmetic return
adjusted for dividends and stock splits. Percentage changes in the exchange rate are
computed over the same horizon for sterling, deutschmark and the French franc (the
home currencies of our sample firms) against the ECU, yen and US dollar. The data
on stock returns and exchange rates were drawn from DataStream.
The interest rate variable used in J-period regressions is the percentage change
in the 1 month interest rate over the J-period horizon. For UK firms, we use the
1 month treasury bill yield to maturity, inferred from the discount yields reported in
DataStream. For French and German firms, we use the local 1 month money market
rate, also gathered from DataStream.
The term premium on date t is the first of two indices used to reflect ex ante
expectations about cyclical factors. For UK firms, the term premium is measured
as the difference between the 10 year benchmark yield on treasury instruments
and the 1 month treasury bill rate. For French and German firms, this premium
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is defined as the difference between the 10 year benchmark yield and the 1 month
money market rate. All data are drawn at a daily frequency from DataStream.
The dividend yield on an aggregate stock market index is our second benchmark
of cyclical expectations. For UK firms, we use the dividend yield on the Financial
Times Stock Exchange All-Share Index. For French and German firms, we use the
yield on the DataStream Total Market Index. As before, the data are collected at a
daily frequency from DataStream.
3. Estimates of exchange rate exposure
To begin our analysis, we estimate exchange rate exposure over monthly horizons
(20 trading days) for our 90 sample firms using equation (1). We construct overlapping
time series for all variables that are measured over monthly horizons; that is, stock
returns, currency returns and the percentage growth in interest rates. In this respect,
our analysis differs from much of the extant published literature, which use a single
non-overlapping time series in estimating exposure.
Our rationale for using overlapping returns is that it enhances the use of sample information in constructing an exposure estimate. By contrast, any single nonoverlapping series of monthly returns uses only approximately 1/20th of all the
observations available within the sample period. In fact, it is possible to construct 20
separate non-overlapping series of monthly returns within the same sample period.
Table 1 illustrates the contrast between overlapping and non-overlapping data series
when estimating exposure. Using a single overlapping returns series that incorporates
all data observations for the sample period, we find that the UK firm Cadbury has
a significantly positive dollar exposure and a significantly negative ECU exposure.
If instead we had used a single non-overlapping series of 20 day returns, we would
have failed to find significant dollar exposure in 70 per cent of the separate nonoverlapping returns series we could have constructed within our sample period, and
significant ECU exposure in 50 per cent of these series. Moreover, the magnitude of
ECU exposure would have ranged from −0.29 to −0.94, depending on the particular
sample chosen and the magnitude of dollar exposures between −0.055 and 0.499.
Similar results are found for the two other firms reported in Table 1, British Telecom
and Boots. Therefore, a particular series of non-overlapping returns can give very
misleading inferences about the exposures embedded in the entire sample.
3.1. Direction and significance of exposure: monthly horizons
Panel A in Table 2 summarizes our estimates of exposure coefficients across all
sample firms. Newey and West (1987) t-statistics have been used for parameter
inferences to control for the autocorrelation in overlapping returns series.
The most striking feature of these coefficients is the strong and uniform evidence of exposure of sample firms to the US dollar. Virtually every sample firm in
all three countries is significantly exposed to dollar fluctuations, and in all cases,
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Table 1
Non-overlapping versus overlapping data: implications for exposure estimates
Non-overlapping
Firm
Mean(β)
σ (β)
Overlapping
Maximum
Minimum
Percentage significant
Coefficient
t-statistic
Panel A: ECU exposure
BT
−0.557
0.153
Cadbury −0.595
0.166
Boots
−0.677
0.160
−0.290
−0.290
−0.446
−0.924
−0.941
−1.041
0.35
0.50
0.60
−0.568
−0.601
−0.669
−4.80
−5.25
−6.25
Panel B: Yen exposure
BT
−0.127
0.163
Cadbury
0.101
0.135
Boots
−0.118
0.131
0.195
0.396
0.195
−0.351
−0.102
−0.296
0.05
0.00
0.05
−0.113
0.105
−0.122
−1.79
1.45
−1.93
Panel C: Dollar exposure
BT
0.365
0.133
Cadbury
0.262
0.162
Boots
0.352
0.140
0.546
0.499
0.543
0.066
−0.055
0.019
0.50
0.30
0.60
0.359
0.267
0.348
5.27
3.38
4.83
This table contrasts monthly exposures estimated from non-overlapping data with those estimated from
overlapping data for three UK firms: British Telecom (BT), Cadbury Schweppes (Cadbury) and Boots.
