Do measures of systemic risk predict U.S. corporate bond default rates? |
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| Institution: | 1. Department of Economics, Dalhousie University, PO Box 15000, Halifax, NS B3H 4R2, Canada;2. Department of Economics, Sobey School of Business, Saint Mary''s University, 923 Robie Street, Halifax, NS B3H 3C3, Canada;3. School of Business, Renmin University of China, 59 Zhongguancun St., Beijing 100872, China;4. Peter B. Gustavson School of Business, University of Victoria, PO Box 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada;1. Itarle AG, Switzerland;2. School of Management, University of Bath, UK;3. Essex Business School, University of Essex, UK;1. Excelia Group, Excelia Business School, CERIIM, 102 Rue de Coureilles, 17024 La Rochelle, France;2. University Paris-Saclay, UMI SOURCE, UVSQ, IRD, France and Paris School of Business, PSB, 59 Rue Nationale, 75013 Paris, France;3. Rabat Business School, International University of Rabat, Rabat 11103, Morocco;4. Audencia Business School (AACSB, EQUIS, AMBA), France |
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| 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. |
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