Explaining co-movements between equity and CDS bid-ask spreads

In normal times, equity and CDSs are liquid markets. In particular, the CDS market is much more liquid than the underlying corporate bond market. Thus, it is the market to which investors are more likely to turn when they want to take long or short credit positions for a relatively short time.³ While the mix of participants in the equity market is heterogeneous, the CDS market is mainly a trading venue for hedging and speculative activity of institutional investors. For example, banks hedge their large portfolios of loans in the CDS market and hedge funds and private equity firms use CDSs for a variety of trading strategies (popularly known as capital structure arbitrage) that attempt to arbitrage across equity and credit markets.⁴

The microstructure of CDS and equity markets is different. The CDS market is a bilateral dealership over-the-counter market, with no centralized quote disclosure mechanism and with a less than fully competitive network of (private) dealers, usually controlled by a group of major banks.⁵ In the CDS market many banks act as dealers by posting bid and ask quotes for CDS protection. Apart from their role as dealers, banks also use CDSs for managing the risk connected to their own loan exposure (and they are net buyers of CDS protection).⁶ Therefore, some of the dealers in the CDS market potentially have access to companies’ private credit information. The role of the dealers in the equity market is much less ambiguous, as they are liquidity-providers with no particular information advantage on the stocks for which they provide a market. Moreover, stocks are exchange-traded and all dealers can access centralized and transparent quote-disclosure mechanisms.

Despite their differences, in both CDS and equity markets the fundamental role of the dealers is to provide liquidity in their respective assets. The dealer buys a security on her own account (at the bid price) or sells a security from her own account (at the ask price). The bid-ask spread is the cost of a round-trip transaction and also represents the compensation earned by the dealer for providing liquidity. Dealers try to make a profit by maximizing the spreads they earn, given the volumes traded and the costs they have to bear.

I employ data on U.S. companies which remain stable components of the Dow Jones 5-years CDX North America Investment Grade Index (CDX.NA.IG) throughout the whole sample period in order to ensure continuous series of CDS quotes.⁷ I use 5-year CDS contracts because trading liquidity and data availability is highest in this maturity. The CDX.NA.IG index is composed of 125 firms; however, 45 firms remain after excluding financial firms⁸ and companies recording missing values in the CDS series for more than 20 consecutive days over the period 2003–2009. The firms in my sample are investment-grade firms that did not suffer from major distress and restructuring events over the period considered. These companies are publicly traded, have large market capitalization and are typically followed by a large number of analysts. Their stocks and CDSs are typically more liquid than the stocks and CDSs of small and distressed firms. This sample selection ensures more conservative results in terms of detecting substantial equity and CDS illiquidity and commonality across their bid-ask spreads.

For each firm I select the corresponding stock and the 5-years on-the-run credit default swap. I collect daily quotes (bid and ask prices) and daily close trading data (price and volume) for firms’ stocks from The Center for Research in Security Prices (CRSP) Daily Stock dataset. The sample period goes from April 2003 to December 2009. The CRSP stock dataset includes all transactions and quotes from NYSE, AMEX, and NASDAQ. I use daily CDS data from Bloomberg, which in turn sources its CDS data from the Credit Market Analysis (CMA) database.⁹ Mayordomo et al. (2014) show that the CMA database leads in CDS price discovery, when compared to other five major CDS data providers (GFI, Fenics, Reuters, Markit and JP Morgan). After filtering the data,¹⁰ I obtain a daily equity dataset of 75,825 observations and a daily CDS dataset of 72,739 observations.

Prior literature has examined liquidity using different proxies for trading costs, trading frequency or trading impact on prices (see for example Kluger and Stephan 1997) and for different markets (Spiegel 2008). I have information on CDS quotes from CMA, but I do not have transaction prices and traded volumes for CDS contracts over the period 2003–2009. So I am left with one possible measure of liquidity: the CDS bid-ask spread. I want to ascertain that the bid-ask spread is an informative measure of liquidity for equity in order to use bid-ask spreads as consistent and significant measures of liquidity costs for both CDS and equity markets. Since for equity I have information on daily prices, volumes and quotes from CRSP,¹¹ I construct a number of liquidity proxies at weekly frequency (Amihud measure, Roll measure, effective spread, bid-ask spread, run length and inverse turnover index) and then perform Principal Component Analysis (PCA) across all of them. I observe that the pattern of the average equity bid-ask spread over time is consistent with other measures of transaction costs and price impact of trades. However, the PCA reveals that the bid-ask spread has the highest loading in the First Principal Component, amongst all other illiquidity measures.¹²