A monthly return is defined as spanning 20 trading days. Within the sample period from 1 January
1987 to 31 December 1998, 20 separate non-overlapping estimates of exposure are constructed for each
firm by incrementing the starting date of the monthly returns series 1 day at a time. The first panel of
columns presents summary statistics of these non-overlapping estimates. The second panel of columns
reports the exposure estimate and t-statistic derived from a single overlapping time series of monthly
returns constructed over the full sample period. The t-statistic is estimated from the Newey–West sample
covariance matrix.
the coefficient of exposure is positive. In economic terms, a depreciation of the
home-currency against the US dollar raises the returns on these firms. This is consistent with the possibility that, in all three countries, sample firms are exposed to the
US dollar predominantly through the revenues they earn, rather than through their
costs of production or their costs of capital.
Among UK firms, exposure to the ECU is nearly as widespread as exposure to the
dollar, but the direction of exposure is diametrically opposed. Of UK firms, 87 per cent
in our sample significantly lose value when sterling depreciates against the ECU, as
indicated by the negative sign on ECU coefficients. This evidence suggests that the
production processes of major UK firms are so deeply integrated with continental
European economies that their costs of production and capital are more sensitive to
European currencies than their revenues.
Exposure to the ECU is generally weaker in Germany and France than in the
UK, with only 23 per cent of French firms and 27 per cent of German firms showing
significant ECU coefficients. Given the importance of European economies for our
sample firms, these modest levels of exposure appear to be best explained by the
conjecture that German and French firms are exposed to similar degrees on both
their revenues and their costs, giving them a natural hedge against other European
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Table 2
Exchange rate exposures of sample European firms at monthly returns horizons
Panel A
British
French
German
ECU
French
German
USD
INT
DYL
TPR
R2
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
−0.597
−0.602
0.271
0.87
0.00
0.048
0.063
0.122
0.00
0.17
0.534
0.544
0.205
0.00
0.97
−0.139
−0.117
0.110
0.63
0.00
0.625
0.511
0.889
0.03
0.40
−0.001
−0.017
0.235
0.23
0.20
0.08
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
−0.340
−0.368
0.300
0.23
0.00
0.051
0.041
0.106
0.00
0.13
0.623
0.600
0.234
0.00
1.00
−0.088
−0.089
0.042
0.90
0.00
1.426
1.401
1.519
0.03
0.53
0.093
0.071
0.364
0.20
0.27
0.08
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
−0.085
−0.042
0.462
0.27
0.17
−0.057
−0.056
0.132
0.23
0.03
0.647
0.598
0.213
0.00
0.97
0.032
0.004
0.083
0.03
0.27
0.364
0.637
1.258
0.07
0.13
−0.052
−0.031
0.224
0.13
0.00
0.08
Panel B
British
Yen
DYL
TPR
R2
FXI
INT
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
−0.007
−0.059
0.360
0.20
0.23
−0.121
−0.147
0.123
0.57
0.00
1.972
2.282
0.977
0.00
0.80
0.162
0.135
0.218
0.03
0.37
0.05
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
0.850
0.844
0.567
0.00
0.70
−0.040
−0.040
0.034
0.33
0.00
1.494
1.410
1.212
0.03
0.67
−0.176
−0.236
0.281
0.40
0.07
0.05
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
0.876
0.821
0.616
0.00
0.73
0.039
0.032
0.056
0.04
0.31
0.998
1.001
1.098
0.00
0.46
−0.197
−0.214
0.273
0.54
0.04
0.05
This table presents summary statistics for currency exposure and interest rate exposure for a sample
of 90 European firms, drawn from the UK, France and Germany. Exposure coefficients are estimated
from equation (1), with returns and other rates of change measured over monthly horizons, using data
from 1 January 1987 to 31 December 1998. INT is the percentage change in the short-term interest
rate over the corresponding monthly horizon; TPR the term-premium on default-free debt; and DYL
the dividend yield on a market index, all taken from the firm’s home market. ECU, European currency
unit; FXI, a composite trade-weighted index of the ECU, yen and the dollar; USD, US dollar. Summary
statistics for regression coefficients, as well as the average adjusted R2 , are computed by country. The
terms ‘Negative, 5%’ and ‘Positive, 5%’ represent the percentage of sample firms from each country
whose coefficients are significantly negative or positive at 5 per cent, using Newey–West standard
errors.
