Measuring International Economic Linkages with Stock Market Data

Board of Governors of the Federal Reserve System International Finance Discussion Papers Number 449

August 1993

MEASURING INTERNATIONAL ECONOMIC LINKAGES WITH STOCK MARKET DATA

John Ammer and Jianping Mei

NOTE: International Finance Discussion Papers are preliminary materials circulated to stimulate discussion and critical comment. References in publications to International Finance Discussion Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors.

ABSTRACT

The covariance between domestic and foreign equity return innovations is decomposed into components associated with news about future real and financial variables. In an application to fifteen national stock markets, we find that news about future dividend growth tends to be more highly correlated than contemporaneous output measures, suggesting that there are lags in the international transmission of real economic shocks. In addition, results from a longer sample period suggest that both real and financial linkages between the U.S. and the U.K. appear to have increased after the Bretton Woods currency arrangement was

abandoned in the early 1970's.

Measuring International Economic Linkages with Stock Market Data

John Ammer and Jianping Mei’

1. Introduction

An important issue in international economics is the degree of integration among different economies. Much of the literature in this area has concentrated on measuring international financial integration.” Some other recent studies (e.g., Stockman and Svensson (1987) and Phillips (1990)) have explored linkages between real economic variables in different countries. In this paper, we develop a framework in which one can measure both financial and real economic integration by characterizing components of covariation between returns on national stock

markets.

1 The first author is a staff economist in the International

Finance Division of the Federal Reserve Board and the second author is Assistant Professor of Finance in the Stern School of Business at New York University. Opinions expressed herein do not necessarily concur with the Federal Reserve Board or any other employees of the Federal Reserve System. The authors would like to thank Gordon Bodnar, Joe Gagnon, Matt Pritsker, and audience participants at the Federal Reserve Board and the 1993 WEA meetings for helpful discussions. The authors are also grateful to Stephen Brown for providing some of the international stock market data and Tina Sun for compiling some of the macroeconomic data. Some of the UK stock market data used in the analysis herein were extracted from the London Share Price Database, which is a copyright work of the London Business School.

2 See, for example, Feldstein and Horioka (1980), Wheatley (1988), Gultekin, Gultekin, and Penati (1989), King, Sentana, and Wadhwani (1990), Campbell and Hamao (1992), and Bekaert and Hodrick (1992).

The intuition behind our approach is very simple. By using the Campbell and Shiller (1988) approximate present value model, we can decompose excess stock return innovations for different countries into news about future excess returns, dividend growth rates, interest rates, and exchange rates. By studying the co-movements of these different excess return components among various countries, we can characterize the relative importance of international linkages between different sectors of the world's

economies.

To be more specific, we measure real economic integration by calculating the correlations of dividend innovations among different countries. Ina fully integrated economic system, labor and capital would be able to move freely across naticnal borders. International differences in technology and production costs should vanish. Accordingly, a common shock would have a similar impact on economic growth, and thus corporate earnings and dividends, in different countries. We measure the degree of financial integration through calculating the correlations between innovations in future expected returns of different countries. As noted by Campbell and Hamao (1992), if asset returns in different countries are generated by an international multivariate linear factor model, the conditional means of these asset returns must move in tandem, as linear combinations of some common risk premiums. In the extreme case of a one-factor model,

any variation over time in mean returns would have to be

perfectly correlated across assets.? Thus, if national financial markets are highly integrated, we should find high

correlations between future expected return innovations.

There are several distinctive advantages of our approach. First, by relying more on financial market data than on macroeconomic data, we likely encounter fewer problems with measurement error. Second, by examining the co-movement of future return news aggregated over a long horizon instead of the

co-movement of one-period expected returns,“

our study coulda detect small but persistent co-movements in expected returns, and more accurately measure the degree of financial integration. Similarly, by using innovations in long-term dividend growth as our proxy for the real economy, we can pick up the common effects of real shocks that impact output in two countries with different lags. In addition, by examining the covariation in innovations in particular variables rather than changes in those variables over time, we make the distinction between co-movements of expected and unexpected changes. Finally, we integrate the stock market, the money market, the goods market, and the foreign exchange: market naturally into a single unified system, making it 3 Tests for the number of factors in an APT model typically reject a single factor specification in favor of a multiple factor alternative, but usually a single factor can explain most of the common variations. More to the point, a statistically significant

risk premium is often estimated for only one factor (for example, see Connor and Korajczyk (1988)).

4 See, for instance, Campbell and Hamao (1992) or Bekaert and Hodrick (1992).

possible to study their interactions without many ad hoc

assumptions.

