Macroeconomic and Financial Determinants of the Volatility of Corporate Bond Returns.

  1. Nieto Domenech, Belén
  2. Novales Cinca, Alfonso
  3. Rubio Irigoyen, Gonzalo
Revista:
Documentos de Trabajo (ICAE)

ISSN: 2341-2356

Año de publicación: 2014

Número: 25

Páginas: 1-54

Tipo: Documento de Trabajo

DIALNET GOOGLE SCHOLAR lock_openDocta Complutense editor

Otras publicaciones en: Documentos de Trabajo (ICAE)

Resumen

This paper analyzes the relationship between the volatility of corporate bond returns and standard financial and macroeconomic indicators reflecting the state of the economy. We employ the GARCHMIDAS multiplicative two-component model of volatility that distinguishes the short-term dynamics from the long-run component of volatility. Both the in-sample and out-of-sample analysis show that recognizing the existence of a stochastic low-frequency component captured by macroeconomic and financial indicators may improve the fit of the model to actual bond return data, relative to the constant long-run component embedded in a typical GARCH model.

Referencias bibliográficas

  • Acharya, V., Y. Amihud, and S. Bharath, 2013. Liquidity risk of corporate bond returns: Forthcoming in the Journal of Financial Economics.
  • Adrian, T., and J. Rosenberg, 2008. Stock returns and volatility: Pricing the short-run and long-run components of market risk. Journal of Finance 63, 2997-3030.
  • Bansal, R. and A. Yaron, 2004. Risks for the long run: A potential resolution of asset pricing puzzles. Journal of Finance 59, 1481-1509.
  • Bao, J., J. Pang, and J. Wang, 2011. The illiquidity of corporate bonds. Journal of Finance 66, 911-946.
  • Bollerslev, T., 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307-326.
  • Cai, N., and X. Jiang, 2008. Corporate bond returns and volatility. The Financial Review 43, 1-26.
  • Campbell, J., and J. Cochrane, 1999. By force of habit: A consumption-based explanation of aggregate stock market behaviour. Journal of Political Economy 107, 205-251.
  • Chen, H., 2010. Macroeconomic conditions and the puzzles of credit spreads and capital structure. Journal of Finance 65, 2171-2212.
  • Chen, L., P. Collin-Dufresne, and R. Goldstein, 2009. On the relation between the credit spread puzzle and the equity premium puzzle. Review of Financial Studies 22, 3367-3409.
  • Chernov, M., R. Gallant, E. Ghysels, and G. Tauchen, 2003. Alternative models for stock price dynamics. Journal of Econometrics 116, 225-257.
  • Clark, T., and M. McCraken, 2012. Reality checks and Nested Forecast Model Comparison. Journal of Business and Economic Statistics 30, 53-66.
  • Engle, R., 1982. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica 50, 987-1008.
  • Engle, R., and G. Lee, 1999. A permanent and transitory component model of stock return volatility. R. Engle and H. White (editors), Cointegration, causality, and forecasting: A festschrift in honour of Clive W. J. Granger, Oxford University Press 475-497.
  • Engle, R., and J. Rangel, 2008. The spline GARCH model for low frequency volatility and its global macroeconomic causes. Review of Financial Studies 21, 1187-1222.
  • Engle, R., E. Ghysels, and B. Sohn, 2013. Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics 95, 776-797.
  • Fama, E., and K. French, 1993. Common risk factors in the returns of stocks and bonds. Journal Financial Economics 33, 3-56.
  • Gebhardt, W., R. Hvidkjaer, and S. Swaminathan 2005. The cross-section of expected corporate bond returns: Betas or characteristics? Journal of Financial Economics 75, 84-114.
  • Ghysels, E., A. Sinko, and R. Valkanov, 2006. MIDAS regressions: Further results and new directions. Econometric Reviews 26, 53-90.
  • Ghysels, E., P. Santa-Clara, and R. Valkanov, 2003. The MIDAS touch. Mixed data sampling regression models. Working Paper, University of California, Los Angeles, and University of North Carolina at Chapel Hill.
  • Ghysels, E., P. Santa-Clara, and R. Valkanov, 2005. There is a risk-return trade-off after all. Journal of Financial Economics 76, 509–548.
  • Ghysels, E., P. Santa-Clara, and R. Valkanov, 2006. Predicting volatility: Getting the most out of return data sampled at different frequencies. Journal of Econometrics 131, 59–95.
  • González, M., J. Nave, and G. Rubio, 2012. The cross-section of expected returns with MIDAS betas. Journal of Financial and Quantitative Analysis 47, 115-135.
  • Huang, J. and M. Huang, 2003. How much of the corporate-treasury yield spread is due to credit risk? Working Paper, Stanford University.
  • Lin, H., J. Wang, and C. Wu, 2011. Liquidity risk and expected corporate bond returns. Journal of Financial Economics 99, 628-650.
  • Malloy, C., T. Moskowitz, and A. Vissing-Jorgensen, 2011. Long-run stockholder consumption risk and asset returns. Journal of Finance 64, 2427-2479.
  • McCracken, M. (2007) Asymptotics for out-of-sample tests of Granger causality, Journal of Econometrics 140, 719–752.
  • Pastor, L. and R. Stambaugh, 2003. Liquidity risk and expected stock returns. Journal of Political Economy 111, 642-685.
  • Rachwalski, M., 2011. Corporate bonds, aggregate wealth, and stock market risk. Working Paper, Emory University.
  • Wang, F., and E. Ghysels, 2011. Econometric analysis for volatility component models. Working Paper, University of Illinois at Chicago, and University of North Carolina at Chapel Hill.
Fuente de los datos: Dialnet