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currencies. For German firms, this conjecture is borne out by the relatively even
distribution of positive and negative exposures among those firms that are significantly
exposed.3
Exposure to the yen is generally weak for firms in all three countries, with less
than one-quarter of firms in each country-sample showing significant exposure coefficients to the yen. UK and French firms, when exposed to the yen, have positive
exposure coefficients that indicate that these firms gain value when their home currency depreciates against the yen. However, virtually all German companies with
significant exposures to the yen have negative exposure coefficients, indicating that
they lose value when the deutschmark depreciates against the yen. This suggests that
the outsourcing of production processes to the Pacific Basin by German companies
has created a much stronger cost side exposure than is being compensated by revenues
from this region.
The quality of these exposure estimates depends upon how well our regressions
control for the other systematic risk factors influencing returns. The most significant
of these are interest rate shocks, which have a close economic relationship with
exchange rate movements. Table 2 reveals that our measure of proportionate monthly
shocks to interest rates, INT, has significant explanatory power for the returns of the
majority of sample firms in the UK and France. Interest rate exposure is significantly
negative for 63 per cent of UK firms and 90 per cent of French firms, suggesting that
a rise in interest rates is associated with a drop in the value of these firms’ equity.
German firms, by contrast, show weaker exposures to interest rates over monthly
horizons. It is possible that this is because of a relatively close match between the
average durations of nominal assets and nominal liabilities (Flannery and James,
1984).
The term premium, TPR, and the market dividend yield, DYL, have been included
to capture the state of the business cycle. In all three countries, these variables appear
to have explanatory power for a significant proportion of sample firms. The dividend
yield is significant for 43 per cent of firms in the UK, 56 per cent of firms in France
and 20 per cent of firms in Germany, and in most cases has a positive relationship
with stock returns as expected. Similarly, the term premium is significant for nearly
50 per cent of UK and French firms and 13 per cent of German firms. Collectively,
these terms do appear to be capturing the influence of macroeconomic cycles on stock
returns.
3 Another possible explanation for the low significance of the ECU among German and French
firms is that the franc and the deutschmark were closely linked, and were both significant
components of the ECU basket. Therefore, franc/ECU and deutschmark/ECU rates were less
volatile than the corresponding exchange rates against the dollar, making it potentially harder
to detect ECU exposures. In our sample period, the standard deviation of the monthly return on
the sterling/ECU rate was 0.0085, more than twice as high as that of the franc/ECU (0.0039)
and the deutschmark/ECU (0.0043) rates.
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3.2. Exposure to a composite foreign exchange index
Many previous studies of exposure analyse foreign currency risk by estimating a
firm’s exposure to a single composite exchange rate index rather than to several individual exchange rates separately, as we have done. This approach has the advantage
of convenience: no prior decisions have to be made on which particular currencies a
firm might be exposed to, and currency exposure itself can be represented by a single
estimate.
However, comparison of the results in Panels A and B in Table 2 suggests that
this convenience might come at a high price. Virtually all UK and German firms are
positively exposed to the US dollar. However, most UK firms are negatively exposed
to the ECU and nearly one-quarter of our sample German firms are negatively exposed
to the yen. When these currencies are brought together in a composite exchange rate
index, it is possible that the firm’s positive exposure to one currency will cancel
out its negative exposure to the other. Therefore, exposure to a composite exchange
rate index might be statistically insignificant, leading to the inference of no currency
exposure, even though the firm is strongly exposed to individual components of the
index.