The paper is divided into five sections. In the next section, we present an approximate present value model in which we decompose excess returns into four different components: innovations (or news) about dividend growth, interest rates, exchange rates, and future expected returns. This framework is a variant of those derived by Campbell (1991), Campbell and Aimer (1993), and Campbell and Mei (1992). The third section discusses an application to American and British data, under both fixed and floating nominal exchange rate regimes. In the following section, we investigate interactions among 15 industrialized countries in the post-Bretton Woods era. The final section

summarizes our conclusions.

2. Decomposing Domestic and Foreign Stock Returns

We first use an excess return version of the Campbell (1991) approximate present value relation to characterize the innovation in the domestic stock return as news about future dividends,

interest rates, and equity risk premiums:>

Ora = (Ey ~ E,) o> pIA de... ~ » P27 Less ~ » pre ..3.,) (1) j=0 j=0 Jal

where r is the one-period treasury bill return, e is the excess return on equity (over the treasury bill), and d is the dividend paid. All variables are measured in real terms and in logs, a tilde (~) superscript represents an innovation in a variable, and a delta (A) designates a first difference. Thus 6 is the equity excess) return innovation, and Ad is the log change in real dividends. We use E, to denote expectations formed at the end of perioc t, while (E,,,-E,) is the revision in expectations given

new information arrived during period t+1. The parameter p is a

5 An approximate intertemporal identity is derived by taking a first-order Taylor expansion of an accounting identity for the log one-period return, computing the forward solution of the resulting difference equation in the log of the dividend-price ratio, and applying expectations operators. The only assumption we make here is to impose a consistency condition on expectations that is somewhat weaker than rational expectations. For details, see Campbell (1991) or Campbell and Ammer (1993).

5

constant of linearization that is slightly less than one. ®

For convenience, we define simpler notation to refer to the

three news components above (see Table 1 for notational summary) :

6 = 6,-6,-6, (2)

Each term in (2) corresponds to one of the summations in (1). Equation (2) says that, ceteris paribus, news that dividends will grow more rapidly in the future would have a positive impact on today's stock return. On the other hand, an upward revision to expected future excess returns on stocks, accompanied with no information about future dividends or interest rates, means that the current stock price will have to drop, so that higher future returns can be generated from the same cash flow. In other words, an unexpected increase in the equity risk premium generates an immediate capital loss. Similarly, positive revisions to future interest rate expectations reduce the current

return on equity.

A foreign version of the stock equation (1) is

6 It is approximately equal to the inverse of the mean of the gross income return on stocks, or about .9973 for the U.S. monthly data analyzed in the following section.

6

Gra1 = (E,,, ~ E.) {> (p°)FAdr.4.; - > (p)IZEa.y jn0 j=a

(3)

jut

where the asterisk (*) superscripts denote foreign variables. However, to facilitate comparison of our results with the

international asset pricing literature, we will work with the excess of the foreign stock return (expressed in dollars) over

the domestic treasury bill return, given by

se s tor = Cro 7 Ades: ‘+ Leo - Tey (4)

where f is the foreign excess return, and q denotes the real

exchange value of the domestic currency. Substituting (4) into

(3), the innovation in the foreign stock excess return can be

written

Jj=0 jad

(5)

~ » (p*) Aga, ~ » (p°)If,.4.,)

Jj=0 jm1

Defining appropriate notation for the four terms on the right,

7

equation (5) can be rewritten as

f= f,-f,-f,-f, (6)

The intuition for the signs on fy, f,, and f, is the same as that given above for the signs on the corresponding components in equation (2). Also, the sign on the exchange rate component is negative for the same reason as the one for the excess return -ceteris paribus, news that the dollar will appreciate sometime in the future must reduce dollar returns on foreign assets at some point in time. With no revision in expected future excess returns on foreign stocks, the loss occurs today.

In this paper, we measure real integration between two countries by the correlation between domestic future dividend innovations, e,, and foreign future dividend innovations, fy. We also measure financial integration by using the correlation between domestic future expected return innovations, e,, and foreign future expected return innovations, f~- To show that these two correlations are reasonable measures of real and

financial integration, let us consider the following two extreme

hypothetical cases.

First, imagine a world consisting of two countries which have open capital markets, but also a complete lack of

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international labor mobility, no trade in goods, and complete secrecy about production technology. Further assume that changes in the cost of capital have negligible effects on production or long-term profits, and asset returns are conditionally multivariate normal, so that the conditional Capital Asset Pricing Model (CAPM) holds. Under these assumptions, there is absolutely no connection between the real economies of the two countries;, and we would expect zero correlation in long-term profits and, thus, zero correlation in e, and f,. However, because the two capital markets are perfectly linked and driven by a one--factor model, any time-variation in expected excess returns in the two countries would be perfectly correlated.

