The influence of macroeconomic news on term and quality spreads

The Quarterly Review of Economics and Finance 45 (2005) 84–102

The influence of macroeconomic news on term and quality spreads Sanjay Ramchander a,∗ , Marc W. Simpson b,1 , Mukesh K. Chaudhry c,2 a

Department of Finance and Real Estate, College of Business, Colorado State University, Fort Collins, CO 80523, USA b Department of Economics and Finance, College of Business Administration, University of Texas—Pan American, Edinburg, TX 78541, USA c Department of Finance, Eberly College of Business and Information Technology, Indiana University of Pennsylvania, 324 Eberly Complex, Indiana, PA 15705, USA

Received 28 March 2002; received in revised form 21 October 2002; accepted 3 February 2003 Available online 7 May 2003

Abstract The role of macroeconomic news on interest rates and yield spreads are of great interest to market observers and policy makers alike. The study investigates the impact of U.S. macroeconomic surprises on the daily market yields of seven debt-market instruments. In addition, various measures of term and quality spreads are constructed in order to ascertain their response to economic surprises. Several important results are documented. First, of the 23 types of ‘news’ release announcements, 17 of them have a significant influence on interest rate changes. Second, changes in the Treasury yields and the corporate bond yield are positively impacted by surprises in the CPI and non-farm payroll figures. Third, movements in the prime interest rate, which is one of the base rates used by banks to price short-term business loans, is positively influenced by an unexpected increase in business activity. Fourth, the Fed funds rate is found to be an important driving variable in the interest rate system. Changes in the Fed funds rate significantly influences every other security in the system, with the exception of corporate bonds, but is itself largely insulated from the movement in yields of other securities. Finally, the study finds several sources of news that impact the term and quality spread measures. Interestingly, news that would encourage economic agents to revise their inflationary expectations upward have a positive influence on the term spread, but on the other hand, they are seen

∗

Corresponding author.Tel.: +1-970-491-6681. E-mail addresses: [email protected] (S. Ramchander), [email protected] (M.W. Simpson), [email protected] (M.K. Chaudhry). 1 Tel.: +1-956-292-7358. 2 Tel.: +1-724-357-5746. 1062-9769/$ – see front matter © 2003 Board of Trustees of the University of Illinois. All rights reserved. doi:10.1016/S1062-9769(03)00030-9

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

85

to narrow the quality spread. In general, the results are in accordance with the major theories of interest rate behavior and determination. © 2003 Board of Trustees of the University of Illinois. All rights reserved. JEL classification: E43; E44; G1; G14 Keywords: Macroeconomic news; Term spreads; Quality spreads

1. Introduction How do unanticipated macroeconomic announcements impact interest rates? What effects do they have on term and quality spreads? And, are these influences consistent with existing theories on interest rate determination? These questions are important since movements in interest rates and their corresponding spreads are often considered to be leading indicators of economic activity, and provide information valuable in portfolio management. In response, a number of studies have examined this issue and document a significant bond market impact from numerous macroeconomic announcements including money supply (Urich & Wachtel, 1981), industrial production (Edison, 1996), producer price index (Smirlock, 1986), consumer price index (McQueen & Roley, 1993), retail sales (Fleming & Remolana, 1997), unemployment rate (Cook & Korn, 1991), nonfarm payroll employment (Krueger, 1996), and new home sales (Balduzzi, Elton, & Green, 2001). There are still other studies that have examined the time series dynamics of term and credit risk premiums. For instance, Jarrow and Turnbull (2000) conclude that credit (or quality) spreads are affected by common economic underlying variables, and argue that this information may be useful in predicting the number of corporate defaults. Consistent with structural models of default risk (as in Merton, 1974), Bevan and Garzarelli (2000) find that variables such as leverage, volatility and cash flow generation explain a significant portion of the variance in corporate bond spreads. In a related paper Neal, Rolph, and Morris (2000) examine the yields on corporate and Treasury securities and find their relationship with each other to be contingent on the time horizon. Specifically, over the long-run, a rise in Treasury rates produces a proportionately larger rise in corporate rates, thus widening the credit spread and inducing a positive relationship between spreads and Treasury rates. In a parallel fashion, several studies have also sought to explain the term structure embodied in fixed-income securities. The consensus view is that the term structure contains little power to forecast near-term changes in the short-term interest rates. On the other hand, interest rate predictions seem to improve for extended forecast horizons.1 The purpose and contribution of this paper is two-fold. First, we investigate the impact of a broad set of unanticipated U.S. macroeconomic information (‘news’) on the yields of 1 Among the many studies that reject the expectation hypothesis for the U.S. are Shiller, Campbell, and Schoenholtz (1983), Campbell and Shiller (1984, 1991), Fama (1984), Mankiw (1986), Fama and Bliss (1987), Froot (1989), and Fama (1990). Shiller (1987) provides a comprehensive survey of this literature.

86

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

several debt instruments. Next, we construct various measures of term and quality spreads and ascertain their response to economic news. The distinguishing feature of our paper is that we model the joint behavior of: (a) long- and short-term interest rates, and (b) interest rate on default-prone and default-free securities in an equilibrium framework using the cointegration methodology. The use of cointegration analysis is particularly suitable since it ensures and preserves the strong substitutability and equilibrium nature of the relationship among the different fixed income securities. To our knowledge, there exists no formal study that examines the exact nature and role of macroeconomic surprises on both yields and yield spreads using this framework. Furthermore, the data set covers the time period from February 1, 1991 to September 1, 2000, over 9 years of daily observations. This is important for accurately measuring long-run effects. The results from this study would serve to either validate or reject several prominent studies on interest rate behavior and determination. In an open economy all credit instruments share important feedback relationships with each other. As such, astute portfolio managers will watch for temporary divergences among the various interest rates and, when such divergences exceed the manager’s required risk premium, an inter-market spread swap may be conducted to generate arbitrage profits. Because of these linkages, the yields on different securities are all affected by aggregate macroeconomic activity. In this regard, evidence of cointegration among the various interest rate series would support the intuition that the associated debt instruments are closely linked with each other in the long-run and they cannot evolve in arbitrary ways. Furthermore, the methodology lends itself to a vector error-correction estimation procedure that captures the dynamic causal relationships and explicitly incorporates the role of macroeconomic surprises on yield spreads. The rest of the paper is organized as follows. Section 2 describes the data and the model specification, while Section 3 reports the findings our study. A summary and conclusions are offered in Section 4.

2. Data and model specification 2.1. Data In this paper, we examine the behavior of six different interest rate series. Specifically, these are: the Fed funds rate (FFR), the 3-year Treasury note rate (3YR), the 10-year Treasury note rate (10YR), the 30-year Treasury bond rate (30YR), the prime interest rate (PR) and the Moody’s Baa corporate bond rate (Baa).2 Daily interest rate data for all the securities were obtained from the Board of Governors of the Federal Reserve for the period February 1, 1991 to September 1, 2000. The Moody’s index comprises an equally-weighted sample of yields on 75 to 100 bonds that are issued by large non-financial corporations. Each bond issue included in the index has a face value exceeding $100 million, an initial maturity of 2 The yields on Treasury securities are of ‘constant maturity’ and are derived by interpolating market yields from the daily yield curve. The yield curve is based on the closing market bid yields on actively trade Treasury securities in the over-the-counter market.