To test this conjecture, we constructed a composite trade-weighted index of the
ECU, the yen and the dollar, denoted FXI, for each of the three countries in our sample.
In the UK currency index, for example, the dollar is weighted by the average share
(over the sample period) of the USA in the UK’s total quarterly foreign trade with the
USA, Japan and European Union (EU) nations. The ECU and the yen are weighted
by their corresponding trade weights. The currency indices of France and Germany
are constructed similarly.4
Panel B in Table 2 summarizes the exposure of our sample firms to this composite
exchange rate index. Among UK firms, evidence of exchange rate exposure weakens
dramatically when inferred from the coefficients of the composite index FXI. Only
43 per cent of UK firms are exposed to the currency index, although 97 per cent are
known to be exposed to the dollar and 87 per cent to the ECU. There is a similar
decline in the percentage of French and German firms exposed, although the decline
is less pronounced than for UK firms. In all three countries the average adjusted R2
of the exposure regression is reduced when individual currencies are replaced with
the exchange rate index. Therefore, our results suggest that the weak evidence of
exposure that has characterized most of the existing published literature might, at
least in part, be a consequence of the measuring exposure with respect to composite
exchange rate indices rather than individual currencies.
4 Quarterly
data on the foreign trade of the UK, France and Germany were collected from the
National Government Statistics series on DataStream. By focusing just on each nation’s trade
against the USA, Japan and the EU, we ensure that our trade weights add up to 1. The series
for Germany includes only West Germany before 1992. We are ignoring trade with nations
outside these three areas because the magnitude of this trade is small. It would have only a
minor impact on the index, and would not influence the conflicting exposures embedded within
our index, which is our main interest.
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The problems in using an exchange rate index are not confined to situations where
the firm has conflicting exposures to different currencies. Even when the firm’s
exposure to all currencies is of the same sign, the use of a composite exchange
rate index is less informative than an unconstrained multi-currency regression. Let
wj represent the weight of currency j in the composite index of a particular firm
and b e , b y and b u its exposure coefficients to the ECU, the yen and the dollar.
The three individual currencies in equation (1) can be replaced by a single index
(we Et + wy Yt + wu Ut ) without loss of information only in the special case where:
by
be
bu
=
=
.
we
wy
wu
(3)
We tested the linear constraints implied by equation (3) for all the firms in our sample
using a Wald test, constructing the test statistic with the Newey–West covariance
matrix. The restrictions were rejected at the 5 per cent significance level for all but 1
UK firm, 6 French firms and 8 German firms. In other words, using a composite currency index results in a statistically significant loss of information for approximately
83 per cent of all firms in our sample (75 out of 90).
The exposure coefficients reported in Table 2 have been estimated over a period
that spans major structural changes in the British and German economies, including
UK’s forced withdrawal from the European Monetary System and the unification
of Germany. We tested for structural shifts in the exposure of our sample firms
using cumulated sum of squares (CUSUM) tests of equation (1) on each firm in the
sample (see, e.g., Greene, 1997). Although we found considerable evidence of shifts
the CUSUM statistic returns within its 5 per cent bounds after first breaking out,
suggesting that the structural shifts are temporary and episodic. We also segmented
the sample into two equal time periods and estimated exposure for each sub-period.
Where significant, the median exposures within sample countries were of the same
sign, although the magnitudes fluctuated across the sample.5 For subsequent tests we
have retained the full sample.
3.3. Interval dependence in exposure coefficients
We have so far estimated exposure coefficients over monthly returns horizons,
in keeping with the majority of prior studies, such as Jorion (1990), Bodnar and
Gentry (1993) and Choi and Prasad (1995). However, this choice of horizon is purely
arbitrary. Unlike the cash flow from a particular transaction, which is exposed until
its stipulated date of receipt, the value of the firm has no obvious horizon of exposure.
Therefore, it is natural to examine whether the estimates of a firm’s currency exposure
will differ as we consider different returns horizons over which to measure them.
5 The
results on stability tests are available from the authors on request.