Thus, we would have perfect correlation between e, and f-.

Now consider the opposite scenario -- frictionless flow of goods, information, labor, but complete capital immobility. Further assume that all shocks have proportional effects on different industries, that profits are perfectly correlated with output in each countries, and that macroeconomic shocks have negligible effects on the expected excess returns required by investors. In this case, we would expect corporate earnings (dividends) to be perfectly correlated internationally, but there would be no possibility for arbitrage between the two equity markets. Thus, we would expect perfect correlation between e,

and f, but zero correlation between e, and ff.

3. Linkages between the United States and the United Kingdom

In this section, we apply equation (2) to a three-part decomposition of U.S. stock returns, and use equation (6) to break U.K. stock returns into four components. In order to proceed, we need some means by which to compute expectations of the variables in equations (1) and (5). Rather than rely ona specific theoretical model, we assume expectations are generated by a vector autoregression (VAR). Previous studies have found that dividend yields and nominal interest rates have significant forecasting power for stock returns.’ Accordingly, our VAR specification includes a dividend-price for each stock market, and Ai (the change in the nominal treasury bill rate), in

addition to q, r, e, and f.

Forecasts for q, r, e, and f from the VAR are used to calculate both the excess return innovations and the components of these innovations that are associated with exchange rates, interest rates, and excess returns, as defined in equations (1) and (5). The dividend growth components can then be inferred

from (2) and (6) by rearranging the equations as

Gy = €+6,+6, (7)

7 See, for example, Ferson and Harvey (1991), Fama and French (1988a), (1988b), (1989), and Keim and Stambaugh (1986).

10

and

f,=f+f,+f +f, (8)

By leaving monthly dividend growth out of our time series model, we avoid confronting the apparent seasonal variation in

dividends.

The generalized method of moments of Hansen (1982) is used to jointly estimate the VAR coefficients and the elements of the variance-covariance matrix of VAR innovations. To calculate the standard errors associated with estimation error for any statistic, we first let g and V represent the whole set of parameters and their variance-covariance matrix respectively. Next, we write any statistic, such as the covariance between news about future dividend growth and news about future expected returns, aS a nonlinear function f(g) of the parameter vector g.

The standard error for the statistic is then estimated as

“lI VE (9)

where f, is the gradient of the statistic with respect to the parameters (g).

11

Our first empirical exercise is a variance decomposition of

the domestic stock return. 8

From equation (2) it is clear that the variance of the excess return innovation can be written as

the sum of six terms:

Var (6) = Var(@,) - 2Cov(é4,6,) + Var(@,) - 2Cov(Eq,€,) (10) Sg) e

+ Var(&,) + 2Cov(é6,,6

The results of such a variance decomposition are reported in Table 2 for several VAR specifications and sample pericds.? The six components are scaled by the total variance so that they sum to one. Like Campbell (1991) and Campbell and Ammer (1993), we find in all cases that variation in the equity risk premium accounts for most of the aggregate volatility on the New York

Stock Exchange. ?°

8 we use the value-weighted New York Stock Exchange as the U.S. stock portfolio and the Financial Times All Shares Index as the foreign equity asset. Data were acquired from the CRSP tapes and the London Share Price Database. The treasury bill return is from Ibbotson (1991).

9 The Akaike Information Criterion was used as a guide in choosing lag lengths. For the 1957 to 1989 period, a 5-lag specification had the highest score, but a 2-lag specification was a close second. The 2-lag specification had the highest: score for both of the shorter samples.

10 Because the reliability of the empirical results is dependent on how accurately our VAR model measures expectations, robustness to specification changes is an important feature.

12

Table 3 reports the outcomes of analogous variance decompositions for the London Stock Exchange market portfolio. Again, news about future excess returns is the main source of variation in current returns. In contrast the exchange rate news component contributes nothing to equity market variance, because our VAR model is not capable of forecasting changes in the real

exchange rate.

Next we examine interactions between the American and British markets. Some simple data correlations appear in Table 4 for our full sample (1957 to 1989) and two subsamples. Note that for all three periods the correlation between the two country's stock returns is substantially greater than the correlation of measures of their real output growth. In addition, the contemporaneous correlations between equity returns and output growth are negligible. Nevertheless, it is impossible to dete:cmine from these statistics alone whether real or financial integration is driving co-movements in the two stock markets. Common shocks that persistently impact the two economies' longrun economic growth and risk premiums but with different lags could be an important signs of real and financial integration. However, one can not see that impact from the contemporaneous correlations between equity returns and output growths due to the time lags. By examine the co-movement of innovations on future dividend growth and excess return, we may be able to discover

impoixtant evidence of long-term real and financial integration.