Since equity bid and ask prices are quoted in dollar terms, while CDS bid and ask prices are quoted in basis points, for CDS bid-ask spread I use the difference between quoted bid and ask prices (as in Bongaerts et al. 2011, Völz and Wedow 2011, Coro et al. 2013; Pires et al. 2015), while for equity bid-ask spread I use the ratio between quoted bid-ask spread and mid-quote price. In the existing literature, the CDS bid-ask spread has been measured by the difference between ask and bid quotes (absolute bid-ask spread, as in Coro et al. 2013, and Pires et al. 2015), or by this difference normalized by the mid-quote point (percentage bid-ask spread, as for example in Hilscher et al. 2015). I favour the former measurement: Pires et al. (2015) provide a convincing numerical argument and show that since the CDS bid-ask spread is already a proportional measure there is no need to divide it by the mid-quote (as it is done instead for the equity bid-ask spread). This choice is particularly appropriate to perform a correct comparison between CDS and equity bid-ask spreads. Using my sample of data from April 2003 to December 2009, I find that the average CDS absolute bid-ask spread is equal to 6 bps; it is therefore lower than the average equity percentage bid-ask of 9 bps. The medians are however both very close to 5 bps. The standard deviations of the bid-ask spreads are equal to 14 bps for equity and 3 bps for CDS.¹³

Given the different distributional properties of the two variables, Figs. 1, 2, and 3 plot the normalized bid-ask spreads to facilitate the comparison of their time-trends over the whole sample and in two sub-samples, before and during the financial crisis (i.e. July 2003–December 2006 and January 2007–December 2009).¹⁴ I observe that equity and CDS bid-ask spreads are closely related: both are downward trending over the pre-crisis period, jump upwards during the crisis period and decline towards the end of the sample.

Pearson’s, Kendall’s Tau and Spearman’s Rho measures of correlation between equity and CDS bid-ask spreads are calculated for each firm over each quarter (with no overlapping observations). The three estimated correlations are used as alternative measures of commonality in illiquidity. Pearson’s correlation (\(\psi\)) measures the degree of linear association between equity and CDS bid-ask spreads. Rank correlation coefficients, such as Spearman’s rank correlation (\(\rho\)) and Kendall’s rank correlation (\(\tau\)), measure how well the relationship between the two variables can be described using a monotonic function, without requiring the function to be linear.¹⁵ The cross-sectional value-weighted averages are 56% for Pearson, 31% for Spearman’s Rho, and 20% for Kendall’s Tau. Table 1 shows instead the distributions of these measures of correlation (averaged over different time samples) across all 45 firms. Despite the dispersion of values being quite wide, the estimated measures remain on average largely positive over the whole sample period (from 15 to 42%), as well as over the sub-samples of 2003 (from 38 to 57%) and of 2007–2009 (from 13 to 27%). Correlation distributions present nearly zero average values only in the middle of the sample (2004–2006). In all subperiods, the correlation measures are statistically significant at the 1% significance level.

To summarize this preliminary statistical analysis, I find evidence of co-movements between equity and CDS bid-ask spreads of 45 firms using different measures of association. However, the co-movement varies over time and becomes prominent only over periods of higher credit risk and market turbulence, such as in 2003 and in 2007–2009. Outside these periods, little or no co-movement is observable.

Equity and CDS bid-ask spreads can surge contemporaneously because of an independent response of equity and CDS dealers to market-wide frictions. Previous literature has pointed out that the ability of dealers to supply liquidity in equity and CDS markets depends on the cost of funding, on the level of market volatility, and on the level of systematic risk (see, amongst others, Brunnermeier and Pedersen 2009). An increase in these factors in fact causes larger uncertainty and inventory risk for dealers, and wider dealership costs. In a crisis period these costs can be so high to force a withdrawal of liquidity supply in both equity and CDS markets and an increase in their liquidity costs. I define this channel of co-movements between equity and CDS bid-ask spreads as the ‘funding channel’.

