Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-06-24T14:12:44.409Z Has data issue: false hasContentIssue false
  • English
  • Français

Detecting Regime Shifts in Credit Spreads

Published online by Cambridge University Press:  08 April 2015

Olfa Maalaoui Chun ,
Georges Dionne  and
Pascal François
Olfa Maalaoui Chun
Affiliation:
olfa.maalaoui@gmail.com, Graduate School of Finance, KAIST, 87 Hoegiro, Dongdamoongu, Seoul 130-722, South Korea
Georges Dionne
Affiliation:
georges.dionne@hec.ca, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 2A7, Canada.
Pascal François
Affiliation:
francois@hec.ca, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, Québec H3T 2A7, Canada.
Get access Rights & Permissions [Opens in a new window]

Abstract

Using an innovative random regime shift detection methodology, we identify and confirm two distinct regime types in the dynamics of credit spreads: a level regime and a volatility regime. The level regime is long lived and shown to be linked to Federal Reserve policy and credit market conditions, whereas the volatility regime is short lived and, apart from recessionary periods, detected during major financial crises. Our methodology provides an independent way of supporting structural equilibrium models and points toward monetary and credit supply effects to account for the persistence of credit spreads and their predictive power over the business cycle.

Type
Research Articles
Information
Journal of Financial and Quantitative Analysis , Volume 49 , Issue 5-6 , December 2014 , pp. 1339 - 1364
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alexander, C., and Kaeck, A.. “Regime Dependent Determinants of Credit Default Swap Spreads.” Journal of Banking and Finance, 32 (2008), 1008–1021.Google Scholar
Bai, J. “Common Breaks in Means and Variances for Panel Data.” Journal of Econometrics, 157 (2010), 78–92.CrossRefGoogle Scholar
Bai, J., and Perron, P.. “Estimating and Testing Linear Models with Multiple Structural Changes.” Econometrica, 66 (1998), 47–78.Google Scholar
Bai, J., and Perron, P.. “Computation and Analysis of Multiple Structural Change Models.” Journal of Applied Econometrics, 18 (2003), 1–22.Google Scholar
Bernanke, B., and Gertler, M.. “Agency Costs, Net Worth, and Business Fluctuations.” American Economic Review, 79 (1989), 14–31.Google Scholar
Bhamra, H. S.; Fisher, A. J.; and Kuehn, L. A.. “Monetary Policy and Corporate Default.” Journal of Monetary Economics, 58 (2011), 480–494.Google Scholar
Bhamra, H. S.; Kuehn, L. A.; and Strebulaev, I. A.. “The Levered Equity Risk Premium and Credit Spreads: A Unified Framework.” Review of Financial Studies, 23 (2010), 645–703.Google Scholar
Buraschi, A.; Trojani, F.; and Vedolin, A.. “Economic Uncertainty, Disagreement, and Credit Markets.” Working Paper, London School of Economics (2011).CrossRefGoogle Scholar
Campbell, J. Y., and Cochrane, J. H.. “By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior.” Journal of Political Economy, 107 (1999), 205–251.Google Scholar
Chen, G.; Choi, Y.; and Zhou, Y.. “Nonparametric Estimation of Structural Change Points in Volatility Models for Time Series.” Journal of Econometrics, 126 (2005), 79–114.Google Scholar
Chen, H. “Macroeconomic Conditions and the Puzzles of Credit Spreads and Capital Structure.” Journal of Finance, 65 (2010), 2171–2212.Google Scholar
Chen, L.; Collin-Dufresne, P.; and Goldstein, R. S.. “On the Relation Between the Credit Spread Puzzle and the Equity Premium Puzzle.” Review of Financial Studies, 22 (2009), 3367–3409.Google Scholar
Chen, N. F. “Financial Investment Opportunities and the Macroeconomy.” Journal of Finance, 46 (1991), 529–554.Google Scholar
Chib, S. “Estimation and Comparison of Multiple Change Point Models.” Journal of Econometrics, 86 (1998), 221–241.Google Scholar
Collin-Dufresne, P.; Goldstein, R. S.; and Martin, J. S.. “The Determinants of Credit Spread Changes.” Journal of Finance, 56 (2001), 2177–2208.Google Scholar
David, A. “Inflation Uncertainty, Asset Valuations, and the Credit Spread Puzzle.” Review of Financial Studies, 21 (2008), 2487–2534.Google Scholar
Davis, R.; Lee, T.; and Rodriguez-Yam, G.. “Break Detection for a Class of Nonlinear Time-Series Models.” Journal of Time Series Analysis, 29 (2008), 834–867.CrossRefGoogle Scholar
Dick-Nielsen, J. “Liquidity Biases in TRACE.” Journal of Fixed Income, 19 (2009), 43–55.Google Scholar
Elton, E. J.; Gruber, M. J.; Agrawal, D.; and Mann, C.. “Explaining the Rate Spread on Corporate Bonds.” Journal of Finance, 56 (2001), 247–277.Google Scholar
Fama, E. F., and French, K.. “Business Conditions and Expected Returns on Stocks and Bonds.” Journal of Financial Economics, 25 (1989), 23–49.Google Scholar
