An Empirical Examination of the Ex Ante International Interest Rate Transmission

THE FINANCIAL REVIEW VOL. 30 No. 1 FEBRUARY 1995 PP. 175-192

A n Empirical Examination of the Ex Ante International Interest Rate

Transmission

Hung-Gay Fung* and Wai-Chung Lo**

Abstract

Using U.S. Treasury bill and Eurodollar futures to proxy for domestic and external interest rates, respectively, this study examines ex ante interest rate transmission across markets for the period 1982-1991. The results indicate that these interest rates are cointegrated and that they Granger-cause each other, implying that both domestic and offshore interest rates move together and that both markets are integrated. Interest rate transmission is found to be more rapid in recent years, a result supporting the idea that the international financial markets are becoming more integrated.

Introduction Early studies (Levin [291, Kwack [281, and Hender-

shott [20]) suggest that U.S. interest rates are relatively immune from the influence of foreign markets. Giddy, Dufey, and Min [141 argue, moreover, that the Eurodol- lar market is more sensitive than its U.S. counterpart to changes in domestic credit conditions because there are fewer regulatory constraints in international money markets. While several studies (Fung and Isberg [121, Swanson 1381, Hartman “41, Schnitzel [341, and Kaen and Hachey [251) provide results that indicate feedback effects from the Eurodollar to the U.S. market are evi-

* University of Baltimore, Baltimore, MD 21201.

** City Polytechnic of Hong Kong, Hong Kong.

reviewers for helpful comments, and Patricia Peat for editorial assistance. The authors would like to thank Raymond Chiang and two anonymous

175

176 Fung and Lo

dent, the main line of causality runs from the domestic to the external markets.

The relationship between domestic (U.S.) and offshore (Eurodollar) interest rates is described in terms of the international transmission of interest rates. Prior studies examining the transmission mechanism between domestic and Eurodollar interest rates use measures such as Treasury bill yields and rates on both commer- cial paper and certificate of deposits (U.S. and Eurodol- lar) as proxy variables. All these rates are ex post measures.

Examination of ex ante interest rates would be an- other way to measure international rate transmission. One approach to generating ex ante interest rates uses models based on the autocorrelation function of interest rates (e.g., Box and Jenkins [4]), an approach that is in- consistent with rational expectations (Grabbe [ 161). A so- lution is to use the futures market to obtain ex ante interest rate proxies.

Risk transfer and arbitrage are commonly viewed as important functions of the futures markets; the futures price can be linked to the spot price via a stable basis (i.e., the difference between futures and spot prices) for hedging purposes or by the carrying cost model for arbitrage purposes (Siegel and Siegel [371 and Rendleman and Carabini [32]). Futures markets also serve the function of price discovery (i.e., using futures prices to anticipate cash market transaction prices). Un- der the speculative efficiency hypothesis (SEH), the forwardlfutures price is an unbiased predictor of the future spot price (e.g., see Bilson [31).

Empirical evidence in many studies (Fung and Le- ung [13], Kamara [261, Walz and Spencer [391, MacDon- ald and Hein [30], Hegde and MacDonald [191, and Cosset [7], among others) indicates that forwardlfutures interest rates provide important information on future interest rate movements. It therefore seems reasonable to use the futures prices to proxy the expected future interest rates.

The Eurodollar futures and the Treasury bill (T- bill) futures contracts, highly liquid short-term interest rate contracts, are the most popular. T-bill futures track

Ex Ante International Interest Rate 177

default-free short-term U.S. government securities while Eurodollar futures track the London Interbank Offer Rate for three-month Eurodollar deposits.

Using U.S. T-bill and Eurodollar interest rate futures to proxy future interest rates for the domestic and international markets, respectively, this study examines ex ante interest rate transmission across markets for the period 1982-1991.' The authors test T-bill and Eurodol- lar futures for the existence of cointegration, and estimate an error correction model (ECM), as specified in Engle and Granger [9] and Johansen [21, 221, to examine the interest rate transmission mechanism.