13

The covariance between stock return innovations in the U.S. and the U.K. is the sum of the covariances between each of the terms on the right sides of equations (2) and (6). The contributions of each of these 12 covariance components are listed in Table 5 for our three sample periods. In general, the two largest contributions to the total covariance come from correlated news about future dividend growth in the two countries and correlated news about future excess returns, although interactions between these two components also plays a role. Ironically, the common interest rate news component makes only a negligible contribution. This is because changes in real

interest rates are difficult to forecast.

A comparison of the two sub-samples shows a significant increase in the covariance of American and British stock returns after fixed exchange rates were abandoned in 1973. The decomposition enables us to attribute most of the change to greater financial integration in the later period. This result may have as much to do with the suspension of capital controls in Great Britain in the late 1970's than it does with the move to

floating exchange rates.

Tables 6, 7, and 8 report simple correlations of the return components. A comparison of the correlations between fe and e,

in Tables 7 and 8 confirms the greater degree of financial

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integration measured in the later period. The two dividend growth components are highly correlated in both sub-samples, but the correlation is slightly higher under the floating rate regime. This suggests that monetary shocks may not be an important source of variation in the real economy. A move to floating exchange rates reduces the obligation of the two central banks to coordinate monetary policy, whereas monetary shocks tend

to be common to all countries under fixed rates.11

We. can also see from comparing Tables 6, 7, and 8 to Table 4 that the innovations in long-term dividend growth are much more highly correlated between the two countries than are our measures of contemporaneous output growth. This suggests that, although output in the two countries may be affected in the short run by transitory country-specific factors or by common factors but with different lags, long-term dividend growth in the two countries is

driven by common influences.

11 Although sufficiently restrictive capital controls can permit independent monetary policy under fixed exchange rates.

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4. Real and Financial Linkages among 15 Industrialized Countries

The United States and the United Kingdom do not seen to be unusual in having more contemporary correlation between their equity returns than between their output growth rates. Tables 9 and 10 report correlation matrices for industrial production growth in 15 industrialized economies and excess dollar returns on their national stock markets, respectively.?? The mean pair-wise production correlation is about 9 percent, while the

equity return correlations average 44 percent.

Once again, we can decompose excess return covariation among the various countries, using equation (6), to measure the relative importance of real and financial integration. For each foreign country, expectations are generated by forecasts from a 2-lag VAR in f, q, r, Ai, the dollar excess return on a world market portfolio, and both the national and world dividerd-price

ratios.13

Correlations among the dividend growth components and excess return components of the various countries are previded in Tables 11 and 12. The means of these correlations are 30 percent and 27 percent respectively, suggesting that both real and financial linkages are important. In general, economies that are

12 National stock market returns are drawn from the Morgan Stanley Capital International database.

13 Separate vector autoregressions are used for each country in lieu of estimating a single system to avoid having a problem with degrees of freedom. Results with 1-lag VAR systems were nearly

identical.

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geographically proximate tend to be connected more closely. For example, we find substantial real and financial integration between France and Germany, but little of either between the Netherlands and Japan. For most pairs of countries, the dividend component correlation exceeds the contemporaneous output correlation reported in Table 9. Thus, again, we see that real and financial linkages are much stronger from a long-run

perspective than from a short-run perspective.

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5. Conclusions

In addition to making a methodological contribution, this paper has several interesting empirical findings. First, the stylized fact that variations in equity risk premiums are the principal source of stock return variance in the United States appears to apply to the United Kingdom as well. Second, we find substantial degrees of both real and financial integration between the U.S. and U.K. economies. Although common news about future risk premiums accounts for the bulk of the covariance between the two country's stock markets, the dividend growth components of the two returns are also highly correlated. In addition, both real and financial linkages are found to be greater after the Bretton Woods arrangement was abandoned in the early 1970's. A common interest rate news component accounts for only a small part of the return covariance because of the lack of

predictability of short-term real interest rates.

In a further application of our methodology to data from 15 countries from 1974 to 1990, we find that both real and financial integration typically contribute to the (consistently positive) correlations between the returns on national stock markets. In most cases, news about future dividend growth in two countries is more highly correlated than contemporaneous output measures. This suggests that there are lags in the international

transmission of real economic shocks.

