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Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach

Author

Listed:
  • Jon Faust
  • Simon Gilchrist
  • Jonathan H. Wright
  • Egon Zakrajsek
Abstract
Employing a large number of real and financial indicators, we use Bayesian Model Averaging (BMA) to forecast real-time measures of economic activity. Importantly, the predictor set includes option-adjusted credit spread indexes based on bond portfolios sorted by maturity and credit risk as measured by the issuer's "distance-to-default." The portfolios are constructed directly from the secondary market prices of outstanding senior unsecured bonds issued by a large number of U.S. corporations. Our results indicate that relative to an autoregressive benchmark, BMA yields consistent improvements in the prediction of the growth rates of real GDP, business fixed investment, industrial production, and employment, as well as of the changes in the unemployment rate, at horizons from the current quarter (i.e., "nowcasting") out to four quarters hence. The gains in forecast accuracy are statistically significant and economically important and owe exclusively to the inclusion of our portfolio credit spreads in the set of predictors--BMA consistently assigns a high posterior weight to models that include these financial indicators.

Suggested Citation

  • Jon Faust & Simon Gilchrist & Jonathan H. Wright & Egon Zakrajsek, 2011. "Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach," NBER Working Papers 16725, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:16725
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    References listed on IDEAS

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    1. Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393, Elsevier.
    2. Cameron, A. Colin & Gelbach, Jonah B. & Miller, Douglas L., 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 238-249.
    3. Ben S. Bernanke, 1990. "On the predictive power of interest rates and interest rate spreads," New England Economic Review, Federal Reserve Bank of Boston, issue Nov, pages 51-68.
    4. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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