Dependence of Structural Breaks in Rating Transition Dynamics on Economic and Market Variations
Haipeng Xing () and
Ying Chen ()
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Haipeng Xing: Department of Applied Mathematics and Statistics, SUNY at Stony Brook, Stony Brook, NY 11794, U.S.A.
Ying Chen: Everspring Capital LLC., 350 Park Avenue, New York, NY 10022, U.S.A.
Review of Economics & Finance, 2018, vol. 11, 1-18
The financial crisis of 2007-2008 has caused severe economic and political consequences over the world. An interesting question from this crisis is whether or to what extent such sharp changes or structural breaks in the market can be explained by economic and market fundamentals. To address this issue, we consider a model that extracts the information of market structural breaks from firms¡¯ credit rating records, and connects probabilities of market structural breaks to observed and latent economic variables. We also discuss the issue of selecting significant variables when the number of economic covariates is large. We then analyze market structural breaks that involve U.S. firms¡¯ credit rating records and historical data of economic and market fundamentals from 1986 to 2015. We find that the probabilities of structural breaks are positively correlated with changes of S&P500 returns and volatilities and changes of inflation, and negatively correlated with changes of corporate bond yield. The significance of other variables depends on the inclusion of latent variables in the study or not.
Keywords: Credit rating; Markov chain Monte Carlos; Stochastic approximation; Structural break; Variable selection (search for similar items in EconPapers)
JEL-codes: C13 C41 G12 G20 (search for similar items in EconPapers)
Note: The research of the first author is supported by the U.S. National Science Foundation DMS- 1206321 and DMS-1612501. We thank two anonymous referees for their comments to improve the paper. We also thank our research assistant, Danqing Li, for helping us code the algorithm in Section 4.1.
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