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Appendix

In order to implement our decomposition, we need to construct empirical proxies for

news about future cash flows, real interest rates, real exchange rate and excess returns. To do this, we assume that Z, isa vector of state variables which includes (e,,

f,. ft. dt) as its first four elements. Next we assume that the state vector follows a first-

order VAR: Zt41 = AZ + Wrey, (A1)

where w;,; is the innovation in z,,;. The assumption that the VAR is first-order is

not restrictive since a higher-order VAR can always be rewritten in first-order form as discussed by Campbell and Shiller (1988) among others. The matrix A is known as

the companion matrix of the VAR. Given the VAR model, revisions in long-horizon expectations of z;,; are:

Finally, we define el to be an L-element column vector whose first element is one and whose other elements are all zero. This vector picks e, out of the state vector.

We also define e2, e3, and e4 to pick fy. ry, and q; out of the state vector, respectively.

Equation (1), (2) (5) and (6) imply that the components of domestic and foreign stock

excess returns can be written as follows: ~~ , . ~ 7 * -1 €e41 = €1 PA(I - pA) Wot, fri =e2 Pp ATI-p A) Wut, Sree = 63 (I - pay? Wut, fate =eq4 (1 - p")d - pA) wus ,

' *, .-1 4 ' ~ ' four =e3 (1-Pp A) Wut, fur =e2' We, Cui = C1 Wu,

Cart = Cut + Cree + Ceters fate = fra + fret + fget + fees. (A3)

Once we have the asset return components above, it is straightforward to decompose

the domestic and foreign stock returns and study their co-movements.

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Ai

q

Aq (d/p) (d/p)* Ay Ay* Ad

Ad*

Fore

For €

Note: All variables are measured in logs. Variables are measured in real terms unless

Table 1 Variable Definitions

excess return on U. S. stocks over one-month treasury bill innovation in excess return on U.S. stocks (e)

excess dollar return on foreign stocks over U.S. treasury bill innovation in excess dollar return on foreign stock (f)

real return on one-month U.S. treasury bill

change in (nominal) yield on one-month U.S. treasury bill real exchange rate index (foreign goods per unit U.S. goods) change in real exchange rate index

U.S. dividend-price ratio (using dividends for previous 12 months) foreign dividend-price ratio

change in (real) U.S. industrial production

change in (real) foreign industrial production

real U.S. dividend growth

real foreign dividend growth

News Components of Excess Stock Returns

Real Dividend Real Interest Real Exchange Excess Stock Growth Rates _ Rates Return eg er eq ee fy fi fq ff

otherwise noted. The timing convention is that variables dated t are known at the end of time t.

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Table 2

Variance Decomposition for Domestic Excess Stock Returns

a Sample Period 57-89 57-72 73-89 57-89 57-89 Var(e) 17.62 12.47 21.37 16.29 17.65 (S.D) (1.778) (1.313) (2.975) (1.648) (1.808) Shares of (2 lags) (2 lags) (2 lags) (5 lags) (2 lags) 4 Var( Sq) 0.121 0.277 0.116 0.119 0.116 (0.375) (0.214) (0.294) (0.375) (0.102) -2Cov(qér) -0.033 -0.077 -0.077 -0.065 -0.043 (0.034) (0.074) (0.092) (0.068) (0.039) -2Cov(€q,€o) 0.075 0.129 -0.123 0.023 0.044 (0.343) (0.449) (0.590) (0.745) (0.292) Var( er) 0.031 0.008 0.054 0.051 0.033 (0.016) (0.007 ) (0.038) (0.032) (0.018) 2Cov(E, Ee) 0.077 -0.005 0.135 -0.124 0.091 (0.122) (0.07 5) (0.172) (0.124) (0.143) Var( A) 0.729 0.669 0.895 0.749 0.758 (0.250) (0.311) (0.323) (0.379) (0.225)

Note: The VAR includes e, f, r, q, Ai, (d/p), and (d/p)*. The equations defining the

components are given by (1) and (2) for the domestic excess returns. And we rescale the components by dividing them by Var(e) so that the components sum to one.

lIn this specification, we replace the q variable in the VAR process with the Aq variable.

