Comparing Possible Proxies of Corporate Bond Liquidity

Houweling, Patrick; Mentink, Albert; Vorst, Ton

doi:10.2139/ssrn.307419

Cited by 74 publications

(56 citation statements)

References 59 publications

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“…For example, Chordia et al (2003), Chacko (2005), Driessen (2005) and Downing et al (2007) showed that liquidity risk is a major determinant of observed credit spreads. 3 Other studies including Collin-Dufresne et al (2001), Delianedis and Geske (2001), Perraudin and Taylor (2003), Houweling et al (2005), Ericsson and Renault (2006) and Van Landschoot (2008), De Jong and Driessen (2012), Chen et al (2014), Tseng et al (2014) and Schwartz (2014), also showed a strong, negative relationship between credit spreads and liquidity. 4 In fact, a decrease in the corporate bond market's liquidity implies a market preference for less risky assets and a higher risk premium to cover the liquidity risk.…”

Section: Theoretical and Empirical Backgroundmentioning

confidence: 94%

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Modelling credit spreads with time volatility, skewness, and kurtosis

Clark

Baccar

2015

Ann Oper Res

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This paper seeks to identify the macroeconomic and financial factors that drive credit spreads on bond indices in the US credit market. To overcome the idiosyncratic nature of credit spread data reflected in time varying volatility, skewness and thick tails, it proposes asymmetric GARCH models with alternative probability density functions. The results show that credit spread changes are mainly explained by the interest rate and interest rate volatility, the slope of the yield curve, stock market returns and volatility, the state of liquidity in the corporate bond market and, a heretofore overlooked variable, the foreign exchange rate. They also confirm that the asymmetric GARCH models and Student-t distributions are systematically superior to the conventional GARCH model and the normal distribution in in-sample and out-of-sample testing.

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Section: Theoretical and Empirical Backgroundmentioning

confidence: 94%

“…In practice, however, they seem to have limited explanatory power (e.g. Jones et al 1984;Ogden 1987;Delianedis and Geske 2001;Elton et al 2001;Lyden and Saraniti 2000;Houweling et al 2005;Ericsson and Renault 2006), leading to the belief that other common factors exist that explain the remaining variation.…”

Section: Theoretical and Empirical Backgroundmentioning

confidence: 99%

Modelling credit spreads with time volatility, skewness, and kurtosis

Clark

Baccar

2015

Ann Oper Res

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“…Although most of the studies focused on the US markets, a bunch of contributions analysed data on euro-denominated bonds (Annaert and De Ceuster, 1999;McGinty, 2001;Dìaz and Navarro, 2002;Houweling et al, 2005;.…”

Section: The Determinants Of Corporate Bond Liquidity: a Survey Of Thmentioning

confidence: 99%

“…Another rationalization hinges on the low-information-costs argument (large issues have less information costs, since more information is disseminated among investors and more investors own and analyze them). In addition, smaller issues are more easily absorbed by buy-and-hold investors who reduce trading and, hence, liquidity (see Houweling et al, 2005, for references on these views). However, some contrarian evidence is provided by McGinty (2001) who finds that the issued amount has a negative impact on liquidity.…”

Section: The Determinants Of Corporate Bond Liquidity: a Survey Of Thmentioning

confidence: 99%

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The Liquidity of Dual-Listed Corporate Bonds. Empirical Evidence from Italian Markets

et al. 2014

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We compute some indicators (zero-trade, turnover ratio, Amihud price impact, and Roll bid-ask spread) to examine the liquidity conditions of corporate bonds traded on the main Italian retail bond markets from January 2010 to June 2013. In order to compare market liquidity for identical securities, our analysis focuses on fragmented bonds, i.e. bonds traded concurrently on two different venues: either DomesticMOT and EuroTLX, or ExtraMOT and EuroTLX. As for bonds traded on DomesticMOT and EuroTLX, the Amihud and the Roll statistics suggest EuroTLX being more liquid. Moreover, irrespective of the trading venue, on average bank bonds are less liquid than bonds issued by non-financial companies, especially from 2011 due to the impact of the sovereign debt crisis. With regard to bonds traded across ExtraMOT and EuroTLX, the latter is characterized by better liquidity conditions, with bank bonds being more liquid than non-financial ones. Furthermore, we find evidence of better liquidity figures for Italian bonds (nationality), structured bonds (complexity), and securities with greater minimum trading size (MTS). We also find that bonds' features (issuers' nationality and industry; bonds' residual maturity, complexity, rating, etc…) affect liquidity differently depending upon the trading venue, thus supporting the view that market microstructure may play a relevant role. Finally, we investigate the effect of fragmentation by comparing the liquidity of dual-listed bank bonds fragmented across DomesticMOT and EuroTLX with otherwise similar bank bonds traded exclusively on DomesticMOT. Italian fragmented bank bonds turn out to be slightly more liquid than similar Italian bonds traded exclusively on DomesticMOT; whereas, the opposite holds for foreign bank bonds. However, overall there is not a clear-cut evidence on the effect of fragmentation on bond liquidity, probably because it is intertwined with bonds' attributes, such as the issue size (in our sample, higher for the Italian bank bonds).JEL Classification: G01, G10, G12, G18

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Investor Sentiment and Corporate Bond Yield Spreads

Nayak

2010

Review of Behavioral Finance

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Although the pervasive influence of investor sentiment in equity markets is well documented, little is known about behavioral manifestations in bond markets. In this paper, we explore the impact of investor sentiment on corporate bond yield spreads. Our results reveal that bond yield spreads co-vary with sentiment, and sentiment-driven mispricings and systematic reversal trends are very similar to those for stocks. Bonds appear underpriced (with high yields) during pessimistic periods and overpriced (with low yields) when optimism reigns. Consequent reversals result in predictable trends in post-sentiment yield spreads. When beginning-of-period sentiment is low, subsequent yield spreads are low; high sentiment periods are followed by high spreads. High-yield bonds (low ratings, Industrials and Utilities, extreme maturities or low durations, specially if low rated) demonstrate greater susceptibility to mispricings due to sentiment compared to low-yield bonds. The incremental yield spread gap between highand low-yield bonds converges subsequent to periods of low sentiment, and diverges after high sentiment. Equity attributes marginally influence the impact of sentiment on bond spreads, but mostly for distressed bonds only.

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Comparing Possible Proxies of Corporate Bond Liquidity

Cited by 74 publications

References 59 publications

Modelling credit spreads with time volatility, skewness, and kurtosis

Modelling credit spreads with time volatility, skewness, and kurtosis

The Liquidity of Dual-Listed Corporate Bonds. Empirical Evidence from Italian Markets

Investor Sentiment and Corporate Bond Yield Spreads

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