Giesecke, K.; Longstaff, F. A.; Schaefer, S.; and Strebulaev, I.. “Corporate Bond Default Risk: A 150-Year Perspective.” Journal of Financial Economics, 102 (2011), 233–250.Google Scholar
Gilchrist, S., and Zakrajšek, E.. “Credit Spreads and Business Cycle Fluctuations.” American Economic Review, 102 (2012), 1692–1720.Google Scholar
Giordani, P., and Kohn, R.. “Efficient Bayesian Inference for Multiple Change Point and Mixture Innovation Models.” Journal of Business and Economic Statistics, 26 (2008), 66–77.Google Scholar
Gomes, J. F., and Schmid, L.. “Equilibrium Credit Spreads and the Macroeconomy.” Working Paper, Duke University (2010).Google Scholar
Gordon, L., and Pollak, M.. “An Efficient Sequential Nonparametric Scheme for Detecting a Change of Distribution.” Annals of Statistics, 22 (1994), 763–804.Google Scholar
Goyal, A., and Welch, I.. “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction.” Review of Financial Studies, 21 (2008), 1455–1508.Google Scholar
Hackbarth, D.; Miao, J.; and Morellec, E.. “Capital Structure, Credit Risk, and Macroeconomic Conditions.” Journal of Financial Economics, 82 (2006), 519–550.CrossRefGoogle Scholar
Huang, J. Z., and Huang, M.. “How Much of the Corporate-Treasury Yield Spread Is Due to Credit Risk?” Review of Asset Pricing Studies, 2 (2012), 153–202.Google Scholar
Hull, J.; Predescu, M.; and White, A.. “The Relationship Between Credit Default Swap Spreads, Bond Yields, and Credit Rating Announcements.” Journal of Banking and Finance, 28 (2004), 2789–2811.Google Scholar
Jagannathan, R., and Wang, Z.. “The Conditional CAPM and the Cross-Section of Expected Returns.” Journal of Finance, 51 (1996), 3–54.Google Scholar
Kendall, M. G. “Note on Bias in the Estimation of Autocorrelation.” Biometrika, 41 (1954), 403–404.CrossRefGoogle Scholar
King, M. “Debt Deflation: Theory and Evidence.” European Economic Review, 38 (1994), 419–445.CrossRefGoogle Scholar
Kiyotaki, N., and Moore, J.. “Credit Cycles.” Journal of Political Economy, 105 (1997), 211–248.Google Scholar
Koopman, S. J.; Kraeussl, R.; Lucas, A.; and Monteiro, A. A.. “Credit Cycles and Macro Fundamentals.” Journal of Empirical Finance, 16 (2009), 42–54.Google Scholar
Koopman, S. J., and Lucas, A.. “Business and Default Cycles for Credit Risk.” Journal of Applied Econometrics, 20 (2005), 311–323.CrossRefGoogle Scholar
Lown, C., and Morgan, D.. “The Credit Cycle and the Business Cycle: New Findings Using the Loan Officer Opinion Survey.” Journal of Money, Credit and Banking, 38 (2006), 1575–1597.CrossRefGoogle Scholar
Lu, Y. K., and Perron, P.. “Modeling and Forecasting Stock Return Volatility Using a Random Level Shift Model.” Journal of Empirical Finance, 17 (2010), 138–156.Google Scholar
Ludvigson, S. C., and Ng, S.. “Macro Factors in Bond Risk Premia.” Review of Financial Studies, 22 (2009), 5027–5067.Google Scholar
Maheu, J. M., and McCurdy, T. H.. “How Useful Are Historical Data for Forecasting the Long-Run Equity Return Distribution?” Journal of Business and Economic Statistics, 27 (2009), 95–112.Google Scholar
Marriott, F. H. C., and Pope, J. A.. “Bias in the Estimation of Autocorrelations.” Biometrika, 41 (1954), 390–402.Google Scholar
Mueller, P. “Credit Spreads and Real Activity.” Working Paper, London School of Economics (2009).Google Scholar
Orcutt, G. H., and Winokur, H. S. Jr. “Autoregression: Inference, Estimation, and Prediction.” Econometrica, 37 (1969), 1–14.CrossRefGoogle Scholar
Perron, P., and Qu, Z.. “Estimating Restricted Structural Change Models.” Journal of Econometrics, 134 (2006), 373–399.Google Scholar
Pesaran, H.; Pettenuzzo, D.; and Timmermann, A.. “Forecasting Time Series Subject to Multiple Structural Breaks.” Review of Economic Studies, 73 (2006), 1057–1084.Google Scholar
Rodionov, S. N. “A Sequential Algorithm for Testing Climate Regime Shifts.” Geophysical Research Letters, 31 (2004), L09204.Google Scholar
Rodionov, S. N. “Detecting Regime Shifts in the Mean and Variance: Methods and Specific Examples.” Workshop on Regime Shifts, Varna, Bulgaria (2005), 68–72.Google Scholar
Rodionov, S. N. “Use of Prewhitening in Climate Regime Shift Detection.” Geophysical Research Letters, 33 (2006), L12707.CrossRefGoogle Scholar
Stine, R., and Shaman, P.. “A Fixed Point Characterization for Bias of Autoregressive Estimators.” Annals of Statistics, 17 (1989), 1275–1284.CrossRefGoogle Scholar
Stock, J. H., and Watson, M.. “New Indexes of Coincident and Leading Economic Indicators.” NBER Macroeconomics Annual, 4 (1989), 351–409.Google Scholar
Wu, L., and Zhang, F. X.. “A No-Arbitrage Analysis of Macroeconomic Determinants of the Credit Spread Term Structure.” Management Science, 54 (2005), 1160–1175.CrossRefGoogle Scholar
Supplementary material: PDF

Chun supplementary material

Internet Appendix A-K

Download Chun supplementary material(PDF)
PDF 2 MB