C AFAANZ, 2005
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W. Rees, S. Unni / Accounting and Finance 45 (2005) 479–497
Prior empirical evidence suggests that estimates of exposure might, indeed, be
dependent upon the returns horizon used. Bartov and Bodnar (1994) find that quarterly
US portfolio returns are largely insensitive to contemporaneous exchange rates, but
are significantly affected by lagged changes in an exchange rate index. Allayanis
(1995) reported similar findings from a sample of US industry portfolios, but at
monthly lags.
These findings suggest that investors impound the effect of a currency shock into
stock prices only with a temporal lag. A plausible reason for the existence of such
a lag might be that investors wait for the firm’s next earnings announcement before
fully assessing how a recent systematic movement in exchange rates has affected
the firm’s value. This gradual adjustment hypothesis is consistent with a growing
body of long-horizon event study evidence, which suggests that investors gradually
revise their assessment of a corporate event over time as its impact on cash flows is
revealed.6
In the presence of such gradual adjustment, we should expect that at relatively
low returns horizons, before investors have had the opportunity to incorporate the
full implications of a systematic currency movement into stock prices, exposure
coefficients should be low in magnitude because stock prices are likely to respond
only weakly to currency shocks. However, as the returns horizon is lengthened and
investors have the opportunity to learn from additional corporate disclosures, exposure
coefficients might increase in absolute magnitude until the returns horizon is long
enough to allow for investors to have fully incorporated the impact of the currency
movement into stock prices.
The path taken by exposure coefficients as they adjust to their ‘true’ level as the
returns horizon increases can depend upon the manner in which investors respond
to the gradual refinement of their information sets. If stock prices reflect currency
information with progressively greater precision over time, exposure coefficients
will converge monotonically to their true levels. Instead, if investors systematically
overreact to early disclosures of information, exposure coefficients might initially
overshoot their true levels at intermediate returns horizons before gradually declining
(in absolute magnitude) to their true levels.
This gradual incorporation of news into prices might also lead to reversals in the
sign of exposure coefficients. If the revenue implications of the currency shock are
systematically revealed earlier than its cost implications, and if investors fail to recognize this pattern over time, markets might initially react positively to a depreciation in
the currency, producing a positive short-horizon exposure coefficient. However, as the
full cost implications of the shock become clear over time, investors will revise their
valuation of the stock downwards. If these cost implications are sufficiently great, the
long-run reduction in stock prices might be so great that exposures estimated over
long horizons are actually negative. The opposite change in sign will be observed if
6 See, for instance, Michaely et al. (1995) on dividend initiations and omissions, Loughran
and Ritter (1995) on seasoned equity issues and Michaely and Womack (1996) on analyst
recommendations. Daniel et al. (1998) offer a complete list of such studies.
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W. Rees, S. Unni / Accounting and Finance 45 (2005) 479–497
491
the cost implications of the shock are impounded into prices sooner than the revenue
implications.
To understand how our estimates of exposure vary with the returns horizon, we
estimated equation (1) for returns horizon ranging from 1 to 80 trading days, in increments of 1 day. For each horizon of J days, we construct an overlapping time series
of J-period returns for all sample firms, and the corresponding J-period overlapping
rates of change in the ECU, yen and dollar exchange rates and in the 1 month interest
rate (for INTR). The term premium and the dividend yield are not interval-dependent
by construction and, therefore, are simply observed for each day ‘t’ in the sample
period, regardless of the returns horizon being used. Using this data, we then estimate
regression (equation (1)) at each returns horizon from 1 to 80 days for all firms in our
sample.
Table 3 summarizes the results for UK firms. Consistent with the gradual adjustment hypothesis, the average exposure of UK firms to the ECU and the US dollar
increases progressively in absolute magnitude as the returns horizon is increased.
The percentage of firms for which these exposures are significant remains relatively
unchanged across the different horizons. The average yen exposure also strengthens
over longer returns horizons, becoming significantly negative for more than half the
sample firms at horizons of over 60 trading days.
French and German sample firms, described in Tables 4 and 5, display the same
monotonically increasing pattern of US dollar exposures that was seen in UK firms,
with the number of significantly exposed firms remaining high at all returns horizons.