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

87

more than 20 years, and a liquid secondary market.3 The daily effective Fed funds rate is a weighted average of rates on trades through N.Y. brokers. The prime interest rate is the rate posted by 25 large insured U.S.-chartered commercial banks. Our empirical objective is achieved by measuring the impact of macroeconomic surprises on (a) debt yields, and (b) measures of term and quality yield spreads. We construct three measures of term spreads and two measures of quality spreads. They are computed as follows:4 • Term spread indicators ◦ 3YR minus FFR, ◦ 10YR minus FFR, and ◦ 30YR minus FFR • Quality spread indicators ◦ PR minus 3YR ◦ Baa minus 30YR. We use the consensus estimates of 23 types of macroeconomic announcements provided by Money Market Services (MMS), together with the actual value of the announcements to compute the unanticipated component of the surprises. Pearce and Roley (1985), Almeida, Goodhard, and Payne (1998), and Balduzzi et al. (2001) find, with few exceptions, that the forecasts provided by MMS are unbiased. The value of the surprise is standardized as: Surprisei =

Actuali − Forecasti , σi

(1)

where σi is the standard deviation of the ith surprise. Thus, when regressing the dependent variables on surprises, the regression coefficient is the change in the dependent variable for a one standard-deviation change in the surprise. Since the standard deviation, σi , is constant across all the observations for a given announcement i, this adjustment does not affect either the significance of the estimates or the fit of the regressions. The benefit of standardization is that it allows us to compare the size of regression coefficients associated with surprises across different announcements. Table 1 presents a list of the different types of macroeconomic announcements considered in the study, and descriptive statistics on the forecasts, actual values, and the surprise component. Note that the statistics listed for the surprises in Table 1 are the data before applying the standardization procedure mentioned above.

3 It is important to note that the index does have some inherent shortcomings. Specifically, yield changes on the index over consecutive periods do not necessarily measure the change in the yield on the same set of bonds. Furthermore, since the index includes callable bonds, credit spread changes may partially reflect changes in the value of the option to call. Unfortunately, since a precise quantification of these distortions is difficult to obtain, and there are no satisfactory alternatives, the study relies on the Moody’s index (see Bevan & Garzarelli, 2000, for a discussion of these and other measurement problems). 4 The choice of our term and quality indicators was based on the existent literature in this area. We also considered other combinations of quality spread that included the Moody’s Baa-rated corporate bonds. The results from these estimations are not reported in the study since they were not qualitatively different.

88

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

Table 1 Descriptive statistics—macroeconomic announcements Announcement

Forecast Mean

Autos (millions-annual) Business inventories (%) Capacity utilization (%) Construction spending (%) Consumer credit ($ billion) Consumer price index (%) Durable good orders (%) Factory orders (%) Hourly earnings (%) Housing starts (millions) Industrial production (%) Leading indicators (%) New home sales (thousands) Non-farm pay (thousands) Personal consumption (%) Personal income (%) Producer price index (%) Real GDP (%) Retail sales (%) Trade balance ($ billion) Treasury budget ($ billion) Unemployment (%) U.S. NAPM (% level)

6.63 0.22 81.85 0.22 4.64 0.26 0.28 0.32 0.34 1.38 0.17 0.12 707 152 0.40 0.40 0.20 2.80 0.35 −11.61 −9.01 5.65 51.95

Actual S.D. 0.42 0.27 1.93 0.57 3.35 0.13 1.36 1.89 0.85 0.20 0.35 0.39 139 106 0.20 0.22 0.24 1.99 0.35 6.49 38.39 1.07 4.45

Mean 6.63 0.26 81.91 0.29 5.14 0.24 0.33 0.39 0.29 1.39 0.22 0.13 713 135 0.44 0.44 0.14 2.95 0.29 −11.83 −8.11 5.61 51.77

Surprise S.D.

Mean

S.D.

0.62 0.35 1.93 1.23 4.70 0.18 3.52 2.10 0.24 0.21 0.50 0.47 149 171 0.32 0.34 0.42 2.10 0.56 6.67 39.95 1.06 4.66

0.00 0.05 0.06 0.08 0.50 −0.02 0.05 0.07 −0.05 0.01 0.04 0.01 6.00 −16 0.04 0.04 −0.06 0.14 −0.06 −0.22 0.89 −0.03 −0.17

0.45 0.22 0.34 1.16 3.12 0.12 2.87 0.65 0.85 0.07 0.26 0.16 55.0 122 0.20 0.23 0.26 0.56 0.43 1.48 8.51 0.15 1.92

2.2. Model specification The attributes of substitutability and complementarily create price interdependence and linkages among the various debt-market instruments. Consequently, this study examines the impact of announcements on yields in the framework of the cointegration methodology. The essence of a cointegrating relationship is that the variables in the system share a common unit root process. The methodology is particularly suitable for our study since it incorporates information about long-run equilibrium forces, and at the same time allows for a flexible lag structure, permitting the data to play a strong role in the specification of the model’s dynamic structure. In addition, this methodology enables us to investigate the response of credit instruments to unanticipated macroeconomic shocks. The empirical process in our study is comprised of four parts: (1) testing for a unit root, I(1), in each interest rate and spread series; (2) testing for the number of cointegrating vectors in the interest rate system, if we are unable to reject the null hypothesis of a unit root in the variables; (3) estimating, in the framework of the vector error-correction model (or VECM), the dynamic relationships among interest rates and documenting their response to macroeconomic news; and (4) examining the impact of news announcements on both the term and quality spreads. In this paper, we emphasize the Phillips (1991) and Perron (1988) (PP) and KPPS (see Kwiatkowski, Phillips, Schmidt, & Shin, 1992) unit root methodology in order to ascertain

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

89

the time series properties of each individual interest rate series. The PP test is preferred to the augmented Dickey–Fuller (ADF) methodology since the ADF test is known to lose power as the lag intervals increase. Moreover, the PP test allows for a weak dependence and heterogeneity in residuals (see Handa & Ma, 1989). The KPSS test too has been found to be robust to different nonstationary processes (see Lee & Schmidt, 1996). The null hypothesis of the PP test is that the series is nonstationary. In contrast, under the KPSS methodology, the null hypothesis of stationarity is tested against the alternative of a unit root. The maximum lag order for the test is calculated by using the lag-order rule provided by Schwert (1989). Both unit root tests are implemented with and without a deterministic trend. To investigate the existence of a long-term equilibrium relationship across the various interest rates, we employ the maximum-likelihood test procedure established by Johansen and Juselius (1990) and Johansen (1991).5 Specifically, if Yt is a vector of n stochastic variables, then there exists a p-lag VECM with Gaussian errors of the following form: p n
n

Y1,t = α1 + β1,j,i Yj,t−i + ω1,k Yk,t−1 + ε1,t (2.1) j=1 i=1

Y2,t = α2 +

p n



k=1

β2,j,i Yj,t−i +

n


ω2,j Yj,t−1 + ε2,t

(2.2)

