“When there is rain, umbrellas become expensive. But when there is no rain, nobody cares about the umbrella and the prices are low. The case of Liquidity is similar.”
Yakov Amihud, Moneylife Magazine, 01/28/2014.
We show that commonality in liquidity is priced in both the cross-section and time-series of credit default swap (CDS) premia. Protection buyers earn a statistically significant and economically important discount for bearing the risk of individual CDS illiquidity co-moving with CDS market illiquidity. The pricing of commonality in CDS liquidity is different for calm and crisis periods as we find liquidity risk to be a priced factor in CDS spreads only during the recent financial crisis. Additionally, we find evidence that liquidity seems to be more important for the pricing of CDS than fundamentals from structural models of default risk.
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Anecdotal evidence for the importance of liquidity in CDS markets is also given by the 2012 trading loss at JP Morgan estimated at 2 billion USD that was caused by the excessive accumulation of outsized CDS positions through their London branch.
Note that CDS symbols (Mnemonics) in Datastream are constructed from two strings. The first string refers to the company’s name and consists of no more than five digits. The second string specifies the seniority and maturity of the debt. In our case, the second string is ’S5’ and denotes CDS contracts that refer to senior-debt issues with a maturity of 5 years.
For instance, the CDS time series of General Electric exhibits no variation after June 2007 and is therefore deleted from the final sample.
Note that positive changes in the S&P500 index are associated with declining default probabilities and increasing recovery rates.
Note that, in fact, the bid-ask spread is a measure of illiquidity rather than a liquidity proxy.
Again, we calculate industry-specific liquidity measures separately for each firm in a given industry sector due to endogeneity concerns and mechanical correlations.
Note that, in case of the liquidity commonalities of firm \(i\), we exclude this firm from the calculation of the corresponding industry-specific liquidity to avoid mechanical correlations.
This seems appropriate given the evidence for a unit root presented in Corò et al. (2013) for a comparable set of variables.
In the following, we refer to these variables as theoretical variables or credit risk variables. Pairwise correlations for our independent variables and covariates are shown in Table 4.
The standard deviation is calculated on the first-differenced variable.
Note that we eliminate the influence of mechanical correlation in the market-wide liquidity measure by excluding firm \(i\) from the computation of averages.
See Petersen (2009) for a comprehensive discussion on the estimation of standard errors in panel data.
Note that we can only perform this analysis for the sample period after Q2:2006 since we do not obtain equity-related bid and ask quotes from Datastream prior to this date.
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We received helpful comments from an anonymous referee that led to further improvements of the paper. Support by the Collaborative Research Center “Statistical Modeling of Nonlinear Dynamic Processes” (SFB 823, Project A7) of the German Research Foundation (DFG) is gratefully acknowledged.
See Table 11.
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Meine, C., Supper, H. & Weiß, G.N.F. Do CDS spreads move with commonality in liquidity?. Rev Deriv Res 18, 225–261 (2015). https://doi.org/10.1007/s11147-015-9110-y