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Are corporate bond market returns predictable?

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  • Hong, Yongmiao
  • Lin, Hai
  • Wu, Chunchi

Abstract

This paper examines the predictability of corporate bond returns using the transaction-based index data for the period from October 1, 2002 to December 31, 2010. We find evidence of significant serial and cross-serial dependence in daily investment-grade and high-yield bond returns. The serial dependence exhibits a complex nonlinear structure. Both investment-grade and high-yield bond returns can be predicted by past stock market returns in-sample and out-of-sample, and the predictive relation is much stronger between stocks and high-yield bonds. By contrast, there is little evidence that stock returns can be predicted by past bond returns. These findings are robust to various model specifications and test methods, and provide important implications for modeling the term structure of defaultable bonds.

Suggested Citation

  • Hong, Yongmiao & Lin, Hai & Wu, Chunchi, 2012. "Are corporate bond market returns predictable?," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2216-2232.
    • Handle: RePEc:eee:jbfina:v:36:y:2012:i:8:p:2216-2232
    DOI: 10.1016/j.jbankfin.2012.04.001
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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Fan, Longzhen & Tian, Shu & Zhang, Chu, 2012. "Why are excess returns on China’s Treasury bonds so predictable? The role of the monetary system," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 239-248.
    3. Sekkel, Rodrigo, 2011. "International evidence on bond risk premia," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 174-181, January.
    4. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    5. Yongmiao Hong & Yoon-Jin Lee, 2005. "Generalized Spectral Tests for Conditional Mean Models in Time Series with Conditional Heteroscedasticity of Unknown Form," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(2), pages 499-541.
    6. Hong, Yongmiao, 1996. "Consistent Testing for Serial Correlation of Unknown Form," Econometrica, Econometric Society, vol. 64(4), pages 837-864, July.
    7. Manuel Dominguez & Ignacio Lobato, 2003. "Testing the Martingale Difference Hypothesis," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 351-377.
    8. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    9. Keim, Donald B. & Stambaugh, Robert F., 1986. "Predicting returns in the stock and bond markets," Journal of Financial Economics, Elsevier, vol. 17(2), pages 357-390, December.
    10. Gebhardt, William R. & Hvidkjaer, Soeren & Swaminathan, Bhaskaran, 2005. "Stock and bond market interaction: Does momentum spill over?," Journal of Financial Economics, Elsevier, vol. 75(3), pages 651-690, March.
    11. Friewald, Nils & Jankowitsch, Rainer & Subrahmanyam, Marti G., 2012. "Illiquidity or credit deterioration: A study of liquidity in the US corporate bond market during financial crises," Journal of Financial Economics, Elsevier, vol. 105(1), pages 18-36.
    12. Downing, Chris & Underwood, Shane & Xing, Yuhang, 2009. "The Relative Informational Efficiency of Stocks and Bonds: An Intraday Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(5), pages 1081-1102, October.
    13. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    14. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    15. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    16. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    17. Kwan, Simon H., 1996. "Firm-specific information and the correlation between individual stocks and bonds," Journal of Financial Economics, Elsevier, vol. 40(1), pages 63-80, January.
    18. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    19. Hamao, Yasushi & Masulis, Ronald W & Ng, Victor, 1990. "Correlations in Price Changes and Volatility across International Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 281-307.
    20. Kessler, Stephan & Scherer, Bernd, 2009. "Varying risk premia in international bond markets," Journal of Banking & Finance, Elsevier, vol. 33(8), pages 1361-1375, August.
    21. Abhyankar, Abhay & Gonzalez, Angelica, 2009. "News and the cross-section of expected corporate bond returns," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 996-1004, June.
    22. Chen, XiaoHua & Maringer, Dietmar, 2011. "Detecting time-variation in corporate bond index returns: A smooth transition regression model," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 95-103, January.
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    More about this item

    Keywords

    Return predictability; Generalized spectrum; Autocorrelation; Causality; Nonlinearity; Bond pricing; Market efficiency;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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