.. . p n
n

βn,j,i Yj,t−i + ωn,k Yk,t−1 + εn,t Yn,t = αn +

(2.n)

j=1 i=1

j=1

j=1 i=1

k=1

where α1 , . . . , αn , β1,1,1 , . . . , βn,n,p and ω1,1 , . . . , ωn,n are coefficients, and the ε1 , . . . , εn terms are white noise errors. Let Ω be an n × n matrix containing the ω coefficients.6 The cointegration procedure yields two likelihood ratio tests for determining the rank of Ω, based on the number of nonzero eigenvalues in Ω—referred to as the trace test and the maximum eigenvalue test. In this study, we utilize only the Trace test because it has been shown to be more robust than the maximum eigenvalue test (see Cheung & Lai, 1993). The trace test statistic is given by: n
TR = ln(1 − λi ) (3) i=r+1 5 This approach is especially appealing since it provides a unified framework for estimating and testing cointegrating relations in the context of a VECM model. Thus, by treating all the variables as endogenous, this approach avoids the arbitrary choice of the dependent variable in the cointegrating equations, as in the Engle–Granger methodology. They have also been shown to have good large- and finite-sample properties (see Phillips, 1991; Gonzala, 1994). 6 The focal point of conducting Johansen’s cointegration test is to determine the rank (r) of the n × n matrix. In the present application, there are three possible alternatives. First, it can be of full rank, which would imply that all variables in the model are stationary; however, this would contradict earlier finding that the variables in the system are nonstationary. Second, the rank of Ω can be zero, in which case it indicates that there is no long-run relationship among the various interest rates. In instances when Ω is of either full rank or zero rank, it will be appropriate to estimate the model in either levels or first differences, respectively. Finally, in the intermediate case when 0 < r < n (reduced rank) there are r cointegrating relations among the elements of Yt and n − r common stochastic trends. The lag length was determined by Akaike’s Information Criterion (AIC; see Akaike, 1973).

90

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

where λr+1 , . . . , λn are the n − r smallest squared canonical correlations between Yt−k and Yt series, corrected for the effect of the lagged differences of Yt . Causal inferences are made by estimating the parameters of the following system of equations (VECM): Y1,t = µ1 +

Y2,t = µ2 + .. . Y6,t = µ6 +

m 6



γ1,k,j Yk,t−j +

23


φ1,i Ai +

h


δ1,l zl,t−1 + ε1,t

k=1 j=1

i=1

l=1

m 6



23


h


γ2,k,j Yk,t−j +

k=1 j=1 m 6



φ2,i Ai +

i=1

γ6,k,j Yk,t−j +

k=1 j=1

23


(4.1)

δ2,l zl,t−1 + ε2,t

(4.2)

δ6,l zl,t−1 + ε6,t

(4.6)

l=1

φ6,i Ai +

i=1

h
l=1

where  is the first-difference operator, Y1 , . . . , Y6 represents the daily interest rates for the six debt instruments, Ai is a vector of exogenous variables that contains the surprise information associated with the ith macroeconomic variable (with i = 1, 2, . . . , 23), zt is the error-correction term and ε1,t , . . . , ε6,t are the residuals. The coefficients φ1,1 , . . . , φ6,23 measure the impact of each of the announcements on interest rates. The error-correction term measures the deviations of the series from the long run equilibrium relation. This deviation affects the short-run behavior of Yt , with the error-correction coefficients δ1,1 , . . . , δh,6 , describing how quickly the interest rate variables respond to the deviations. The primary focus of this paper is to gauge the impact of macroeconomic news on term and credit spreads. If the spreads themselves exhibit nonstationarity and cointegration, it would seem appropriate to examine the impact of the surprises on the spreads in the context of another VECM:7 S1,t = χ1 +

g 5



η1,k,j Sk,t−j +

k=1 j=1

S2,t = χ2 + .. . S5,t = χ1 +

η2,k,j Sk,t−j +

k=1 j=1

k=1 j=1

ϕ1,i Ai +

i=1

g 5



g 5



23


23


23
i=1

θ1,l zl,t−1 + ε1,t

(5.1)

l=1

ϕ2,i Ai +

i=1

η5,k,j Sk,t−j +

d


d


θ2,l zl,t−1 + ε2,t

(5.2)

θ5,l zl,t−1 + ε5,t

(5.5)

l=1

ϕ5,i Ai +

d
l=1

where S1 to S5 are the five credit spreads. While the effects of the announcements on spreads are estimated in a single vector error-correction system, for the sake of exposition, the effects of the announcements on the term and quality spreads are reported separately in Tables 5 and 6, respectively. 7

We thank an anonymous referee for this suggestion.

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

91

In addition to the above VECM of the spreads, for robustness, we also calculate the impact of the surprises on the spreads indirectly using the coefficients from the VECM of the yields (obtained from Eqs. (4.1)–(4.6)). For example, the effect of a surprise variable on the 3-year term spread can be deduced by subtracting the coefficient for that variable when the dependent variable is the 3-month T-bill from the coefficient for the same variable when the Fed funds rate is the dependent variable. Wald coefficient tests are then used to determine the statistical significance of the difference in the coefficients. The results of such a procedure for the term and quality spreads are also reported in Tables 5 and 6.

3. Empirical results 3.1. Unit root and cointegration tests The time series properties of debt instrument yields and the economic surprise are first investigated. Table 2 reports the PP and KPSS unit root test results for each individual interest rate series over the entire sample period. The evidence suggests that all of the interest rates are integrated of order one, I(1). These results are largely consistent with several studies that have examined the stochastic nature of interest rates. Table 2 Stationarity of the interest rates

Levels Interest rates Fed funds rate Prime rate Moody’s Baa bonds 3-year Treasury 10-year Treasury 30-year Treasury First differences Interest rates Fed funds rate Prime rate Moody’s Baa bonds 3-year Treasury 10-year Treasury 30-year Treasury

PP test

KPSS test

H0 : nonstationarity

H0 : stationarity

w/o trend

w/ trend

−2.12 −1.35 −1.79 −2.36 −2.12 −1.68

−1.99 −1.65 −1.69 −2.08 −2.67 −3.10

−82.20∗∗∗ −51.36∗∗∗ −79.58∗∗∗ −44.85∗∗∗ −44.62∗∗∗ −47.30∗∗∗

−82.85∗∗∗ −51.54∗∗∗ −79.59∗∗∗ −44.87∗∗∗ −44.61∗∗∗ −47.29∗∗∗

w/o trend

w/ trend

1.15∗∗∗ 8.29∗∗∗ 5.93∗∗∗ 1.62∗∗∗ 5.40∗∗∗ 7.11∗∗∗

1.19∗∗∗ 1.99∗∗∗ 1.07∗∗∗ 0.79∗∗∗ 0.55∗∗∗ 0.35∗∗∗

0.45 0.05 0.14 0.15 0.06 0.04

0.10 0.03 0.06 0.07 0.05 0.04

This table presents the results of Phillips–Perron (PP) and Kwaitoski, Phillips, Schmidt, and Shin (KPSS) tests for stationarity in the levels and first differences of the interest rates. The null hypothesis of the PP test is that the series is nonstationary. The null hypothesis of the KPSS test is that the series is stationary. Both tests are performed with and without a deterministic trend in the test equation. ***: Statistical significance at the 0.01 level.