Yen exposures become progressively more negative at longer returns horizons, with a
greater number of firms displaying such exposure. However, ECU exposures display
a change not only in magnitude, but also in sign as the returns horizon is lengthened.
Using daily data, ECU exposures are negative for 70 and 73 per cent, respectively,
of the sample of German and French firms. However, as the horizon extends the
number of positively exposed firms increase. At the longest horizons we examine, 80
trading days, positive exposure dominates with some 50 per cent of firms displaying
significant exposure in both countries.
It is only in the case of ECU exposure for UK firms that we see exposure coefficients
converging to a stable level (on average) as the returns horizon extends beyond
60 days. In all other cases, we find average exposure coefficients continuing to rise
perceptibly in absolute magnitude even at horizons of 80 trading days, the longest
we consider. It is, therefore, tempting to consider longer returns horizons in the hope
of finding an ultimate convergence towards the true exposure coefficients.
However, recent research on long-horizon returns regressions demonstrates that
standard regression methods can lead to spurious inferences regarding both the magnitude of estimated coefficients and their statistical significance if returns horizons are
extended to the point where they represent a non-trivial fraction of the total sample
size. The basic intuition behind this result, as Valkanov (2003) notes, is that even if
a particular returns series is integrated of order zero, a rolling sum of these returns
asymptotically behaves like a series integrated of order 1 – and, thereby, produces
apparent evidence of persistent stochastic behaviour – when these rolling sums are
C AFAANZ, 2005
492
Returns horizon (in trading days)
Variable
Statistic
1
5
10
20
30
40
50
60
70
80
ECU
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
−0.142
−0.136
0.055
0.63
0.00
−0.383
−0.359
0.164
0.90
0.00
−0.388
−0.331
0.225
0.70
0.00
−0.597
−0.602
0.271
0.87
0.00
−0.654
−0.688
0.322
0.83
0.00
−0.744
−0.760
0.398
0.83
0.00
−0.775
−0.741
0.458
0.83
0.00
−0.797
−0.738
0.516
0.83
0.00
−0.780
−0.722
0.559
0.83
0.00
−0.777
−0.820
0.591
0.83
0.03
Yen
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
−0.011
−0.008
0.050
0.03
0.00
0.122
0.121
0.116
0.00
0.37
0.083
0.065
0.121
0.00
0.23
0.048
0.063
0.122
0.00
0.17
−0.008
−0.046
0.138
0.07
0.17
−0.028
−0.066
0.166
0.17
0.17
−0.063
−0.122
0.185
0.37
0.13
−0.090
−0.151
0.201
0.53
0.07
−0.153
−0.195
0.226
0.57
0.03
−0.205
−0.239
0.243
0.57
0.03
USD
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
0.220
0.185
0.110
0.00
0.83
0.317
0.274
0.157
0.00
0.90
0.404
0.398
0.183
0.00
0.90
0.534
0.544
0.205
0.00
0.97
0.622
0.563
0.232
0.00
0.97
0.678
0.578
0.246
0.00
1.00
0.731
0.599
0.264
0.00
1.00
0.780
0.640
0.285
0.00
1.00
0.824
0.698
0.301
0.00
1.00
0.856
0.701
0.308
0.00
1.00
0.01
0.03
0.05
0.08
0.11
0.14
0.16
0.19
0.21
0.23
R2
This table presents summary statistics for the currency exposures of a sample of 30 UK firms, estimated from equation (1), for measurement horizons ranging from 1
to 80 trading days. Regressions use overlapping returns from 1 January 1987 to 31 December 1998. The terms ‘positive, 5%’ and ‘negative, 5%’ measure the percentage of firms whose coefficients are significantly positive or negative at 5 per cent, using the Newey–West standard errors. ECU, European currency unit; USD, US dollar.
