Asymmetric determinants of corporate bond credit spreads in China: Evidence from a nonlinear ARDL model |
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| Affiliation: | 1. Department of Finance, Ocean University of China, Qingdao, Shandong, China;2. Department of Finance, Qingdao University, Qingdao, Shandong, China;1. Shenzhen University, Shenzhen International Business School, Shenzhen, China;2. San Francisco State University, College of Business – Department of Finance, 1600 Holloway Drive, San Francisco, CA 94132, United States;3. California State University, Chico, College of Business – Department of Finance and Marketing, 400 W First St, Chico, CA 95929, United States;4. San Francisco State University, College of Business – Department of Finance, 1600 Holloway Drive, San Francisco, CA 94132, United States;1. Department of Finance, Business School, Université de Sherbrooke, 2500 Boul. De l’Université, Sherbrooke (QC) J1K 2R1, Canada;2. Department of Accounting, Business Faculty, Université de Moncton, 18 Antonine-Maillet Ave, Moncton (NB) E1A 3A9, Canada;1. Department of Finance, Faculty of Business and Law, Deakin University, 221 Burwood Highway, Burwood, Vic − 3168, Australia;2. School of Business Administration, American University of Sharjah, P.O. Box 26666, Sharjah, United Arab Emirates;3. Blix, 283 Normanby Road, Port Melbourne, Vic − 3207, Australia;1. School of Economics and Management, University of Science and Technology Beijing, China;2. College of Business, University of Nevada, Reno, United States;3. Lindner College of Business, University of Cincinnati, United States;1. University of Sousse, Sousse, Tunisia;2. LEO (CNRS UMR 7322), University of Orléans, Orléans, France;3. IPAG Business School, Paris, France |
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| Abstract: | This study utilizes the nonlinear ARDL (NARDL) model proposed by Shin, Yu, and Greenwood-Nimmo (2014) to quantify the potentially asymmetric transmission of positive and negative changes in each of the possible determinants of industry-level corporate bond credit spreads in China. The determinants we consider include the corresponding industry stock price, China’s stock market volatility, the level and slope of the yield curve (i.e., the interest rate), the industrial production growth rate, and the inflation rate. The empirical results suggest substantial asymmetric effects of these determinants on credit spreads, with the positive changes in the determinants showing larger impacts than the negative changes for most industries we consider. Moreover, the corresponding industry stock prices, the interest rate, and the industrial production growth rate negatively drive the industry credit spreads for many industries. In turn, China’s stock market volatility and the inflation rate positively affect the credit spreads at each industry level. These findings may be helpful to investors, bond issuers and policymakers in understanding the dynamics of credit risks and corporate bond rates at the industry level. |
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| Keywords: | Corporate bond Credit spreads Credit risk Asymmetry NARDL model |
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