92

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

Table 3 Test for cointegration Eigenvalue

Likelihood ratio

5% critical value

1% critical value

Hypothesized no. of CVs

Panel A: Yields 0.051378 199.6837 0.026478 91.50461 0.009904 36.46619 0.004385 16.05163 0.003182 7.039023 0.000245 0.502290

94.15 68.52 47.21 29.68 15.41 3.76

103.18 76.07 54.46 35.65 20.04 6.65

None∗∗∗ At most1∗∗∗ At most 2 At most 3 At most 4 At most 5

Panel B: Yield spreads 0.045215 91.9206 0.036055 46.1133 0.007533 9.75922 0.001927 2.27350 0.000367 0.36358

68.52 47.21 29.68 15.41 3.76

76.07 54.46 35.65 20.04 6.65

None∗∗∗ At most 1 At most 2 At most 3 At most 4

This table presents the results of Phillips–Perron (PP) and Kwaitoski, Phillips, Schmidt, and Shin (KPSS) tests for stationarity in the levels and first differences of the interest rates. The null hypothesis of the PP test is that the series is nonstationary. The null hypothesis of the KPSS test is that the series is stationary. Both tests are performed with and without a deterministic trend in the test equation. ***: Rejection of the hypothesis at the 0.01 significance level.

Table 3 reports Johansen cointegration test results for yields (Panel A) and yield spreads (Panel B). For the trace test, we start with r = 0 and move upwards. We stop the first time we are unable to reject the null hypothesis. For instance, observing Panel A, the hypothesis of r = 0 is rejected as the computed value of the test statistic (199.68) is greater than the 1% critical value (103.18). Similarly, the null hypothesis of r ≤ 1 is also rejected. However, in the next step, the null hypothesis of at most two cointegrating vectors (r ≤ 2) cannot be rejected at the 1% level of significance, suggesting that there are two or fewer cointegrating vectors (CVs) in the system containing yields. Similarly, Panel B results indicate the presence of a long-run, equilibrium relationship among the various yield spreads.8 3.2. Impact of macroeconomics announcements on yields Given the cointegration results, the next stage in our empirical process requires the construction of a VECM which is useful in making causal inferences among the interest rates, and to ascertain the impact of macroeconomic information on interest rates.9 Table 4 provides the results from estimating the parameters given in Eqs. (4.1)–(4.6). The endogenous 8 Prior to testing cointegration of spreads, unit root tests were conducted. Unit root tests generally indicated that all of the spread measures were nonstationary in levels, and attained stationarity after first-differencing. 9 The impact of macroeconomic news on debt instrument yields was also examined in an OLS framework. When compared to OLS regression estimates, VECM results indicated that the inclusion of added control variables improved the precision of estimated coefficients. Additional diagnostics in the form of F- and log-likelihood ratio tests were conducted. Both tests indicated that the lagged endogenous variables and cointegrating vectors in the VECM are not redundant, and therefore should not be omitted.

Table 4 VECM results 3-year Treasury 1.15 1.04 1.05 0.63 1.33 3.13∗∗ 0.17 (0.31) 0.09 (0.19) −0.04 (−0.32) 0.69 (1.38) −0.28 (−0.49) 0.33 (0.50) 0.24 (0.36) 0.31 (0.51) 2.15 (3.20∗∗∗ ) 1.58 (2.50∗∗∗ ) 0.70 (1.10) 1.48 (2.52∗∗∗ ) 0.23 (0.37) −1.10 (−1.79∗ ) 0.57 (0.85) 1.29 (2.09∗∗ ) 2.30 (3.57∗∗∗ ) −0.16 (−0.27) −0.10 (−0.18) −0.88 (−1.41) 0.14 (0.26) 1.55 (2.40∗∗ ) −0.63 (−1.10) −3.13 (−2.98∗∗∗ ) −0.10 (−0.17) 1.05 (1.75∗ )

2.42∗ 1.83 1.91 1.79 0.66 3.07∗∗ −0.01 (−0.01) 0.13 (0.28) −0.10 (−0.80) 0.35 (0.74) −0.46 (−0.84) 0.40 (0.64) 0.40 (0.62) −0.09 (−0.16) 1.76 (2.74∗∗∗ ) 0.78 (1.29) 0.26 (0.42) 1.15 (2.05∗∗ ) 0.53 (0.88) −0.99 (−1.68∗ ) 0.33 (0.51) 1.02 (1.73∗ ) 1.30 (2.12∗∗ ) −0.39 (−0.69) −0.01 (−0.02) −0.71 (−1.19) 0.13 (0.25) 0.93 (1.50) −0.65 (−1.18) −3.27 (−3.25∗∗∗ ) 0.12 (0.21) 0.68 (1.19)

30-year Treasury 0.51 0.94 .046 0.74 0.87 2.66∗∗ −0.09 (−0.19) 0.19 (0.45) −0.10 (−0.93) 0.48 (1.17) −0.51 (−1.07) −0.09 (−0.17) 0.11 (0.20) −0.24 (−0.46) 1.97 (3.52∗∗∗ ) 1.22 (2.33∗∗ ) 0.06 (0.12) 0.63 (1.30) 0.54 (1.02) −0.51 (−1.00) 0.24 (0.43) 0.81 (1.57) 0.99 (1.85∗ ) −0.33 (−0.66) 0.16 (0.33) −0.62 (−1.20) −0.42 (−0.91) 0.59 (1.11) −0.49 (−1.03) −3.00 (−3.43∗∗∗ ) 0.25 (0.52) 0.60 (1.21)

Baa bonds 0.86 14.70∗∗∗ 126.11∗∗∗ 107.66∗∗∗ 1.77 0.02 0.54 (2.09∗∗ ) −0.19 (−0.81) −0.02 (−0.39) 0.10 (0.44) −0.61 (−2.31∗∗ ) −0.52 (−1.72∗ ) 0.13 (0.43) −0.16 (−0.56) 0.79 (2.57∗∗∗ ) 0.54 (1.85∗ ) −0.17 (−0.59) 0.42 (1.57) 0.01 (0.03) −0.15 (−0.52) 0.71 (2.27∗∗ ) 0.40 (1.41) 0.59 (2.00∗∗ ) −0.09 (−0.35) −0.26 (−0.93) 0.37 (1.29) 0.20 (0.77) 0.33 (1.09) −0.19 (−0.74) −0.98 (−2.03∗∗ ) −0.20 (−0.74) 0.71 (2.58∗∗∗ )

Prime rate 4.39∗∗ 0.15 0.43 0.11 0.31 2.54∗ 3.30 (7.97∗∗∗ ) −2.73 (−7.38∗∗∗ ) −0.05 (−0.48) 0.30 (0.82) 0.07 (0.16) 0.02 (0.04) 0.92 (1.86∗ ) −0.21 (−0.47) 0.71 (1.42) 0.08 (0.17) −0.29 (−0.61) 0.57 (1.32) 0.30 (0.64) 0.76 (1.66∗ ) 0.88 (1.77∗ ) −0.25 (−0.55) 0.55 (1.16) −2.23 (−5.08∗∗∗ ) 1.48 (3.37∗∗∗ ) −0.16 (−0.35) 0.01 (0.02) 1.18 (2.47∗∗∗ ) −0.78 (−1.84∗ ) 0.53 (0.68) −1.15 (−2.67∗∗∗ ) −0.12 (−0.27)

0.30 1.66 0.35 4.59∗∗∗ 0.88 18.72∗∗∗ −7.41 (−3.76∗∗∗ ) 20.22 (11.52∗∗∗ ) −0.34 (−0.73) 2.40 (1.38) −1.18 (−0.58) 0.80 (0.35) −0.69 (−0.29) −5.51 (−2.54∗∗∗ ) −4.29 (−1.82∗ ) −4.48 (−2.02∗∗ ) 2.47 (1.10) −0.78 (−0.38) −0.30 (−0.14) −1.22 (−0.57) 4.44 (1.87∗ ) −2.08 (−0.96) 3.91 (1.73∗ ) −2.34 (−1.12) 1.89 (0.91) 0.52 (0.24) 3.12 (1.59) 1.28 (0.56) −8.03 (−4.00∗∗∗ ) −3.51 (−0.95) 0.39 (0.19) 3.31 (1.57)

93

Coefficients have been multiplied by 100. t-statistics are in parentheses. F: Indicates that the variable reported is an F-statistic. *, **, ***: Statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.