W. Rees, S. Unni / Accounting and Finance 45 (2005) 479–497
C AFAANZ, 2005
Table 3
Exchange rate exposure of UK firms over different returns horizons
Returns horizon (in trading days)
Variable
Statistic
1
5
10
20
30
40
ECU
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
−0.185
−0.210
0.079
0.73
0.00
−0.201
−0.205
0.125
0.17
0.00
−0.185
−0.188
0.173
0.17
0.00
−0.340
−0.368
0.300
0.23
0.00
−0.215
−0.213
0.416
0.17
0.00
−0.149
−0.124
0.507
0.17
0.03
0.021
0.059
0.559
0.13
0.07
0.303
0.346
0.647
0.10
0.20
0.600
0.646
0.774
0.07
0.33
0.892
0.861
0.941
0.07
0.50
Yen
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
0.002
−0.012
0.054
0.00
0.00
0.082
0.051
0.095
0.00
0.20
0.069
0.045
0.106
0.00
0.13
0.051
0.041
0.106
0.00
0.13
−0.011
−0.022
0.139
0.13
0.10
−0.028
−0.024
0.175
0.23
0.13
−0.063
−0.063
0.189
0.23
0.13
−0.121
−0.134
0.215
0.40
0.13
−0.229
−0.258
0.226
0.63
0.07
−0.331
−0.385
0.239
0.77
0.00
USD
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
0.307
0.312
0.125
0.00
0.93
0.370
0.367
0.120
0.00
0.93
0.455
0.484
0.163
0.00
0.97
0.623
0.600
0.234
0.00
1.00
0.773
0.728
0.281
0.00
1.00
0.886
0.823
0.320
0.00
1.00
0.959
0.897
0.345
0.00
1.00
1.036
0.949
0.371
0.00
1.00
1.130
1.043
0.381
0.00
1.00
1.183
1.070
0.394
0.00
1.00
0.01
0.03
0.05
0.08
0.11
0.15
0.17
0.20
0.23
0.25
R2
50
60
70
80
This table presents summary statistics for the currency exposures of a sample of 30 French firms, estimated from equation (1), for measurement horizons ranging from
1 to 80 trading days. Regressions use overlapping returns from 1 January 1987 to 31 December 1998. The terms ‘positive, 5%’ and ‘negative, 5%’ measure the percentage of firms whose coefficients are significantly positive or negative at 5 per cent, using the Newey–West standard errors. ECU, European currency unit; USD, US dollar.
W. Rees, S. Unni / Accounting and Finance 45 (2005) 479–497
C AFAANZ, 2005
Table 4
Exchange rate exposure of French firms over different returns horizons
493
494
Returns horizon (in trading days)
Variable
Statistic
1
5
10
20
30
ECU
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
−0.189
−0.193
0.061
0.70
0.00
−0.240
−0.246
0.112
0.33
0.00
−0.065
−0.059
0.188
0.03
0.03
−0.085
−0.042
0.462
0.27
0.17
0.035
−0.003
0.635
0.27
0.33
0.224
0.207
0.760
0.20
0.43
0.362
0.205
0.818
0.13
0.47
0.441
0.360
0.906
0.13
0.50
0.544
0.673
1.003
0.13
0.50
0.666
0.842
1.105
0.13
0.53
Yen
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
0.000
0.004
0.044
0.03
0.00
0.045
0.040
0.079
0.00
0.03
0.038
0.023
0.097
0.00
0.00
−0.057
−0.056
0.132
0.23
0.03
−0.165
−0.170
0.154
0.53
0.00
−0.233
−0.247
0.164
0.63
0.00
−0.274
−0.269
0.168
0.70
0.00
−0.314
−0.314
0.182
0.77
0.00
−0.402
−0.405
0.196
0.87
0.00
−0.482
−0.486
0.208
0.90
0.00
USD
Mean
Median
Standard deviation
Negative, 5%
Positive, 5%
0.216
0.213
0.075
0.00
0.87
0.385
0.353
0.146
0.00
0.93
0.460
0.453
0.186
0.00
0.97
0.647
0.598
0.213
0.00
0.97
0.790
0.782
0.236
0.00
1.00
0.896
0.882
0.251
0.00
1.00
0.952
0.918
0.258
0.00
1.00
1.001
0.980
0.269
0.00
1.00
1.047
1.023
0.282
0.00
1.00
1.065
1.027
0.296
0.00
1.00
0.01
0.03
0.04
0.08
0.12
0.16
0.19
0.21
0.23
0.25
R2
40
50
60
70
80
This table presents summary statistics for the currency exposures of a sample of 30 German firms, estimated from equation (1), for measurement horizons ranging
from 1 to 80 trading days. Regressions use overlapping returns from 1 January 1987 to 31 December 1998. The terms ‘positive, 5%’ and ‘negative, 5%’ measure
the percentage of firms whose coefficients are significantly positive or negative at 5 per cent, using the Newey–West standard errors. ECU, European currency unit;
USD, US dollar.