Cointegration analysis improves on the vector autoregression (VAR) model used in the literature because the VAR model may be misspecified (Granger [171 and Engle and Granger [9]) if the time series are cointegrated. The economic theory put forth in Granger [17] and in Engle and Granger [9] specifies use of ECM to examine causal relationships in cases where two series are cointegrated. Some studies examining interest rate transmission rely on the Granger-Sims causality test in a VAR model (Swanson [38], Hartman [18], and Kaen and Hachey [251). In order to demonstrate the supe- riority of ECM over VAR, the authors also examine their forecasting performance in both Eurodollar and T-bill futures.

Cointegration Analysis A time series is said to be integrated of order one

[i.e, I(1)l if it must be first-differenced to induce station- arity. If there exists a linear combination of two or more I( 1) series that is itself stationary, the series are cointegrated. Consider a p x 1 vector of I( 1) variables X , which has an autoregressive representation:

t = 1, 2, ..., T The long-run static equilibrium corresponding to (1) is:2

178 Fung and Lo

where the long-run coefficient matrix n is defined

n = 1-rI1-rI2- ... -n, (3)

Thus, n is a p x p matrix whose rank determines the number of distinct cointegrating vectors that exist between the variables in X . Define two p x r matrices, a and p, such that:

n = ap’ (4)

The rows of p’ form the r distinct cointegrating vectors such that, if p’ is the ith row of p’:

Johansen [211 demonstrates that the likelihood ratio test statistic for the hypothesis that there are at most r distinct cointegrating vectors is:

P

LR = -T i = r + l

where f r+l , ..., kp are the p - r smallest squared canoni- cal correlations between the Xt- , and AXt series, cor- rected for the effect of the lagged differences of the X process (see details of how to extract the estimated h in Johansen [221 and Johansen and Juselius [231).

Empirical Results of the Cointegration Analysis To analyze interest rate transmission using Euro-

dollar and Treasury bill interest rates futures, the nearby futures daily data for both Eurodollar and T-bill futures contracts from the Wall Street Journal were col- lected. The contracts are matched by maturity for a bet- ter comparison. The data sample runs from May 1982 to the end of 1991.

Ex Ante International Interest Rate

16

14

12

10

8

6

4

2 -

179

-

I

'

Interest Rates May 1982 - December 199 I

Jan 83 Jan 85 Jan 87 Jan 89 Jan 91 Jan 84 Jan 86 Jan 88 Jan 90

I - Eurodollar - - ~ - Treasury Bill 1

Figure 1 plots the monthly average of both Eurodol- lar and Treasury bill interest rate futures. The interest rates in Eurodollar futures are higher than the Treasury bill futures, a result consistent with relationships in the underlying cash markets. Eurodollar deposit rates are normally higher than their domestic counterparts because of perceived differences in risk and market imper- fections, including regulatory constraints (see Giddy, Dufey, and Min [141 for details).

Results from Cointegration Test A prime consideration is the order of integration of

the time series since the time series are assumed to be I(1). The authors use the standard Augmented Dickey- Fuller [81 procedure to test for a unit root.

Table 1 reports the results for the unit root test, which indicate that both Eurodollar and Treasury bill interest rate futures are not stationary at the level but are stationary after the first difference. Thus, these interest rate series are I( 1). Higher lags for the autoregressive terms in the unit root test shown in Table 1 were tested; results are similar, but not reported here.

180

TABLE 1

Results of Augmented Dickey-Fuller Unit Root Test for Eurodollar and Treasure Bill Futures 1982-1991

Fung and Lo

Y b l No. of lags, k

Panel (a): Level

Eurodollar -0.003 5 (-1.99)

T-Bill

-0.003 10 (-2.17)

-0.004 5 (-2.07)

-0.004 10 (-2.12)

Panel (b): First Difference

Eurodollar -1.018 5 (-20.83)*

-0.944 10 (-14.12)*

T-Bill -1.369 5 (-24.20)*

-0.944 10 (-15.90)*

* significant at 5 percent level. Augmented Dickey-Fuller (ADF) t-values are in parentheses. The ADF test is run as follows:

AY, = bo+ blYt-l+ AYr-k +e t .