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Table 3

Variance Decomposition for Foreign Excess Stock Returns: UK

Ee Sample Period 57-89 57-72 73-89 57-89 57-89 Var(f) 41.38 20.42 58.69 37.88 41.46 (S.D) (4.821) (2.539) (8.480) (3.760) (4.804) a Shares of (2 lags) (2 lags) (2 lags) (5 lags) (2 lags) } Var( fg) 0.173 0.160 0.179 0.243 0.174 (0.055) (0.401) (0.108) (0.165) (0.083) -2Cov(fgsfr) -0.041 -0.019 -0.060 -0.088 -0.047 (0.631) (0.033) (0.057) (0.068) (0.035) -2Cov(Fasfg) 0.000 0.000 -0.000 0.000 -0.077 (0.000) (0.000) (0.000) (0.000) (0.088) -2Cov(fa,fp) 0.131 0.165 -0.001 0.141 0.169 (0.230) (0.873) (0.328) (0.292) (0.235) Var( fr) 0.013 0.005 0.020 0.021 0.014 (0.230) (0.004) (0.014) (0.012) (0.007) 2Cov(F; fq) 0.000 -0.000 0.000 -0.00 0.037 (0.000) (0.000) (0.000) (0.000) (0.007) 2Cov(f, ff) -0.043 -0.025 -0.021 -0.041 -0.061 (0.071) (0.053) (0.096) (0.07 5) (0.069) Var‘ fq) 0.000 0.000 0.000 0.000 0.215 (0.000) (0.000) (0.000) (0.000) (0.057) 2Cov(fg ff) 0.000 0.000 ~ 0.000 0.000 0.006 (0.000) (0.000) (0.000) (0.000) (0.191) Var( ff) 0.766 0.714 0.882 0.724 0.570 (0.246) (0.569) (0.299) (0.244) (0.252)

Note: The VAR includes e, f, r, q, Ai, (d/p), and (d/p)*. The equations defining the components are given by (5) and (6) for the foreign excess returns. And we rescale the components by dividing them by Var(f) so that the components sum to one.

1In this specification, we replace the q variable in the VAR process with the Aq variable.

25

Table 4 Data Correlations: U.S. and U.K. A. Monthly Correlations 1957:1-1972:12

e f Aq Ay e 1.000 f 0.348 1.000 Aq -0.028 -0.028 1.000 Ay 0.133 0.074 0.039 1.000 B. Monthly Correlations 197 3:1-1989:12 e f Aq Ay Ay* e 1.000 0.516 1.000 Aq -0.033 -0.487 1.000 Ay -0.077 -0.137 0.183 1.000 Ay* -0.070 0.035 -0.098 0.243 1.000 Cc. Monthly Correlations 1957:1-1989:12 e f Aq Ay e 1.000 0.464 1.000 Aq -0.031 -0.452 1.000 Ay 0.022 -0.046 0.120 1.C:00 D. Quarterly Correlations for Three Sample Periods Sample Period 1957-1989 1957-1972 1973-1989 US&UK 0.074 -0.087 0.177

Table 5

Covariance Decomposition for U.S. and U.K. Excess Stock Returns

News fg f, fg ff cov( 3,f) =12.15 (2.183) 1957:1-1989:12 eg 1.150 0.291 -0.001 -2,168 (1.288) (0.287) (0.004) (2.610) Ee 0.865 0.547 0.000 -0.902 (0.667) (0.279) (0.002) (1.478) Se 2.803 0.626 -0.005 12.530 (3.688) (1.018) (0.007) (5.299) cov(¢,f) = 5.610 (1.337) 1957:1-1972:12 gq 1.492 0.472 -0.002 -1.753 (2.404) (0.443) (0.002) (3.507) e 0.196 0.101 0.000 -0.265 (0.340) (0.080) (0.000) (0.566) ee -1.519 -0.031 -0.001 1.710 (2.960) (0.451) (0.002) (4.602) cov(@,f) = 17.51 (3.802) 197 3:1-1989:12 eg 2.631 0.817 0.001 -0.254 (2.949) (0.936) (0.003) (5.159) e, 1.779 1.156 0.002 -0.599 (1.664) (0.765) (0.002) (2.846) Ee 6.767 1.394 0.001 22.035 (5.710) (1.755) (0.006) (9.103)

Note: The covariance of the return innovations is provided on the first line of each panel The standard deviations for each statistics appear in the parentheses. The variables are defined in Table 1 and by equation (1),(2), (5), and (6) in the text. The Statistics are estimated based on a 2-lag VAR in e, f, r, q, Ai, (d/p), and (d/p)*.

