 Do measures of systemic risk predict U.S. corporate bond default rates?Angelos Kanas and Philip Molyneux () International Review of Financial Analysis, 2020, vol. 71, issue C Abstract: Using univariate and multivariate Mixed Data Sampling (MIDAS) and LASSO estimation methodologies, we explore whether the U.S. annual average corporate bond default rate can be predicted by 12 monthly systemic risk measures proposed in the literature. We find that nearly all of the systemic risk indicators have predictive power for the default rate. Granger causality tests based on multivariate mixed frequency VAR models further support this conclusion. On the basis of MIDAS models, we illustrate that five of these indicators are able to forecast out-of-sample the 2009 corporate default crisis. Using a LASSO multivariate model, it is further shown that the systemic risk indicators can forecast out-of-sample both the 2009 default rate and the default rates during the buildup before the crisis and in the aftermath of the crisis. Institution-specific and volatility systemic risk measures are the most relevant for modeling U.S. corporate bond default rates, with the Conditional VaR measure of Adrian and Brunnermeier (2016) exhibiting the best performance. Keywords: Systemic risk; Corporate bond defaults; Mixed data sampling; LASSO; Predictive ability (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:71:y:2020:i:c:s1057521920301976 DOI: 10.1016/j.irfa.2020.101553 Access Statistics for this article International Review of Financial Analysis is currently edited by B.M. Lucey More articles in International Review of Financial Analysis from Elsevier |
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