Yt denotes Eurodollar or Treasury bill interest rate futures. The critical value for the 5 percent level is -2.86 (see Fuller [ I l l , p. 3731).

As a preliminary to the Johansen analysis, the lag length of the VAR is determined. The authors employ Schwarz’s [35] Bayesian approach to estimate the lag length, K, that minimizes the value of


TABLE 2

Johansen’s Cointegration Tests 1982-1991

H o : r < l Ho:r<_O VAR lag length

Eurodollar & T-bill 5.587 22.901* 7

5% Critical Values 9.094 20.168

* denotes significance at 5 percent level.

Notes: r denotes the maximum number of cointegration vectors. The 5 percent critical values are taken from Johansen and Juselius [231. The vector autoregressions include constant and seasonal dummies.

In zh + m2k ln(T)/T (7) I I where Z is the estimated variance-covariance matrix of the VAR residuals, m is the number of variables, and T is the number of observations.

After determining the optimal lag length, the authors proceed with the cointegration test. Table 2 presents the r e s u l t ~ . ~ They suggest that there is one cointegration vector between Eurodollar and Treasury bill interest rate futures, implying that there is one com- mon trend for both time series. It is clear from this result that the Eurodollar and Treasury bill futures move together.

Results of Interest Rate Transmission Since both Eurodollar and Treasury bill futures are

cointegrated, their relationship can be expressed in an error correction model (ECM) as follows (see Engle and Granger [91 and Johansen [221):

i = l i = l

where Y, is either Eurodollar or T-bill futures, 2, - is the error correction term, and X t - i is the cross-market variables, i.e., if Y denotes Eurodollar futures, X will be the

182 Fung and Lo

TABLE 3

Tests of International Interest Rate Transmission The error correction model for the sample period is:

I 7

AYt = aZL-l+ CPIAYt-,+ C y A X - , + e t

Ho: Causal variables or cross-market variables, X , do not have a n important impact on the dependent variable, Y, i.e., a and all 'i: are equal to zero.

r = l i = l

H,: Dependent variable is not caused by the

Dependent Variable Causal Variable causal variable

Eurodollar Futures T-bill Futures 10.88 (0.00)

T-bill Futures Eurodollar Futures 21.56 (0.00)

Numbers reported are F-values. Figures in parentheses (p-values) are marginal significance levels.

Treasury bill futures). e, is the error term. 2,- can be ob- tained from regressing Y, on X, (see Granger [17]). The Granger-causality of cross-market variables on the dependent variable is examined by testing whether a and all yl are equal to zero using a standard F-test (for details, see Fung and Isberg [ 121 and Swanson [381).4

Table 3 shows the empirical results of the Granger- causality tests. The results indicate that historical T- bill futures have a significant impact on the Eurodollar futures (when Eurodollar futures are used as the dependent variable) and that historical Eurodollar futures also have an important influence on T-bill futures (when Treasury bill futures are used as the dependent variable).

These findings suggest that ex ante domestic and offshore interest rates Granger-cause each other, implying that both markets are integrated. These results are consistent with prior studies on financial market integration (Fung and Isberg 1121, Swanson [38], and Hart- man [IS]).


Empirical Tests of Parameter Stability Many studies (Jorion [241 and Campbell and

Clarida [61 are examples) demonstrate that interest rate behaviors are time-varying. Furthermore, the relationship between futures price and the future spot price may change over time. Fung and Isberg [121 and Swanson [381 indicate that there may be a structural shift in the domestic and offshore interest rate relationship, but they have not formally tested the change.

In this study, the structural change using Ashley’s [ 11 procedure is examined. Following Ashley’s test of parameter stability, the authors split the whole period into eight subperiods for analysis.

Table 4 reports the results of the parameter stability test. For the Eurodollar equation (where the dependent variable is the Eurodollar futures), the error correction term (which represents the long-run relationship) was found to be stable over time. However, neither the own-lagged nor cross-market variables (which repre- sent the short-run adjustments) is stable. In the Treas- ury bill equation (where the dependent variable is the Treasury bill futures), we found that all variables, including the error correction term, change over time.

