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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

Discovering the impact of systemic and idiosyncratic risk factors on credit spread of corporate... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Operations Research Springer Journals

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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References (26)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Economics / Management Science; Operations Research/Decision Theory; Combinatorics; Theory of Computation
ISSN
0254-5330
eISSN
1572-9338
DOI
10.1007/s10479-014-1727-y
Publisher site
See Article on Publisher Site

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.

Journal

Annals of Operations ResearchSpringer Journals

Published: Sep 19, 2014

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