W. Rees, S. Unni / Accounting and Finance 45 (2005) 479–497
C AFAANZ, 2005
Table 5
Exchange rate exposure of German firms over different returns horizons
W. Rees, S. Unni / Accounting and Finance 45 (2005) 479–497
495
constructed over horizons that constitute a ‘significant’ portion of the sample size.
The standard estimators for slope coefficients might not be consistent and t-statistics
do not converge to well-defined distributions, therefore casting doubt both upon the
estimated coefficients of the regression and upon any inferences about their statistical
significance.
Valkanov (2003) and Torous et al. (2005), among others, have proposed alternative
inference techniques that allow for unit-root, or ‘local-to-unity’, data generating
processes. However, besides being computationally intensive, these techniques are
based upon models with only a single regressor (besides a constant), whereas a wellspecified statistical model of exposure should, as we have noted, allow for the separate
influence of several currencies in addition to other control variables. In another
strand of this research, Ang and Bekaert (2004) have proposed that the standard
errors proposed by Hodrick (1992), which allow for multiple regressors, retain their
correct size even in small samples of long-horizon regressions. However, these results
have been established within the context of a particular structural model that relates
stock returns to dividend yields. It is as yet unclear whether these results will also
emerge in a well-specified structural model relating exchange rate movements to stock
prices.
Therefore, it would be useful to develop statistical models and robust inferential
methods for estimating exchange rate exposures over longer horizons than those
investigated in the present paper, so that we can gain a better understanding of the
true economic exposure of firms to exchange rates and establish reliable models to
estimate this true exposure.
4. Concluding remarks
The present paper makes several contributions to the empirical published literature on exchange rate exposure. It offers, for the first time, evidence of pervasive
exposures for a sample of large European firms, drawn from the UK, France and
Germany. Exchange rate exposure is shown to be widespread among European firms:
considerably more widespread than would have been thought from previous North
American results and most studies including European data. Moreover, the patterns of
exposure are shown to be complex. The majority of UK firms gain when the sterling
depreciates against the dollar, but lose when it depreciates against the ECU. Several
German firms have similarly conflicting exposures to the dollar and the yen. This
insight regarding conflicting exposures and the high incidence of significant exposure
is discovered by the use of multiple exchange rates rather than the conventional composite index. We also document that the reliability of exposure estimates is improved
by the use of overlapping measurement intervals.
The present paper also demonstrates that estimates of a firm’s exposure are sensitive
to the returns-interval used in estimating them. The magnitude of exposure changes
significantly as the returns horizon is extended from 1 to 80 trading days. The pattern
of this change is not necessarily monotonic: for some firms, exposure changes sign
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W. Rees, S. Unni / Accounting and Finance 45 (2005) 479–497
as the returns horizon increases. These results are consistent with the hypothesis that
share prices adjust gradually to exchange rate movements.
This evidence casts doubt on the conventional approach of estimating exposures
with monthly returns. It also raises the question of how to estimate the true exposure
of firms. The ECU exposure of our sample UK companies converge to a stable level
for returns horizons of approximately 60 trading days, suggesting that this stable
level can be interpreted as the true exposure of these firms to the ECU. However,
the exposures of our sample firms to other currencies (and of French and German
firms to the ECU) continue to change perceptibly with the returns horizon even at
horizons of 80 trading days. Given the dangers of extending conventional inferential techniques for very long horizons, we believe that the behaviour of exchange
rate exposures at longer returns horizons can be evaluated only after developing
theoretical or simulation evidence on the behaviour of alternative inferential methods at longer horizons. We feel this issue presents an interesting avenue for further
research.
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