The difference between the long-run behavior of Eurodollar and T-bill futures is interesting. The result is consistent with the contention that the offshore market (an integral part of the total dollar market) is more com- petitive, thus reflecting long-run global market forces (Giddy, Dufey, and Min [14]). Yet the domestic market is subject to many regulatory constraints and attempts to incorporate information from the external market. It appears to be becoming more integrated with the international market.

Because Ashley’s test indicates that the interest rate relationship is time-varying, the authors split the whole period into earlier and later periods of roughly equal size to examine the impact of the structural change in the interest rate relationship. Table 5 reports the results of the Granger-causality tests for the subperiods.

First, Eurodollar and Treasury bill interest rates were found to Granger-cause each other in both earlier

184 Fung and Lo

TABLE 4

Ashley Tests of Parameter Stability The error correction model is subdivided into eight subperiods with (1,O) dummies. For instance, if the hypothesis of stability in Zt - 1 term is tested, multiply the eight dummies by the Zt-1 term, and run the regression as follows:

Then. test

Ho: the error correction term is stable over time, i.e., Nl) = a(2) = . . . = a(8), where w ) is thej th period coefficient.

In addition, using a similar formulation, other hypotheses can be tested:

Ho: the own-lagged variables (AYt --I which have seven variables) are stable over time, i.e., bz(l) = PL(2) = ... = p,(8), for i = 1, 2, ..., 7, where pt(j) is the j t h period coefficient of the ith variable.

Ho: the cross-market variables (which have seven variables) are stable over time, i.e., ~ ( 1 ) = y42) = ... = ~ ( 8 ) ) for i = 1,2 , ..., 7, where yLG) is the j t h period coefficient.

Ho: all variables are stable over time.

H,: coefficients are stable over time Interest Rate Variables Z, I Own-lagged Cross-market All Variables

ED 1.243 2.053 0.913 2.824 (0.274) (0.000) (0.000) (0.000)

TB 2.568 3.452 2.563 2.243 (0.012) (0.000) (0.000) (0.000)

ED denotes Eurodollar futures; TB, Treasury bill futures.

The statistics are F-values. Figures in parentheses @-values) indicate marginal significance level.

and later periods, implying that the two markets are closely linked. This result differs from Fung and Isberg’s [121 results, which find only unidirectional impact from the domestic market interest rate to the offshore market in the earlier period, but reverse causality in the later period. These results are likely attributable to the ex ante nature of the interest rate futures markets that anticipate future interaction of domestic and offshore interest rates in the cash markets.


TABLE 5

Subperiod Analysis of the Interest Rate Transmission The error correction model for each period is:

Ho: all variables or cross-market variables, X , do not have an important impact on the dependent variable, Y; i.e., c1 and all are equal to zero.

Panel (a): First Subperiod

H,: cross-market variables do not cause dependent Optimal Lag

Dependent Variable Causal Variable variable Length

Eurodollar Futures T-bill Futures 19.37 7 (0.00)

(0.00) T-bill Futures Eurodollar Futures 11.81 7

Panel (b): Second Subperiod

H,: cross-market variables do not cause dependent Optimal Lag

Dependent Variable Causal Variable variable Length

Eurodollar Futures T-bill Futures 7.08 3 (0.00)

(0.00) T-bill Futures Eurodollar Futures 7.42 3

Numbers reported are F-values. Figures in parentheses (p-values) are marginal significance levels.

Using the same optimal lag selection procedure, the authors find that the optimal lag length in the second period (found to be three) is shorter than in the first period (found to be seven). These results imply that the adjustment process (as reflected in the lagged variables) in both markets is faster in the later period. This finding supports the conclusion that the domestic and offshore financial markets have become more integrated.

186 Fung and Lo

Interest Rates Spread Eurodollar Rate Minus T-Bill Rate

May 1982 -December 1991 3 . 5

Jan 83 Jan 85 Jan 87 Jan 89 Jan 91 Jan 84 Jan 86 Jan 88 Jan 90

Figure 2 plots the monthly average interest rate differential between the Eurodollar and Treasury bill futures for the entire period 1982-1991. The figure clearly shows that the width of the interest rate spread has declined over time, suggesting that the two markets are becoming more integrated. Thus, this simple figure further supports the empirical analysis of market integration.