Let us consider the three groups of agents examined in most market microstructure models: (i) risk-averse dealers; (ii) uninformed risk-averse noise traders; and (iii) well-informed risk-neutral arbitrageurs. As explained in Sect. 2.1, in the CDS market the dealers can be informed or uninformed agents, while noise traders are uninformed agents mostly demanding CDS protection.¹⁶ In the equity market both dealers and noise traders are uninformed agents. Noise traders enter into trades mainly for liquidity reasons. Arbitrageurs acquire and analyse public and private information (at a cost) to discover the ‘fair’ value of the assets, the ‘correct’ hedge ratio between the two markets, and how they vary over time. In this way, they can immediately recognize when prices in the equity and CDS markets are inconsistent and trade in order to profit from the mispricing.

Now let us analyse what may happen when the credit risk of a firm and its debt-to-equity hedge ratio increase and how this may affect the CDS-equity bid-ask spread commonality. I begin by considering the CDS dealers and their hedging needs and then turn to examining the interaction between the dealers in the CDS and equity markets, and the interaction between arbitrageurs and dealers.

The risk-averse (RA) CDS dealer who supplies CDS liquidity to noise-traders (e.g. bond investors) and arbitrageurs can hedge her short CDS unbalanced position (say X) by shorting the corresponding equity (for an amount equal to hX).¹⁷ The implicit cost of the hedging is the bid-ask spread of equity multiplied by the hedge ratio (\(h \times E^{BA}\)).¹⁸ This hedging cost is recovered by the CDS dealer from the bid-ask spread she sets in the CDS market (\(CDS^{BA}\)). When the size of the hedge ratio h increases (this should also be large enough to have a recognizable effect) and the CDS dealer faces an increasing demand for CDS protection from noise-‘CDS buyers’, the cost of hedging surges and becomes a more important component of the CDS bid-ask spread, creating a stronger linkage between the liquidity costs in the CDS market and the liquidity costs in the equity market.

As explained also by Huh et al. (2015) model, when derivative dealers hedge their unbalanced positions in the equity market in presence of asymmetric information, they involuntarily convey a signal to equity dealers about the higher information risk. Some dealers-banks have an informational advantage over equity dealers. As a consequence, equity bid-ask spreads widen and further increase CDS bid-ask spreads. This hedging channel can help to explain not only the effect of the hedge ratio on the bid-ask spread commonality, but also the existence of illiquidity spillovers running from CDS to equity (signal effect) which I report in Appendix 2. The hedging channel mechanism is depicted in the graph below:

Furthermore, past literature has assessed that firm-specific bad news tends to be incorporated first in the CDS market and then in the equity market ahead of possible credit events, when the hedge ratio is particularly high (see, for example, Acharya and Johnson 2007; Qiu and Yu 2012; refer also to my detailed analysis of lead-lag relationships between CDS and equity market in Appendix 2). Xiang et al. (2015) test the economic significance of CDS price discovery on a similar sample to mine (non-financial investment-grade US firms from 2005 to 2009) and document non-trivial economic profits from trading stocks according to the credit risk price signal of the CDS market. The asymmetric information can generate a temporary mispricing between CDS and equity for a specific firm and it can fuel arbitrage trading across the two markets (so-called capital structure arbitrage).¹⁹ For example, if well-informed arbitrageurs (e.g., hedge funds) believe that the market CDS premium for a specific firm is too high with respect to the CDS premium implied by the firm’s equity price S and volatility \(\sigma\,(\widehat{CDS}(S,\sigma ))\), they take a short position (Z) in the CDS and a short position (hZ) in the corresponding equity.²⁰ The size of their cross-market positions is equal or proportional to the debt-to-equity hedge ratio estimated from a sophisticated structural model.²¹ A higher hedge ratio, coupled with a substantial mispricing, therefore commands a larger correlated liquidity demand from informed arbitrageurs across the two markets, to which uninformed CDS and equity dealers react by increasing CDS and equity bid-ask spreads (see Foucault et al. 2014). Thus, if the CDS-equity arbitrage is possible and convenient (i.e. the CDS mispricing and h are significantly above 0), then bid-ask spreads should increase in both markets due to a surge in asymmetric information (Glosten and Milgrom 1985; Kyle 1985; Amihud and Mendelson 1986; Easley and O’Hara 1987; Admati and Pfleiderer 1988).²²

This arbitrage channel represents another potential source of CDS-equity bid-ask spread commonality. Its mechanism is depicted in the graph below:

To sum up, when a firm’s credit condition worsens and its debt-to-equity hedge ratio increases, higher commonality between CDS and equity bid-ask spreads can arise because of:

Next, I perform two tests on the existence of the ‘funding channel’ and ‘hedging–arbitrage channel’:Sections 4 and 5 illustrate the tests’ methodologies and results.