Fed funds S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

Lagged 3-year Treasury (F) Lagged 10-year Treasury (F) Lagged 30-year Treasury (F) Lagged Baa bond (F) Lagged prime (F) Lagged Fed funds (F) z1,t−1 z2,t−1 Constant Autos Business inventories Capacity utilization Construction spending Consumer credit Consumer price index Durable good order Factory orders Hourly earnings Housing starts Industrial production Leading indicators New home sales Non-farm pay Personal consumption Personal income Producer price index Real GDP Retail sales Trade balance Treasury budget Unemployment U.S. NAPM

10-year Treasury

94

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

variables in the system include lagged variables of the interest rates and the error-correction terms (residuals) from the two cointegrating equations. The joint significance of the lagged coefficient values are provided by the F-statistic. If the coefficients are statistically significant, it implies that the lagged variables in the system are important in predicting current movement of the dependent variables, and the dependent variables in the equation adjust to the previous equilibrium error. To identify the effect of each type of macroeconomic announcement on the interest rate, the table reports the associated coefficient values and their marginal significance (t-statistic) from the regression. Several important observations can be made by looking at Table 4. First, the error-correction terms emerge as important channels of influence in mediating the relationship between the different interest rates. This implies that the variables in the system (specifically, the interest rates belonging to Baa corporate bonds, prime loans and Fed funds) have a strong tendency to adjust to their past disequilibria by moving toward the trend values of their counterparts. Furthermore, the prime rate and the Fed funds rate coefficients have opposite signs, indicating that they react in opposite ways to disequilibria in the relationships among the interest rates. This may make sense in light of an understanding that banks would wish to move funds between the Fed funds market and the market for commercial loans, depending on the relative advantages apparent in each market at different points in time. Second, in terms of the short-run dynamics among the various debt instruments, it can be seen that changes in yields have a significant causal influence (in the Granger-sense) on each other. For instance, there is evidence that the 10- and 30-year Treasury rates have a significant impact on Baa-rated corporate bond yields. Furthermore, the 3-year Treasury rates have a short-run impact on the 10-year Treasury note and the prime interest rate. Third, the Fed funds instruments are observed to be an important driving variable in the interest rate system. Changes in the Fed funds rate significantly influences every other security in the system, with the exception of corporate bonds, but is itself largely insulated from the movement in yields of other securities. Fourth, and perhaps most importantly, several (17 out of 23) of the macroeconomic news releases have a significant influence on the daily change in interest rates. Of the 17 announcements that affect the interest rates of at least 3 securities, three describe the perceived condition of economy activity (industrial production, leading indicators and the Treasury budget), one describes the situation in the labor market (non-farm payroll), one describes the inflationary process (CPI) and the last describes the state of consumer demand (durable goods orders). Of the news announcements that affect less than three interest rate instruments, three reflects consumer demand (business inventory, consumer credit, personal consumption and retail sales), two describes the real estate market (construction spending and new home sales), two highlights the labor market situation (unemployment rate and hourly earnings), two sheds light on the economic activity (NAPM and personal income) and lastly one announcement is related to foreign trade (trade balance). The announcements that do no have a statistically significant influence on the interest rates include auto sales, capacity utilization, factory orders, housing starts, PPI and real GDP. One important observation is that the signs of the coefficients that either pertains to inflationary expectations or the perceived level of the economy are generally consistent with the Fisher-effect hypothesis. The Fisher hypothesis posits that the nominal interest rate on a security is composed of an expected real return and a premium to compensate investors for inflation expected over the life of the security. Our results confirm that changes in the

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

95

3YR, 10YR, and 30YR Treasury rates and the corporate bond yield are positively impacted by surprises in the consumer price index (CPI) and non-farm payroll. In addition, 3YR and 10YR Treasury rates have a positive relationship with the surprise in hourly wages. That is, if there is evidence that inflation (as seen in the price level, employment numbers, and wages) is growing at a higher than expected rate, then, market participants revise their expectations upward. These revisions are then quickly reflected in debt security yields in a manner that is consistent with the Fisher and efficient markets hypotheses. It is also interesting to observe that the prime rate, which is one of the base rates used by banks to price short-term business loans, is positively influenced by economic news pertaining to construction spending, industrial production, leading economic indicators, personal income and retail sales. The obvious implication here is that banks increase the rates they charge business in response to an unexpected increase in business activity. It is however surprising to notice the Fed funds rate to behave somewhat differently than other debt instruments. For instance, news pertaining to CPI and durable goods negatively impacts the Fed funds rate, but has a positive influence on much of the other interest rates. Not surprisingly, the yields on the three Treasury securities are significantly, negatively, related to the Treasury budget announcement. This announcement provides crucial information with regard to the future supply of and demand for Treasury instruments. A positive surprise in the Treasury budget announcement means that the budget deficit (surplus) is smaller (larger) than was expected. The lower (higher) the budget deficit (surplus) the lower the future supply of T-bills, T-notes, and T-bonds. Furthermore, in the case of a surplus, the higher the surprise, the more likely it is that the Treasury will retire debt, rather than issue more of it. This affects not only the supply but the demand for Treasuries, as the government begins to buy back its outstanding issues. Positive surprises in the Treasury budget announcement, then, mean that the price of Treasury securities may rise, and therefore the yield will fall. It is further interesting to note that the Baa bonds also share a significant negatively relationship with the Treasury budget surprises. This again points to the interdependence of interest rates. As the price of the longer-term Treasuries begins to rise, corporate bonds become relatively more attractive. As investors shift into the corporate bonds, their prices also begin to rise and the result is a decrease in corporate yields brought about by the decreasing Treasury yields. Several other causal linkages may be inferred from Table 4. For instance, durable good orders positively influence the Treasury yields (specifically, the 3- and 30-year maturity) and corporate bond yields. One can also notice the 3YR Treasury note yield to be most sensitive to the release of economic news (9 out 23 economic announcements have a statistically significant coefficient). It is important to note that the regression coefficients have been standardized to allow drawing inferences on the sensitivity of the yields to macroeconomics news, across the different types of announcements. The coefficients reported in Table 4 can be interpreted as the change in the yields, given a one standard deviation surprise in the announcement. This leads us to conclude that; first, changes in the yields are most sensitive to inflation-related surprises as evinced by the coefficients pertaining to CPI, non-farm payroll, and hourly earnings. Second, as should be expected, the yields on the three Treasury securities display large reactions to surprises in the Treasury budget. Third, in comparing the coefficients