Forecasting Analysis Does cointegration analysis outperform the conven-

tional VAR model in forecasting analysis? Engle and Yo0 [lo] suggest in a simulated study that the cointegration analysis outperforms the VAR model in a multi-step forecast. Using cattle futures prices, however, Bessler and Covey [21 find that VAR marginally outperforms cointegration analysis.

The authors compare the forecasting performance for three models: the cointegration model, the VAR model, and the random walk model. The authors include the random walk model because prior studies have assumed futures prices behave like a random walk (Gold- enberg [ 151 and Samuelson [361).


Following Campbell and Shiller [51 and Bessler and Covey [2], equation (8) can be written in an equivalent form in term of lagged AY, and AZ,:

i = l i = l

k b

i = l i = l

where u, and u, are white noise residuals, and A,, B,, C,, and D, are parameter^.^ Equations (10) and (11) allow multi-steps-ahead forecast (see Sargent [331).6

In generating the out-of-sample multi-step forecasts, the first 2,100 observations were used as a bench- mark for estimation. The authors also use “rolling-over regressions” to update more current information for forecasting because of the time-varying relationship docu- mented in the previous section. Other benchmarks for the estimation period were tried, with similar results, not reported here. Table 6 reports the mean squared error (MSE) measures on forecasts at various horizons for the Eurodollar and Treasury bill models for the three specifications.

Several interesting results emerge. For one-day- ahead forecasts, the random walk model has the best forecast in terms of mean squared errors. These results are not surprising, and are consistent with the literature on financial market^.^

For the longer-horizon forecasts, the cointegration model and the VAR model outperform the random walk model. When the former two models are compared, the cointegration model outperforms the VAR in the longer- horizon forecast. These findings are consistent with the results in Engle and Yo0 [lo].

Summary This study uses Eurodollar and Treasury bill fu-

tures to examine the ex ante relationship between do-

188 Fung and Lo

TABLE 6

Mean Squared Errors on Out-of-Sample Forecasts of Eurodollar and Treasury Bill Futures, 1982-1991

Forecast Horizon (days)

1 5 10 20

Eurodollar Model

Random Walk (RW) 0.007766 0.030502 0.061837 0.1268323

Cointegration Model 0.008244 0.030354 0.061807 0.1266419 Improvement over RW* -6.16% 0.48% 0.05% 0.15%

VAR 0.008352 0.030639 0.061879 0.12683016 Improvement over RW -7.55% -0.45% -0.068 0.002%

Treasury Bill Model

Random Walk Model (RW) 0.02991 0.065840 0.105743 0.1746166

Cointegration Model 0.030126 0.065403 0.105424 0.17349800 Improvement of RW -0.73% 0.66% 0.30% 0.32%

VAR 0.030404 0.065455 0.105813 0.17405793 Improvement over RW -1.66% 0.58% -0.07% 0.002%

* Positive means improvement over random walk model; negative, otherwise.

mestic and external interest rate markets for the period 1982-1991. It can be concluded that the ex ante domestic interest rates have a direct and inverse effect on Euro- dollar futures. This relationship occurs even in the earlier periods. The reverse causality (i.e., Eurodollar effect on Treasury bill market) is consistent with the price discovery function of the futures market literature in that the Eurodollar futures anticipates its underlying cash market’s interest rate movement, which is expected to affect the Treasury bill market as these two markets become more integrated.

The results also indicate that the ex ante interest rate relationship between domestic and offshore markets is time-varying. The adjustment process appears to hap- pen more quickly in recent years, a finding consistent with the belief that international financial markets are


becoming more integrated because of fewer regulations and restrictions in these markets in recent years.

Finally, a comparison of the multi-step ahead forecasts for the cointegration model, the VAR model, and the random walk model shows that the random walk model has the best one-day-ahead forecast, while the cointegration model performs the best in a longer-horizon forecast. These results suggest that interest rates do not follow a random walk over a longer horizon, and therefore have important implications for forecasting future interest rate movements.