Tables 2 and 3 respectively report the pair-wise correlation and the (Granger) causality matrices for the relevant variables. The hedge ratio is highly correlated with bid-ask spread commonality and return commonality.²⁷ The bid-ask spread commonality and the hedge ratio are also closely related to the market default risk, the VIX index, the TED spread, and the SMB systematic risk-factor (see Table 2). The pair-wise causality matrix in Table 3 suggests that the hedge ratio Granger-causes all commonality variables. However, market default risk and VIX index Granger-cause both the commonality variables and the hedge ratio. This justifies a control for the orthogonalised hedge ratio (\(H^{SS,ORT}\)). The causality relationship between bid-ask spread commonality and return commonality remains instead ambiguous and their correlation is only about −7%. Thus, the return commonality proxy is not included in right-hand side of Eq. (1).

The hedge ratio represents a first approximation to the arbitrage relationship between equity and CDS; in fact, it is obtained as the elasticity of the CDS (or underlying debt) value to the equity value of the firm (see Appendix 1). Figure 4 illustrates the time-series plot of the value-weighted average of the hedge ratio across all firms. It shows that the average debt-to-equity elasticity (hedge ratio) H and sensitivity h gradually decrease from 2003 over the following years; they then rise again from the second semester of 2007 and decrease towards the end of 2009. Figure 5 displays a similar pattern for both the average hedge ratio estimated with SS methodology and with VX methodology (April 2003–November 2008). Noticeably, Fig. 6 reveals a very close relationship between the average hedge ratio and CDS-equity bid-ask spread commonality over time.

The panel analysis in Table 4 (Panel A) reveals positive and significant effects of the TED spread, VIX index, and systematic risk factors on the bid-ask spread commonality, but also a positive influence of the hedge ratio, after controlling for firms’ unobservable fixed effects.²⁸ The positive effect of the hedge ratio on the bid-ask spread commonality survives when I replace the hedge ratio with its component orthogonal to market default risk and market volatility (\(H^{SS,ORT}\)). I also evaluate the separate economic impact of the hedge ratio versus the impact of market frictions and systematic risk factors. In Table 4 (Panel B) I notice that the aggregate economic significance of all systematic factors is about 0.60 (in terms of standard deviations impact) and the economic significance of the hedge ratio is around 0.16.²⁹

In Table 5 I control more directly for time-effects. This check is needed since the period analyzed includes the crisis event. I notice that when the hedge ratio is interacted with time-quarter dummies (Panel A), its positive effect on the bid-ask spread commonality variable is strong and significant only during the first two quarters of 2003, the first three quarters of 2007, the third quarter of 2008 and the second quarter of 2009. The effect of the hedge ratio instead decreases and even turns negative but insignificant during the third quarter of 2005. Furthermore, the panel analysis results reported in Table 5 (Panel B), where the bid-ask spread commonality is regressed on the hedge ratio, the firm’s exposure to systematic risk and the time dummies, confirm that the positive and significant effects of hedge ratio and systematic risk is not wiped out after controlling for time fixed effects.