27

Table 6 Correlations in Components of US and UK Excess Stock Return Innovations (1957-1989)

ea er ee fy f, fy fr eq 1.000 (0.000) er -0.270 1.000

(0.240) (0.000)

€e 0.127 0.254 1.000 (0.652) (0.411) (0.000)

fq 0.295 0.435 0.292 1.000 (0.278) (0.224) (0.349) (0.000)

fy 0.271 1.000 0.237 -0.429 1.000 (0.241) (0.000) (0.396) (0.225) (0.000)

fg -0.108 0.138 -0.312 0.090 0.160 1.000 (0.597) (0.514) (0.390) (0.463) (0.486) (0.000)

fr -0.264 -0.216 0.621 0.181 -0.216 0.227 1.000 (0.397) (0.331) (0.163) (0.343) (0.324) (0.335) (0.000)

Note: Asymptotic standard errors appear below each statistic in the parentheses. The variables are defined in Table 1 and by equation (1),(2), (5), and (6) in the text. The Statistics are estimated based on a 2-lag VAR in e, f, r, q, Ai, (d/p), and (d/p)*.

28

Table 7 Correlations in Components of US and UK Excess Stock Return Innovations (1957-1972)

a a, a a ed & Ce fy f fg fr ey 1.000 - (0.000) e, -0.811 1.000 (0.446) (0.00) ee 0.150 -0.032 —-1.000 (0.591) (0.513) (0.00) fg 0.445 0.339 -0.291 1.000 (0.490) (0.368) ~— (0.822) (0.000) fy 0.805 1.000 -0.034 —--0.338 1.000 (0.444) (0.000) (0.501) (0.363) — (0.000) fg 0.762 0.666 -0.345 0.170 -0.656 1.000 (0.332) (0.346) (0.343) ~— (0.516) +~—s- (0.332) ~—S_ (0.000) ff -0.247 0.216 0.155 0.244 -0.214 0.184 1.000

(0.540) (0.488) (0.365) (1.650) (0.474) (0.550) (0.000) Note: Asymptotic standard errors appear below each statistic in the parentheses. The variables are defined in Table 1 and by equation (1),(2), (5), and (6) in the text. The statistics are estimated based on a 2-lag VAR in e, f, r,q, Ai, (d/p), and (d/p)*.

29

Table 8 Correlations in Components of US and UK Excess Stock Return. Innovations (1973-1989)

Sq é. So ta f. fy ff eq 1.000 (0.000) er -0.486 1.000

(0.377) (0.000)

&e -0.191 0.306 1.000 (0.664) (0.364) (0.000)

fq 0.516 0.508 0.477 1.000 (0.589) (0.287) (0.308) (0.000)

fy 0.485 1.000 0.298 -0.503 1.000 (0.384) (0.000) (0.359) (0.286) (0.000)

fq 0.168 0.405 0.048 -0.100 0.412 1.000 (0.751) (0.425) (0.376) (0.452) (0.414) (0.000)

fp -0.022 -0.077 0.700 -0.001 -0.078 0.371 1.000 (0.463) (0.367) (0.143) (0.412) (0.362) (0.301) (0.000)

Note: Asymptotic standard errors appear below each statistic in the parentheses. The variables are defined in Table 1 and by equation (1),(2), (5), and (6) in the text. The Statistics are estimated based on a 2-lag VAR in e, f, r, q, Ai, (d/p), and (d/p)*.

30

Table 9

Correlation of Industrial Production Growth for Fifteen Nations

BE 27 CA 01 DN ~ -.02 FR -.03 GE -.04 IT -.03 JA -.06 NE .05 NO .04 SP 02 SD 08 SZ 11 UK 21 US 10

.20 .33

14

-.05

28

.24

-.09

.03 09 -.07 139.12) -.18

04 05 04 -.00

04 24 -13 03 -10 .03

Note: The fifteen nations are Austria, Belgium, Canada, Denmark, France, Germany,

Italy, Japan Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom

’

United States. The sample covers the time period of 1974:1-1990:12.

31

Table 10

Correlation of (Dollar) Excess Stock Returns for Fifteen Nations

IT 30 46 32 33 49 40 JA 22 45 24 38 41 38 42 NE 41 .68 58 49 59 68 41 ~ 4!

Note: The fifteen nations are Austria, Belgium, Canada, Denmark, France, Germany, Italy, Japan Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States. The sample covers the time period of 1974:1-1990:12.

32

Table 11

Correlation of Future Dividend Growth News for Fifteen Nations

CA .20) .28 DN 50) .28 .07

GE 47 44 .20 .25 48 IT 28 34 09 19 46 50 JA 38 .23. -10 27 16 -06 .10

NO 40 42 19 .29 40 .27 26 .20 49 SP 45 18 .02 .27) 11) ~21 -10 50 -06 .27 SD 33 36 05 .23 33 47 38 16 56 40 .02

Note: The fifteen nations are Austria, Belgium, Canada, Denmark, France, Germany, Italy, Japan Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States. The sample covers the time period of 1974:1-1990:12. The statistics are estimated. based on a 2-lag VAR in f, r, q, Ai, the dollar excess return on a world market portfolio, the national (d/p), and the world (d/p).