Notes

1. This study is also of interest to the hedging literature. Since the Eurodollar futures contract joined the T-bill futures contract in 1981, spreading between the two (simultaneous purchase of one contract and sale of the other) has become increasingly popular.

2. Dynamic steady-state equilibrium involves the addition of a term as the constant vector of steady-state growth rates to equation (2) omitted here for convenience. The constant term in the empirical analysis was in- cluded.

3. Parzen’s [31] criterion was also to select the optimal lag length in the VAR; results are similar, but not reported here.

4. Since the error correction term, zt - contains the cross-market variable, X , - 1, it is appropriate to test its significance. All yi without including the error correction term are tested; results are similar.

5 . To be exact, the number of lags in the lagged A Z t - i terms should be one more than that of the lagged AYt - I terms. For details, see Camp- bell and Shiller [51.

6. For detailed forecasting procedure, see Bessler and Covey [21, Campbell and Shiller [51, and Engle and Yo0 [lo].

7. The authors appreciate the reviewer for pointing this out.

References

[I] Ashley, Richard. “A Simple Test for Regression Instability.” Eco- nomic Inquiry 22(April 1984): 253-268.

[23 Bessler, David A., and Ted Covey. “Cointegration: Some Results on U.S. Cattle Prices.” Journal of Futures Markets ll(Apri1 1991): 461-474.

[31 Bilson, John. “The ’Speculative Efficiency’ Hypothesis.” Journal of Business 54(July 1981): 435-451.

[41 Box, George, and Gwilym M. Jenkins. Time Series Analysis: Fore- casting and Control. Rev. ed. San Francisco: Holden-Day, 1976.

190 Fung and Lo

[51 Campbell, John Y., and Robert J. Shiller. “Interpreting Cointe- grated Models.” Journal of Economic Dynamics and Control 12( 1988): 505-522.

[61 Campbell, John Y., and Richard H. Clarida. “The Term Structure of Euromarket Interest Rates: An Empirical Investigation.” Journal of Monetary Economics 19(January 1987): 25-44.

[71 Cosset, Jean-Claude. “Forward Rates as Predictors of Future Inter- est Rates in the Eurocurrency Market.” Journal of International Business Studies (Winter 1982): 71-83.

[81 Dickey, David A., and Wayne A. Fuller. “Distribution of the Esti- mators for Autoregressive Time Series with a Unit Root.” Journal of the American Statistical Association 74 (June 1979): 427-431.

[91 Engle, Robert, F. and C.W.J. Granger. “Co-integration and Error Correction: Representation, Estimation, and Testing.” Economet- rica 35(March 1987): 251-276.

[lo] Engle, Robert F., and Byung S. Yoo. “Forecasting and Testing in Co-Integrated Systems.” Journal of Econometrics 35(May 1987): 143-159.

[111 Fuller, Wayne A. Introduction to Statistical Time Series. New York John Wiley and Sons, 1976.

[121 Fung, Hung-Gay, and Steven Isberg. “The International Transmis- sion of Eurodollar and U.S. Interest Rates: A Cointegration Analy- sis,’’ Journal of Banking and Finance 16 (August 1992): 757-769.

[131 Fung, Hung-Gay, and Wai K. Leung. “The Pricing Relationship of Eurodollar Futures and Eurodollar Deposit Rates.” Journal of Fu- tures Market 13(April 1993) 115-126.

1141 Giddy, Ian, Gunter Dufey, and Sangkee Min. “Interest Rates in the U.S. and Eurodollar Markets,” Weltwirtschaftliches Archiv 115(1979): 51-67.

1151 Goldenberg, David H. “Memory and Equilibrium Futures Prices,” Journal of Futures Markets SfJune 1991): 199-213.

[161 Grabbe, J. Orlin. International Financial Markets. 2nd ed. New York: Elsevier, 1991.

[171 Granger, C.W.J. “Developments in the Study of Cointegrated Eco- nomic Variables,” Oxford Bulletin of Economics and Statistics 48(August 1986): 213-228.

[181 Hartman, David G. “The International Financial Market and U.S. Interest Rates,” Journal of International Money and Finance 3(April 1984): 91-103.