96

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

across the different yields for each announcement, it is striking to note that the Fed Funds coefficients tend to be higher than the coefficients for the same announcements in the regressions for the other yields. That is, while all of the yields tend to react in the same general fashion to the surprises, the Fed Funds rate appears to react with a greater magnitude. 3.3. Impact of macroeconomic announcements on yield spreads The final step in our empirical analysis is to estimate the impact of macroeconomic announcements on term and quality spreads. These results are reported in Tables 5 and 6, respectively. The tables report two sets of results—one where the dependent variables in the VECM are spread measures, and the other where spreads are derived indirectly from the coefficients of the VECM for yields. Since the impact of macroeconomic announcements on spreads is robust to alternative specifications, the focus of our presentation is on the model where the spreads were used in a VECM (see Eqs. (5.1)–(5.5)).10 Specifically, the three term spread measures are calculated by subtracting the Fed funds rate from the 3-, 10-, and 30-year Treasury rates. The two quality spread measures are created by subtracting the 3-year Treasury rate from the prime rate, and by subtracting the 30-year Treasury rate from the Baa yield. The results from Table 5 document several common sources of ‘news’ influence on the term spread variables. An unexpected increase in consumer credit, CPI, durable good orders and trade balance (deficits) each work to widen the term structure spread across the entire 3-, 10- and 30-year horizon. A plausible explanation for this finding is that surprises in the aforementioned macroeconomic variables causes an increase in the investors’ risk aversion (brought about by greater economic uncertainty in the future), and consequently raises the associated term risk premium that is demanded by the market. Furthermore, it is plausible that the observed reaction in the term structure is a manifestation of the supply and demand adjustments in loanable funds market. For instance, an unexpected increase in consumer credit would increase the demand for loanable funds primarily in the shorter-end of the maturity spectrum, thus driving short-term interest rates lower, and consequently widening the term spread. The results for the quality spread, on the other hand, show surprising variability (see Table 6). Notably, the 3-year quality spread measure is seen to be more sensitive to macroeconomic news announcements than the corresponding 30-year quality spread measure. For both quality spread measures, we observe that an unexpected increase in the CPI narrows the quality spread, but a surprise increase in the Treasury budget evokes an increase in the spread. Comparing Tables 5 and 6, the results indicate that news that would encourage economic agents to revise their inflationary expectations upward (such as an unexpected increase in CPI) have a positive influence on term spread, but on the other hand have a negative influence on quality spread. The contrasting results can be explained by using existing 10 An alternative testing strategy was performed, wherein the spreads was grouped by type (either quality or term) and a separate VECM was estimated for each type. Results indicated that the VECM that included all of the spreads in a single system seemed to be better specified than the alternative testing strategy. Furthermore, such a system with added control variables (lagged endogenous variables and cointegrating vectors) provided more robust estimates when compared to an OLS methodology.

Table 5 Term spreads VECM of spreads 3-year term spread

10-year term spread

30-year term spread

3-year term spread

10-year term spread

30-year term spread

0.47 1.63 4.32 0.54 9.77∗∗∗ −38.9 (−18.7∗∗∗ ) 0.31 (0.65) −1.85 (−1.03) 1.09 (0.53) −0.58 (−0.25) 0.84 (0.35) 5.80 (2.58∗∗∗ ) 6.58 (2.70∗∗∗ ) 6.25 (2.73∗∗∗ ) −1.96 (−0.85) 2.33 (1.09) 0.48 (0.21) 0.10 (0.05) −3.86 (−1.57) 3.21 (1.43) −1.78 (−0.76) 2.17 (1.01) −2.12 (−0.98) −1.30 (−0.57) −3.01 (−1.49) 0.18 (0.08) 7.51 (3.61∗∗∗ ) 0.49 (0.13) −0.52 (−0.25) −2.31 (−1.06)

0.18 0.97 2.74 0.34 11.54∗∗∗ −39.2 (−18.8∗∗∗ ) 0.25 (0.53) −2.17 (−1.21) 0.92 (0.44) −0.51 (−0.21) 1.00 (0.41) 5.38 (2.39∗∗ ) 6.19 (2.53∗∗∗ ) 5.44 (2.37∗∗ ) −2.38 (−1.03) 1.98 (0.93) 0.77 (0.33) 0.22 (0.10) −4.11 (−1.68∗ ) 2.95 (1.31) −2.78 (−1.19) 1.92 (0.89) −2.02 (−0.93) −1.15 (−0.50) −3.03 (−1.49) −0.46 (−0.19) 7.50 (3.61∗∗∗ ) 0.34 (0.09) −0.28 (−0.13) −2.71 (−1.24)

0.59 2.48 4.40 0.28 8.76∗∗∗ −39.2 (−19.0∗∗∗ ) 0.26 (0.54) −2.07 (−1.16) 0.93 (0.45) −1.05 (−0.44) 0.71 (0.29) 5.26 (2.36∗∗ ) 6.41 (2.65∗∗∗ ) 5.93 (2.61∗∗∗ ) −2.65 (−1.15) 1.49 (0.70) 0.75 (0.33) 0.70 (0.31) −4.21 (−1.73∗ ) 2.69 (1.21) −3.13 (−1.35) 1.96 (0.92) −1.86 (−0.87) −1.00 (−0.44) −3.57 (−1.78∗ ) −0.84 (−0.36) 7.67 (3.71∗∗∗ ) 0.66 (0.17) −0.15 (−0.07) −2.76 (−1.28)

−1.71 (0.87) 0.89 (0.18) −0.47 (0.05) 0.94 (0.07) 5.83 (6.42∗∗∗ ) 6.44 (5.87∗∗∗ ) 6.05 (7.14∗∗∗ ) −1.77 (0.48) 2.26 (1.02) 0.53 (0.77) 0.12 (0.02) −3.86 (0.89) 3.37 (1.65) −1.61 (0.45) 2.18 (1.88) −1.99 (0.53) −1.40 (0.53) −2.97 (1.98) 0.27 (0.01) 7.40 (12.6∗∗∗ ) 0.38 (0.01) −0.49 (0.04) −2.26 (1.10)

−2.05 (1.26) 0.72 (0.12) −0.39 (0.04) 1.09 (0.10) 5.42 (5.59∗∗ ) 6.05 (5.19∗∗ ) 5.25 (5.39∗∗ ) −2.21 (0.79) 1.93 (0.74) 0.83 (0.91) 0.24 (0.01) −4.11 (1.13) 3.10 (1.36) −2.61 (1.22) 1.95 (1.67) −1.90 (0.46) −1.24 (0.39) −2.98 (2.03) −0.35 (0.15) 7.38 (12.7∗∗∗ ) 0.24 (0.001) −0.27 (0.01) −2.63 (1.50)

−1.92 (1.11) 0.67 (0.13) −0.89 (0.19) 0.81 (0.04) 5.28 (5.40∗∗ ) 6.25 (5.76∗∗ ) 5.70 (6.49∗∗∗ ) −2.41 (0.97) 1.42 (0.39) 0.84 (0.91) 0.71 (0.01) −4.20 (1.28) 2.89 (1.17) −2.92 (1.56) 2.02 (1.79) −1.73 (0.36) −1.15 (0.38) −3.54 (2.95∗ ) −0.68 (0.28) 7.53 (13.5∗∗∗ ) 0.51 (0.01) −0.14 (0.002) −2.71 (1.62)

97

Coefficients have been multiplied by 100. For VECM of spreads results t-statistics are in parentheses. For the results computed from the coefficients in the VECM of yields, χ2 statistics are in parentheses. F: Variable reported is an F-statistic. *, **, ***: Statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

Lags 3-year term spread (F) Lags 10-year term spread (F) Lags 30-year term spread (F) Lags 3-year quality spread (F) Lags 30-year quality spread (F) z1,t−1 Constant Autos Business inventories Capacity utilization Construct spending Consumer credit Consumer price index Durable good order Factory orders Hourly earnings Housing starts Industrial production Leading indicators New home sales Non-farm pay Personal consumption Personal income Producer price index Real GDP Retail sales Trade balance Treasury budget Unemployment U.S. NAPM