33

Table 12

Correlation of Excess Stock Return News for Fifteen Nations

AU BE CA DN FR CE IT JA NE NO SP SD SZ UK

NE .21 61 .58 31 .72 #469 4.61 = .04 NO .06 .39 37 09 4.47) «340 4200 (O74 SP ll -14 -15 05 -13 -10 -07 13 -14 -15

Note: The fifteen nations are Austria, Belgium, Canada, Denmark, France, Germany, Italy, Japan Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States. The sample covers the time period of 1974:1-1990:12. The statistics are estimated based on a 2-lag VAR in f, r, q, Ai, the dollar excess return on a world market portfolio, the national (d/p), and the world (d/p).

34

IFDP NUMBER

449

448

447

446

445

444

443

442

441

440

439

438

437

International Finance Discussion Papers TITLES

1993

Measuring International Economic Linkage with Stock Market Data

Macroeconomic Risk and Asset Pricing: Estimating the APT with Observable Factors

Near observational equivalence and unit root processes: formal concepts and implications

Market Share and Exchange Rate Pass-Through in World Automobile Trade

Industry Restructuring and Export Performance: Evidence on the Transition in Hungary

Exchange Rates and Foreign Direct Investment: A Note

Global versus Country-Specific Productivity Shocks and the Current Account

The GATT'’s Contribution to Economic Recovery in Post-War Western Europe

A Utility Based Comparison of Some Models of Exchange Rate Volatility

Cointegration Tests in the Presence of Structural Breaks

1992

Life Expectancy of International Cartels: An Empirical Analysis

Daily Bundesbank and Federal Reserve Intervention and the Conditional Variance Tale in DM/$-Returns

War and Peace: Recovering the Market's Probability Distribution of Crude Oil Futures Prices During the Gulf Crisis

AUTHOR(s

John Ammer Jianping Mei

John Ammer

Jon Faust

Robert C. Feenstra Joseph E. Gagnon Michael M. Knetter

Valerie J. Chang Catherine L. Mann

Guy V.G. Stevens

Reuven Glick Kenneth Rogoff

Douglas A. Irwin

Kenneth D. West Hali J. Edison Dongchul Cho

Julia Campos

Neil R. Ericsson David F. Hendry

Jaime Marquez

Geert J. Almekinders Sylvester C.W. Eijffinger

William R. Melick Charles P. Thomas

Please address requests for copies to International Finance Discussion Papers, Division of International Finance, Stop 24, Board of Governors of the Federal

Reserve System, Washington, D.C.

20551.

35

IFDP NUMBER

436

435

434

433

432

431

430

429

428

427

426

425

424

423

“422

International Finance Discussion Papers TITLES 1992

Growth, Political Instability, and the Defense Burden

Foreign Exchange Policy, Monetary Policy, and Capital Market Liberalization in Korea

The Political Economy of the Won: U.S.-Korean Bilateral Negotiations on Exchange Rates

Import Demand and Supply with Relatively Few Theoretical or Empirical Puzzles

The Liquidity Premium in Average Interest Rates

The Power of Cointegration Tests

The Adequacy of the Data on U.S. International Financial Transactions: A Federal Reserve Perspective

Whom can we trust to run the Fed? Theoretical support for the founders views

Stochastic Behavior of the World Economy under Alternative Policy Regimes

Real Exchange Rates: Measurement and Implications for Predicting U.S. External Imbalances

Central Banks’ Use in East Asia of Money Market Instruments in the Conduct of Monetary Policy

Purchasing Power Parity and Uncovered Interest Rate Parity: The United States 1974 - 1990

Fiscal Implications of the Transition from Planned to Market Economy

Does World Investment Demand Determine U.S. Exports?

The Autonomy of Trade Elasticities: Choice and Consequences

36

AUTHOR(s

Stephen Brock Blomberg

Deborah J. Lindner

Deborah J. Lindner

Andrew M. Warner

Wilbur John Coleman II Christian Gilles Pamela Labadie Jeroen J.M. Kremers Neil R. Ericsson Juan J. Dolado

Lois E. Stekler Edwin M. Truman Jon Faust

Joseph E. Gagnon Ralph W. Tryon

Jaime Marquez

Robert F. Emery

Hali J. Edison William R. Melick

R. Sean Craig Catherine L. Mann

Andrew M. Warner

Jaime Marquez