Financial predictors of real activity and the financial accelerator B

Transcription

1 Economics Letters 82 (2004) Financial predictors of real activity and the financial accelerator B Ashoka Mody a,1, Mark P. Taylor b,c, * a Research Department, International Monetary Fund, USA b Department of Economics, University of Warwick, Coventry CV4 7AL, UK c Centre for Economic Policy Research, UK Received 7 August 2002; accepted 26 November 2002 Abstract We document the breakdown in the term spread as a predictor of activity during the 1990s and provide the first long-horizon regression evidence that the high yield spread predicts well. The results support the presence of a US financial accelerator. D 2003 Elsevier B.V. All rights reserved. Keywords: High yield spread; Financial accelerator JEL classification: E44; E33 1. Introduction The slope of the nominal yield curve, or the term spread, was shown during the late 1980s and early 1990s to have significant predictive content for future real economic activity, both in the US and in Europe. 2 There is, however, mixed evidence concerning the strength of this relationship for more recent periods (Dotsey, 1998). On the other hand, Gertler and Lown (1999), drawing on the B The research reported in this letter was undertaken while Mark Taylor was a Visiting Scholar at the International Monetary Fund. The views expressed here are those of the authors and should not be attributed to the International Monetary Fund or to any of its member countries. * Corresponding author. Department of Economics, University of Warwick, Coventry CV4 7AL, UK. Tel.: ; fax: addresses: [email protected] (A. Mody), [email protected] (M.P. Taylor). 1 Tel.: ; fax: Dotsey (1998) provides an overview of this literature /$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi: /j.econlet

2 168 A. Mody, M.P. Taylor / Economics Letters 82 (2004) theory of the financial accelerator (see, for example, Bernanke et al., 1999, and the references cited therein) argue that an alternative financial variable the premium required on high yield or junk bonds over that required on government debt or AAA-rated corporate bonds should also have predictive power for real economic activity. 3 In this letter, we provide some new evidence on these issues, including the first long-horizon regression evidence for the high yield spread. 2. The high yield spread and the financial accelerator The central features of the theory of the financial accelerator are reasonably uniform across specific models. There is some friction present in the financial market, such as asymmetric information or costs of contract enforcement, which, for a wide class of industrial and commercial businesses, introduces a wedge between the cost of external funds and the opportunity cost of internal funds the premium for external funds. This premium is an endogenous variable which depends inversely on the balance sheet strength of the borrower, since the balance sheet is the key signal through which the creditworthiness of the firm is evaluated. However, balance sheet strength is itself a positive function of aggregate real economic activity, so that borrowers financial positions are procyclical and hence movements in the premium for external funds are countercyclical. Hence, as real activity expands, the premium on external funds declines which in turn leads to an amplification of borrower spending which further accelerates the expansion of real activity. This is the basic mechanism of the financial accelerator. The development of the US market for below investment grade debt since the mid-1980s provides scope for testing this theory, since firms raising funds in that market are likely to be precisely those that face the type of market frictions that the theory describes. Thus, the spread between high yield bonds and government debt or the highest rated commercial debt is likely to be a good indicator of the premium on external finance. If the theory of the financial accelerator has some empirical content, therefore, one would expect the high yield spread to be a countercyclical predictor of real activity. 3. Data Quarterly data for the US for the period 1975Q1 2001Q4 were obtained from the International Monetary Fund s International Financial Statistics database, on real GDP, the 3-month treasury bill rate and the 10-year government bond yield. A quarterly series on an index (in annualized yield terms) of the yield on below investment grade bonds (rated BBB3 or lower), publicly issued in the US with a year or more to maturity, was obtained from the Merrill Lynch Global Bond Indices database. Since the market for below investment grade debt only developed during the 1980s, this series was only available for the period 1988Q1 2001Q4. 3 Gertler and Lown (1999) provide some empirical support for this view based on correlation and impulse-response analysis.

3 4. Long-horizon regressions A. Mody, M.P. Taylor / Economics Letters 82 (2004) The dependent variable in the basic regressions is the annualized cumulative percentage change in real GDP: j k y tþk u 400 k ðy tþk y t Þ ð1þ where k denotes the forecasting horizon in quarters and y t is the logarithm of the level of real GDP. The k-period change in the logarithm of GDP, is multiplied by (400/k) to ensure that the percentage growth rate is expressed in annualized terms. The slope of the nominal yield curve is measured by the difference between the yield on 10-year US government bonds (R t ) and the 3-month US Treasury bill rate (r t ), while the high yield spread is measured as the difference between the Merrill Lynch high yield bond index ( Q t ) and the 10-year government bond yield. The basic regression equations are therefore of the form: j k y tþk ¼ a k þ b k ðr t r t Þþg tþk ; ð2þ for the term spread regressions, and j k y tþk ¼ c k þ d k ðq t R t Þþe tþk ; ð3þ for the high yield spread regressions, where g t + k and e t + k are the forecast errors. The possibility of moving average errors in overlapping forecast errors was allowed for by using a method-of-moments correction to the estimated covariance matrix Term spread regressions The results of estimating Eq. (2) for forecast horizons up to 20 quarters are given in Table 1 for the sample period 1975Q1 1987Q4. They are in line with previous empirical evidence in that the estimated slope coefficient is significantly different from zero at the 5% level or better at every horizon up to 16 quarters, and the R 2 statistics are impressive, with as much as 60% of the variation in the cumulative growth in GDP explained. On the other hand, the estimation results for the more recent period, 1988Q1 2001Q4, shown in Table 2, reveal the apparently complete breakdown in this relationship: the estimated slope coefficients are in every case insignificantly different from zero at the 5% level and the R 2 statistics are, in every case, extremely low High yield spread regressions The estimates of the long-horizon regressions, Eq. (3), are given in Table 3. The predictive content of the high yield spread is quite striking. In every case except the longest horizon of 5 years, the estimated