A possible concern may come from the fact that the hedge ratio—both measures implied from the Merton structural model—relates to the firm’s volatility and leverage which capture the firm’s individual risks. These risks are also underlying factors for the illiquidity commonality between equity and CDS. If the hedge ratio is just a proxy of the firm-specific risks, the conclusion that hedging and arbitrage trading are determinants of the illiquidity co-movement may be in doubt. In order to address this concern and check whether the hedge ratio carries information on the hedging–arbitrage activity, I instrument this variable and carry out a Haussman-Wu test and a Two-Stage Least Squares estimation of Eq. (1) Specification I. As instruments I select variables that proxy the ‘sources’ of hedging activity of CDS dealers (namely, higher risk aversion, higher CDS demand pressure, and higher asymmetric information), and the ‘sources’ of cross-market arbitrage trading (namely, higher asymmetric information and larger CDS-equity mispricing). The risk aversion is proxied by the VIX index. In absence of CDS transaction data, the CDS demand pressure is proxied by the increase in total bonds’ traded volume (\(\Delta BondV\)). If there is an increase in the volume of traded bonds for a specific firm, it is more likely that there would also be an increase in the demand for CDS protection from bondholders (and therefore higher CDS demand pressure). To obtain this measure, I select for each firm the transacted volumes for all its traded bonds from the TRACE database; I then sum the bonds’ volumes and take the first differences. The asymmetric information (AsymInfo) is proxied by the dispersion of analysts’ forecasts. If there is wide analysts’ disagreement on the future perspectives of a well-known large U.S. firm, this means that less public information is available and the risk of asymmetric information is higher. I construct the dispersion of analysts’ forecasts on firm’s earnings per share over a forecasting period of three months as the ratio between the standard deviation of earnings forecasts for each firm across all analysts and the relative median. I use analysts’ forecasts of earnings per share taken from the Institutional Brokers Estimate System (I/B/E/S) database.³⁰ Finally, the CDS-equity mispricings (Log CDS Mispricing) are obtained as residuals from a structural model. For this purpose, I use an implicit structural model that includes the usual controls for firms’ leverage ratio, asset volatility and for short spot interest rate (Merton 1974), but also controls for firms’ (equity) illiquidity, size and market-wide default risk.

To evaluate the goodness of the instruments, I perform the usual checks. First, the instruments should present low and statistically insignificant correlation with the errors of the panel Eq. (1) Specification I. So I check heuristically whether the residuals from the original panel regression Eq. (1) Specification I are correlated with the four proposed instruments. The correlation between the residuals and the analysts’ forecasts dispersion variable is about −3% and it is not statistically significant. The correlation between the residuals and log CDS mispricing is about −14% but statistically significant at the 5% level, while the correlation between the residuals and the change in bonds’ traded volume is about −5% and statistically insignificant. The correlation between the residuals and VIX is 0.3% and not statistically significant. The unconditional correlations of the instruments with the residuals are low and insignificant, with the exception of log CDS mispricing which is therefore excluded from the instrument list. I perform a further check on the conditional correlations: I regress the residuals on the three remaining instruments and find that none of them represents a significant explanatory variable. The F-statistics of this regression also rejects their joint significance. Second, the instruments should be significantly correlated with the hedge ratio. I check the correlations between the hedge ratio and AsymInfo, \(\Delta BondV\), and VIX. These are respectively 15, 7, and 55% and are all statistically different from 0 at the 1% significance level. So the asymmetric information proxy, the VIX index, and the change in bonds’ traded volume are used as instruments in this robustness analysis.

In Table 6 (Panel A) I present the results of the Hausmann–Wu test. For this test, I run an auxiliary panel regression which includes the hedge ratio as the dependent variable and the three instrumental variables as regressors. The fitted values \(\widehat{H}\) from this regression are then used as additional regressor in the panel Equation (1) Specification I. If the hedge ratio H captures already the drivers of the hedging–arbitrage trading, then \(\widehat{H}\) should not appear significant in the regression. Given my choice of instruments, I find that the coefficient of the fitted variable \(\widehat{H}\) appears in fact insignificant.³¹ Using the same instrumental variables, I also perform a 2-Stage Least Squares estimation (with and without fixed effects) of Eq. (1) Specification I. The results are reported in Table 6 (Panel B) and confirm the significance of the hedge ratio’s coefficient, the robustness of analysis and its interpretation.

As a final robustness check I repeat the analysis (Specifications I and II) using:A comparison between the results of the three alternative regressions in Panel A of Table 7 shows that using Spearman’s instead of Kendall’s correlation does not change the results, while using the Pearson correlation as the dependent variable in the regression leads to the hedge ratio and the size factor being the only significant variables. Also, the economic impact of the hedge ratio (unreported) remains in a range between 0.13 and 0.16 standard deviations.

When I replace the hedge ratio estimated using the Schaefer and Strebulaev (SS) methodology with the one estimated using the Vassalou-Xing (2004) methodology, I find that the latter is also significant in the panel regressions (Table 7, Panel B) and has very similar economic significance to the SS hedge ratio (0.15–0.17 standard deviations). However, the R-squared halves with respect to the case when I use the SS hedge ratio, so the Vassalou-Xing measure of the hedge ratio appears less useful than the SS measure.