1191 Hegde, Shantaram P., and Bill MacDonald. “On the Informational Role of Treasury bill Futures.” Journal of Futures Markets 6(Win- ter 1986): 629-643.


[203 Hendershott, Patric H. “The Structure of International Interest Rates: The U.S. Treasury Bill Rate and the Eurodollar Deposit Rate.” Journal of Finance 22(September 1967):455-465.

[21] Johansen, Soren. “Estimation and Hypothesis Testing of cointegration Vectors in Gaussian Vector Autoregressive Models.” Economet- rica 59(November 1991): 1,551-1,580.

[22] Johansen, Soren. “Statistical Analysis of Cointegration Vectors.” Journal of Economic Dynamics and Control 12(June 1988): 231- 254.

[23] Johansen, Soren, and Katarina Juselius. “Maximum Likelihood Es- timation and Inference on Cointegration-With Applications to the Demand for Money.” Oxford Bulletin of Economics and Statistics 52(May 1990): 169-210.

[241 Jorion, Philippe. “Term Premiums and the Integration of the Euro- currency Markets.” Journal of International Money and Finance 11(February 1992): 17-39.

[25] Kaen, Fred R., and George A. Hachey. “Eurocurrency and National Money Market Interest Rates.” Journal of Money, Credit and Banking 15(August 1983): 327-338.

[26] Kamara, Avraham. “Forecasting Accuracy and Development of a Financial Market: The Treasury Bill Futures Market.” Journal of Futures Markets 10(August 1990): 397-405.

[271 Krol, Robert. “The Term Structure of Eurodollar Interest Rates and its Relationship to the US. Treasury Bill Market.” Journal of Znter- national Money and Finance G(September 1987): 339-354

[281 Kwack, Sung Y. “The Structure of International Interest Rates: An Extension of Hendershott’s Tests.” Journal of Finance 26(Septem- ber 1971): 897-900.

[29] Levin, Jay H. “The Eurodollar Market and the International Trans- mission of Interest Rates.” Canadian Journal of Economics 7(May 1974): 205-224.

[30] MacDonald, S. Scott, and Scott E. Hein. “Futures Rates and For- ward Rates as Predictors of Near-Term Treasury bill Rates.” Jour- nal of Futures Markets 3(June 1989): 249-262.

[31] Parzen, Emanuel. “Multiple Time Series: Determining the Order of Approximating Autoregressive Schemes.” In Multivariate Analysis- ZV, edited by P. Krishnaian. Amsterdam: North Holland, 1977.

[321 Rendleman, Richard, Jr . and Christopher E. Carabini. “The Ef i - ciency of the Treasury Bill Futures Market.” Journal of Finance 34(September 1979): 895-914.

[331 Sargent, Thomas. Macroeconomic Theory. New York: Academic Press, 1979.

192 Fung and Lo

[341 Schnitzel, Paul. “Testing for the Direction of Causality between the Domestic Monetary Base and the Eurodollar System.” Weltwirt- schaftliches Archio 119( 1983): 616-629.

[35] Schwarz, G. “Estimating the Dimension of a Model.”AnnaZs of Sta- tistics 6(March 1978): 461-464.

[361 Samuelson, Paul. “Proof That Properly Anticipated Prices Fluctu- ate Randomly.” Journal of Management Review (Spring 1965): 41- 49.

1371 Siegel, Daniel R., and Diane F. Siegel. Futures Markets. Chicago: Dryden Press, 1990.

[38] Swanson, Peggy. “The International Transmission of Interest Rates: A Note on Causal Relationships Between Short-Term Exter- nal and Domestic U.S. Dollar Returns.” Journal of Banking and Fi- nance 12(1988): 563-573.

[391 Walz, Daniel T., and Roger W. Spencer. “The Information Content of Forward Rates: Further Evidence.” Journal of Financial Re- search 12(Spring 1989): 69-81.

An Empirical Examination of the Ex Ante International Interest Rate Transmission

Documents

Transcript of An Empirical Examination of the Ex Ante International Interest Rate Transmission