4 170 A. Mody, M.P. Taylor / Economics Letters 82 (2004) Table 1 The term spread as a predictor of real activity, 1975Q1 1987Q4 Forecast horizon k b k R 2 S.E. (%) (2.966) (4.845) (5.968) (6.471) (6.436) (6.579) (6.801) (7.562) (9.326) (3.992) (2.424) (1.680) (1.912) Estimation is by ordinary least squares, with a method-of-moments correction to the estimated covariance matrix. k is the forecast horizon in quarters, R 2 denotes the coefficient in determination and S.E. denotes the standard error of the regression. Figures in parentheses below coefficient estimates are asymptotic t-ratios. An intercept term was also included in the regressions. slope coefficient is strongly significantly different from zero at the 5% level or lower. In terms of goodness-of-fit, the high yield spread seems to have strongest predictive content at around three to four quarters ahead, although in terms of the significance of the coefficients, it seems to predict well from horizons as short as one quarter to horizons as long as 18 quarters. Table 2 The term spread as a predictor of real activity, 1988Q1 2001Q4 Forecast horizon k b k R 2 S.E. (%) (0.655) (1.118) (1.154) (1.007) (0.824) (0.744) (0.649) (0.614) (0.610) (0.732) (1.140) (1.326) (1.335) Estimation is by ordinary least squares, with a method-of-moments correction to the estimated covariance matrix. k is the forecast horizon in quarters, R 2 denotes the coefficient in determination and S.E. denotes the standard error of the regression. Figures in parentheses below coefficient estimates are asymptotic t-ratios. An intercept term was also included in the regressions.

5 A. Mody, M.P. Taylor / Economics Letters 82 (2004) Table 3 The high yield spread as a predictor of real activity, 1988Q1 2001Q4 Forecast horizon k d k R 2 S.E. (%) ( 4.696) ( 5.297) ( 4.545) ( 3.495) ( 3.044) ( 3.227) ( 3.464) ( 3.496) ( 3.264) ( 6.089) ( 3.568) ( 2.273) ( 1.319) Estimation is by ordinary least squares, with a method-of-moments correction to the estimated covariance matrix. k is the forecast horizon in quarters, R 2 denotes the coefficient in determination and S.E. denotes the standard error of the regression. Figures in parentheses below coefficient estimates are asymptotic t-ratios. An intercept term was also included in the regressions. The negative sign of the estimated coefficients, as well as their significance, is in line with the predictions of the theory of the financial accelerator, in that the high yield spread should interact countercyclically with real activity according to the theory. 5. Conclusion In this letter, we have documented the breakdown in the nominal term spread as a predictor of economic activity during the 1990s. On the other hand, we have also provided the first long-horizon regression evidence that the high yield spread does act as a significant predictor of economic activity over the 1990s. The breakdown in the nominal term spread as a predictor of real activity after 1987 may perhaps be related to the sharp decline in the level and volatility of inflation during the 1990s or to a shift in US monetary policy behavior (see, e.g., Clarida, 2001). However, the lack of a firm theoretical foundation for this relationship has always been somewhat problematic. As Plosser and Rouwenhorst (1994, p. 138) note, there is no a priori reason why one should expect the slope of the term structure of interest rates to predict future real activity particularly well. On the other hand, there is an a priori reason why one should expect the high yield spread to predict future real activity. Viewed as a proxy for the premium on external financing, the theory of the financial accelerator implies that it should do exactly that, and that it should, moreover be countercyclically related to real activity. Our results therefore provide further empirical evidence for the presence of a US financial accelerator.

6 172 A. Mody, M.P. Taylor / Economics Letters 82 (2004) References Bernanke, B.S., Gertler, M., Gilchrist, S., The financial accelerator in a quantitative business cycle framework. In: Taylor, J.B., Woodford, M. (Eds.), Handbook of Macroeconomics: Amsterdam, New York and Oxford: Elsevier Science, North-Holland. Clarida, R.H., The empirics of monetary policy rules in open economies. International Journal of Finance and Economics 6, Dotsey, M., The predictive content of the interest rate term spread for future economic growth. Economic Quarterly 84, (Federal Reserve Bank of Richmond). Gertler, M., Lown, C.S., The information in the high-yield bond spread for the business cycle: evidence and some implications. Oxford Review of Economic Policy 15, Plosser, C.I., Rouwenhurst, K.G., International term structures and real economic growth. Journal of Monetary Economics 33,