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Discovering the impact of systemic and idiosyncratic risk factors on credit spread of corporate bond within the framework of intelligent knowledge management

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  • Rongda Chen
  • Liu Yang
  • Weijin Wang
  • Ling Tang

Abstract

This paper exploits the implied information of data collected from credit spreads of Chinese corporate bonds and systemic and idiosyncratic risk factors. We compute contribution of risk factors to credit spreads of Chinese corporate bonds by establishing the unbalanced panel data model, identify the key factors impacting the size of credit spreads of corporate bonds. Knowledge extracted by data mining is helpful to investors for reasonable pricing of bonds and making rational investment decisions. When selecting variables, the unbalanced panel data model is used to calculate the Zero-volatility credit spreads, which are more accurate. We use term structure adjusted return of bond index as the systemic risk factor of corporate bond market, the three Fama/French systemic factors as the systemic risk factors of stock markets and idiosyncratic stock/bond volatility and idiosyncratic bond value-at-risk as the idiosyncratic risk factors. Empirical analysis of corporate bonds sampling China’s listing Corporation issued and traded on Shanghai Stock Exchange from 2008 to 2011 shows that the size of credit spreads is mainly determined by the systemic risk factors of bond market, i.e. risk factors of stock market make very little contribution to the spread; the idiosyncratic risk factors also contribute. An interesting phenomenon is that we find that the relationship between idiosyncratic stock volatility and credit spread is negative, which is contrary to extant research while the relationship is positive and mainly focuses on impact of risk factors on credit spread of corporate bond. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Rongda Chen & Liu Yang & Weijin Wang & Ling Tang, 2015. "Discovering the impact of systemic and idiosyncratic risk factors on credit spread of corporate bond within the framework of intelligent knowledge management," Annals of Operations Research, Springer, vol. 234(1), pages 3-15, November.
    • Handle: RePEc:spr:annopr:v:234:y:2015:i:1:p:3-15:10.1007/s10479-014-1727-y
    DOI: 10.1007/s10479-014-1727-y
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    Cited by:

    1. Mariya Gubareva & Maria Rosa Borges, 2018. "Rethinking economic capital management through the integrated derivative-based treatment of interest rate and credit risk," Annals of Operations Research, Springer, vol. 266(1), pages 71-100, July.
    2. Bhanu Pratap Singh Thakur & M. Kannadhasan & Vinay Goyal, 2018. "Determinants of corporate credit spread: evidence from India," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 45(1), pages 59-73, March.
    3. Yuanxin Liu & FengYun Li & Xinhua Yu & Jiahai Yuan & Dong Zhou, 2018. "Assessing the Credit Risk of Corporate Bonds Based on Factor Analysis and Logistic Regress Analysis Techniques: Evidence from New Energy Enterprises in China," Sustainability, MDPI, vol. 10(5), pages 1-21, May.
    4. Li, Xiao-Lin & Li, Xin & Si, Deng-Kui, 2020. "Asymmetric determinants of corporate bond credit spreads in China: Evidence from a nonlinear ARDL model," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    5. Rong-Xi Zhou & Ya-Hui Xiong & Tian-Hao Liu & Jing Li, 2019. "Macroeconomic Determinants of Credit Spreads: An Empirical Comparison between Chinese and American Corporate Bonds," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(5), pages 604-616, May.

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