From coefficients in VECM of yields

98

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

Table 6 Quality spreads VECM of spreads 3-year quality spread Lags 3-year term spread (F) Lags 10-year term spread (F) Lags 30-year term spread (F) Lags 3-year quality spread (F) Lags 30-year quality spread (F) z1,t−1 Constant Autos Business inventories capacity utilization Construction spending Consumer credit Consumer price index Durable good orders factory orders Hourly earnings Housing starts Industrial production Leading indicators New home sales Non-farm pay Personal consumption Personal income Producer price index Real GDP Retail sales Trade balance Treasury budget Unemployment U.S. NAPM

0.65 1.63 1.01 0.77 1.38 −1.56 (−2.19∗∗ ) −0.01 (−0.03) −0.30 (−0.49) 0.30 (0.42) −0.33 (−0.41) 0.81 (0.97) −0.40 (−0.52) −1.57 (−1.88∗ ) −1.56 (−2.00∗∗ ) −0.97 (−1.22) −0.85 (−1.16) 0.10 (0.13) 1.92 (2.51∗∗∗ ) 0.27 (0.32) −1.47 (−1.91∗ ) −1.60 (−2.01∗∗ ) −2.11 (−2.86∗∗∗ ) 1.65 (2.23∗∗ ) 0.79 (1.02) −0.03 (−0.05) −0.37 (−0.46) −0.27 (−0.38) 3.70 (2.83∗∗∗ ) −1.08 (−1.50) −0.99 (−1.34)

From coefficients in VECM of yields 30-year quality spread 30.47∗∗∗ 12.39∗∗∗ 0.89 98.21∗∗∗ 237.35∗∗∗ 0.44 (0.91) 0.05 (0.46) −0.12 (−0.29) −0.29 (−0.59) −0.33 (−0.61) 0.14 (0.25) 0.09 (0.18) −1.34 (−2.38∗∗ ) −0.93 (−1.75∗ ) 0.11 (0.21) −0.33 (−0.66) −0.49 (−0.92) 0.40 (0.78) 0.42 (0.73) −0.20 (−0.39) −0.27 (−0.50) 0.13 (0.25) −0.26 (−0.52) 0.78 (1.48) 0.65 (1.38) −0.23 (−0.42) 0.25 (0.52) 1.81 (2.04∗∗ ) −0.25 (−0.50) −0.04 (−0.07)

3-year quality spread

30-year quality spread

−0.39 (0.41) 0.35 (0.25) −0.31 (0.11) 0.68 (0.67) −0.53 (0.49) −1.44 (3.04∗ ) −1.50 (3.77∗ ) −0.99 (1.59) −0.91 (1.45) 0.07 (0.03) 1.86 (5.83∗∗ ) 0.31 (0.11) −1.54 (3.83∗∗ ) −1.75 (5.19∗∗ ) −2.07 (8.16∗∗∗ ) 1.59 (4.29∗∗ ) 0.72 (0.80) −0.13 (8.1E−4) −0.37 (0.20) −0.15 (0.05) 3.66 (7.64∗∗∗ ) −1.05 (2.19) −1.17 (2.50)

−0.38 (0.64) −0.10 (0.03) −0.42 (0.27) 0.02 (3.0E−3) 0.08 (0.01) −1.17 (3.86∗∗ ) −0.69 (1.11) −0.23 (0.13) −0.21 (0.19) −0.53 (0.59) 0.37 (1.00) 0.47 (0.77) −0.41 (0.35) −0.40 (0.31) 0.23 (0.27) −0.42 (0.38) 1.00 (2.81∗ ) 0.62 (1.80) −0.27 (0.15) 0.30 (0.44) 2.02 (4.14∗∗ ) −0.45 (0.63) 0.11 (0.05)

Coefficients have been multiplied by 100. For VECM of spreads results t-statistics are in parentheses. For the results computed from the coefficients in the VECM of yields, χ2 statistics are in parentheses. F: Variable reported is an F-statistic. *, **, ***: Statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.

findings on interest rate determination. The positive influence of inflationary expectations on term spread may be attributed to the notion that long-term bond rates are more likely to driven by expected inflation than short-term rates (see Mishkin, 1990; Barr & Campbell, 1997). Goodfriend (1998) suggests that at longer horizon the real interest rate becomes less variable, leaving expected inflation as the dominant factor driving bond returns. As such, an increase in economic activity that engenders inflationary pressures, in accordance with the Phillips-curve view, would lead to an increase in the term spread.

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

99

On the other hand, our results show that evidence of weaker economic growth leads to an expansion in the quality spread. One could argue that the ability to service debt is a function of the cost of servicing that debt and earnings, and in theory the ability of a business to generate future cash flows are closely tied with the macroeconomic environment. Jaffee (1975) and Fama and French (1989) among others argue that credit spreads are significantly related to macroeconomic conditions, both in a statistical and economically meaningful sense.11 Several other results emerge from simultaneously observing Tables 5 and 6. First, the paper demonstrates that the spread measures have a joint lagged error-correction representation, as seen by the significant zt−1 terms. Second, significant short-term relationships among the various spread measures are documented. For example, the 30-year quality spread is significant in influencing the 3-, 10-, and 30-year term spread measure. Third, in comparing the magnitude of the coefficients across the different announcements, surprises in consumer credit, CPI, durable goods orders, and the trade balance all seem to impact term spread to a greater extent than quality spreads. Finally, comparing Tables 4–6, it seems that there are more macroeconomic news impacting yield changes than impacting credit spreads. This would make sense in an informationally-efficient market where only news that has a differential impact on yields should impact spreads. For example, a surprise in non-farm payrolls has a significant positive impact on all of the yields except the prime rate. Non-farm payrolls can be viewed as conveying information about the health of the economy, and about potentially inflationary wage pressures. For most of the yields, the inflationary pressure will be transmuted into an increase in the inflation premium, and a subsequent rise in the yield. For the prime rate, however, an increase in non-farm payrolls may be indicative of healthier, expanding businesses; perhaps caused by an increase in sales or anticipated revenue. Thus, an increase in non-farm payrolls could indicate a decrease in the default risk of loans made at the prime rate. There would be no appreciable impact on the prime rate if the increase in the inflation premium attended to a surprise in non-farm payrolls is approximately offset by a decrease in the default risk associated with the increase in non-farm payrolls. Furthermore, if the inflation premium rises on Treasury securities, but gets canceled out on loans made at the prime rate, there should be a significant, negative, impact on the 3-year quality spread. This is exactly what we observed. Furthermore, we would not observe a significant impact on the other spreads so long as the increased in the inflation premium was fairly consistent across the credit instruments.

4. Summary and conclusions The nature of interest rate behavior and determination are of prime interest to market observers and policy makers alike. For instance, information contained in the term structure and default premium sheds light on the business cycle, credibility of the central bank’s policy making, probability of loan default, portfolio management, and the pricing of credit 11

Ewing (2003) presents a time-series plots showing the default risk premium to be relatively high during recessionary conditions, and during periods corresponding with financial market crisis.

100

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

instruments among others factors. This study undertakes a formal investigation into the role of macroeconomic ‘news’ announcements on interest rate behavior and yield spreads. Apart from its obvious implications, the results from this study would serve to either validate or reject several well-established interest rate theories and evidence. The paper reports several important findings. Of the 23 types of new release announcements, 17 of them have a significant influence on interest rate changes. This is not surprising given the close nature of association between interest rates and macroeconomic variables. Consistent with the Fisher hypothesis, it is observed that the Treasury yields and the corporate bond yield are positively impacted by news that exacerbates inflationary expectations (i.e., surprises in the CPI and non-farm payroll figures). Moreover, movements in the prime interest rate, which is one of the base rates used by banks to price short-term business loans, is positively influenced by an unexpected increase in business activity. Interestingly, the Fed funds rate is found to be an important driving variable in the interest rate system. Specifically, changes in the Fed funds rate significantly influences most interest rates in the system, but is itself largely insulated from the movement in yields of other securities. In sum, there is an overwhelming evidence of interdependence among the various credit instruments. In our subsequent analysis, we estimate the impact of news releases on the term and quality spread measures. We document several common sources of macroeconomic news that have an influence on the entire term structure horizon. Specifically, unexpected increases in consumer credit, durable goods orders, CPI and the trade balance each serve to widen the 3-, 10-, and 30-year term spreads. We attribute this result to changes in the investors’ risk aversion and their demand for funds in the market for loanable funds. Furthermore, upon comparing the sensitivity of the term spread with the quality spread, we observe an asymmetric impact of inflationary expectations on term and quality spreads. Notably, while increases in inflationary expectations (as seen by the CPI announcement) widen the term spread, on the other hand, they have a negative impact on the quality spread. We attempt to reconcile this apparent inconsistency by drawing upon existing evidence on term structure and default risk behavior.

References Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Craki (Eds.), Proceedings of the 2nd International Symposium on Information Theory, Budapest, Akademiai Kiado. Almeida, A., Goodhart, C., Payne, R., 1998. The effects of macroeconomic news on high-frequency exchange rate behavior. Journal of Financial and Quantitative Analysis 33, 383–408. Balduzzi, P., Elton, E.J., Green, T.C., 2001. Economic news and bond prices: Evidence from the U.S. Treasury market. Journal of Financial and Quantitative Analysis 36, 523–544. Barr, D.G., Campbell, J.Y., 1997. Inflation, real interest rates, and the bond market: A study of U.K. nominal and index-linked government bond prices. Journal of Monetary Economics 39, 360–384. Bevan, A., Garzarelli, F., 2000. Corporate bond spreads and the business cycle: Introducing GS-SPREAD. Journal of Fixed Income 9, 8–18. Campbell, J.Y., Shiller, R.J., 1984. A simple account of the behavior of long-term interest rates. American Economic Review, Papers and Proceedings 74, 44–48.

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

101

Campbell, J.Y., Shiller, R.J., 1991. Yield spreads and interest rate movements: A bird’s eye view. Review of Economic Studies 58, 495–514. Cheung, Y.W., Lai, K.S., 1993. Finite-sample sizes of Johansen’s likelihood ratio tests for cointegration. Oxford Bulletin of Economics and Statistics 55, 313–328. Cook, T., & Hahn, T. (1991). The reaction of interest rates to the employment report: The role of policy anticipations. Federal Reserve Bank of Richmond Economic Review, September–October, 3–12. Edison, H. J. (1996). The reaction of exchange rates and interest rates to news releases. Board of Governors of the Federal Reserve System International Finance Discussion Papers, No. 570, October. Ewing, B.T., 2003. The response of the default risk premium to macroeconomic shocks. Quarterly Review of Economics and Finance 43, 261–272. Fama, E.F., 1984. The information in the term structure. Journal of Financial Economics 13, 509–528. Fama, E.F., 1990. Term-structure forecasts of interest rates, inflation, and real returns. Journal of Monetary Economics 25, 59–76. Fama, E.F., Bliss, R.R., 1987. The information in long-maturity forward rates. American Economic Review 77, 680–692. Fama, E.F., French, K.R., 1989. Business conditions and expected returns on stocks and bonds. Journal of Financial Economics 25, 23–49. Fleming, M.J., Remolona, E., 1997. What moves the bond market? Federal Reserve Bank of New York. Economic Policy Review 3, 31–50. Froot, K.A., 1989. New hope for the expectations hypothesis of the term structure of interest rates. Journal of Finance 44, 283–305. Gonzala, J., 1994. Comparison of five alternative methods of estimating long run equilibrium relationships. Journal of Econometrics 60, 203–233. Goodfriend, M., 1998. Using the term structure of interest rates for monetary policy. Federal Reserve Bank of Richmond Economic Quarterly 84, 13–30. Handa, J., Ma, B.K., 1989. Four test for the random walk hypothesis: Power versus robustness. Economics Letters 29, 141–145. Jaffee, D.M., 1975. Cyclical variations in the risk structure of interest rates. Journal of Monetary Economics 1, 309–325. Jarrow, R.A., Turnbull, S.M., 2000. The intersection of market and credit risk. Journal of Banking and Finance 24, 271–299. Johansen, S., 1991. Estimation and hypothesis testing for cointegration vectors in Gaussian vector autoregressive models. Econometrica 59, 1551–1580. Johansen, S., Juselius, K., 1990. Maximum likelihood estimation and inference on cointegration—With applications to the demand for money. Oxford Bulletin of Economics and Statistics 52, 169–210. Krueger, A. B. (1996). Do markets respond more to more reliable labor market data? A test of market rationality. Unpublished paper, Princeton University, September. Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y., 1992. Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics 54, 159–178. Lee, D., Schmidt, P., 1996. On the power of the KPSS test of stationarity against fractionally-integrated alternatives. Journal of Econometrics 73, 285–302. Mankiw, N.G., 1986. The term structure of interest rates revisited. Brookings Papers on Economic Activity 11, 61–96. McQueen, G., Roley, V.V., 1993. Stock prices, news, and business conditions. Review of Financial Studies 6, 683–707. Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29, 449–470. Mishkin, F.S., 1990. What does the term structure tell us about future inflation? Journal of Monetary Economics 25, 77–95. Neal, R., Rolph, D., & Morris, C. (2000). Interest rates and credit spread dynamics. Working Paper, Indiana University, December. Pearce, D.K., Roley, V.V., 1985. Stock prices and economic news. Journal of Business 58, 49–67. Phillips, P., 1991. Optimal inference in cointegrated systems. Econometrica 59, 283–306.

102

S. Ramchander et al. / The Quarterly Review of Economics and Finance 45 (2005) 84–102

Perron, P., 1988. Testing for a unit root in time series regression. Biometrica 75, 335–346. Schwert, G.W., 1989. Tests for unit roots: A Monte Carlo investigation. Journal of Business and Economic Statistics 7, 147–160. Shiller, R. J. (1987). The term structure of interest rates. In B. Friedman & F. H. Hahn (Eds.), Handbook of monetary economics. Amsterdam: North-Holland. Shiller, R.J., Campbell, J.Y., Schoenholtz, K.L., 1983. Forward rates and future policy: Interpreting the term structure of interest rates. Brookings Papers on Economic Activity 1, 173–217. Smirlock, M., 1986. Inflation announcements and financial market reaction: Evidence from the long-term bond market. Review of Economics and Statistics 68, 329–333. Urich, T., Wachtel, P., 1981. Market response to the weekly money supply announcements in the 1970s. Journal of Finance 36, 1063–1072.