To sum up the results in this section, the equity-CDS bid-ask spread commonality increases with higher funding costs, higher market volatility and systematic risk. The debt-to-equity hedge ratio is also found strongly significant, both statistically and economically, and survives several robustness checks. The hedge ratio appears to capture well the effects of hedging and arbitrage trading on the equity-CDS bid-ask spread commonality. There is evidence of both a ‘funding channel’ and a ‘hedging–arbitrage channel’.

In the panel regression analysis at the weekly frequency for CDS bid-ask spreads (Table 8) market volatility (VIX) is found positively significant. After controlling for all cost-components, the economic impact of VIX is always in a range between 0.23 and 0.25 SD (depending on the regression specification, when using the whole sample of data or the crisis sub-sample). In addition, the hedging-cost component enters significantly in all estimated equations, also when I include a control for (time and firms) fixed effects or the VIX index. A 1 standard deviation (SD) change in hedging costs generates an increase of around 0.4 SD in the CDS bid-ask spread. The hedging cost component alone can explain about one third of the variation in the CDS bid-ask spreads. When I add other control variables (particularly time-fixed effects), the impact of the hedging costs on the CDS bid-ask spread remains still significant, but it is halved.

In principle, the effect of hedging costs on CDS bid-ask spreads should be larger when the CDS demand from noise bondholders is higher. However, I find that the hedging cost interacted with the change in the amount of traded bond volumes has at most a weakly significant positive coefficient (with less than 10% significance level). In terms of economic impact, the interaction term adds on average only 0.09 SD to the impact of the hedging factor.³³ \(^{,}\) ³⁴ I find instead that the hedging activity of CDS dealers becomes a more significant cost-component of the CDS bid-ask spread when it is triggered by higher asymmetric information and speculative demand, rather than by higher uninformed liquidity demand in the CDS and bond markets. In fact, the hedging cost interacted with the analysts’ forecast dispersion is positively significant at the 1% significance level. On average a 1 SD increase in the interaction variable has an additional impact of 0.17 SD on the hedging factor.³⁵ When I interact the hedging cost with the lagged positive CDS mispricing, I observe also a significant effect (at 1% significance level). On average, a 1 SD increase in this interaction variable has an additional impact of 0.11 on the hedging factor.³⁶ When both the CDS mispricing and the asymmetric information proxy are interacted with the hedging cost variable, the latter becomes insignificant.

Table 8 also shows that the CDS mispricing variable alone is found highly significant (with 1% significance level and 0.32 SD economic impact). Interestingly, when the CDS mispricing is included in the panel regression together with the proxy for asymmetric information, the latter appears only weakly insignificant (at 10% significance level). These results seem to suggest that: (1) the asymmetric information risk is connected to some form of speculative trading across markets; (2) potential speculative demand can also affect directly the CDS bid-ask spreads, outside the hedging channel. CDS dealers do not know with certainty the timing and size of capital structure arbitrageurs’ trading and cannot completely protect themselves by hedging in the equity market. Some unhedged information risk remains and increases dealership costs further.

To conclude, the CDS bid-ask spread is significantly influenced by the cost of dealers’ hedging activity in the equity market. This activity consolidates the linkage between the liquidity of the CDS market and the liquidity of the equity market. Higher asymmetric information and higher speculative demand increase the effect of CDS dealers’ hedging activity on the CDS bid-ask spread, more significantly than higher uninformed liquidity-demand. The explanatory power of analysts’ disagreement and CDS mispricing (proxy for arbitrage interest) suggests that CDS dealers may not be able to perfectly hedge against the risk of informed speculative activity.³⁷ Time-fixed effects and higher market volatility are also found highly significant in explaining an increase in CDS dealership costs and bid-ask spreads and capture the crisis effect. The most complete specifications of the panel regression in Eq. (2) exhibit impressive adjusted \(R^2\)s between 50 and 72%. The adjusted \(R^2\)s drop to 40–55% when fixed effects are removed, but they are still large. In separate panel analysis, I also find a strong effect of CDS bid-ask spreads and VIX on equity bid-ask spreads, but I observe that the equity bid-ask spreads are not significantly influenced by CDS mispricings and asymmetric information. The information risk signal is instead conveyed to the equity dealers by the CDS dealers via their hedging activity. In fact, when I include the past value of the CDS dealers’ hedging costs in the panel equation (I use past values to avoid endogeneity issues), I find that this variable is highly significant to explain an increase in equity bid-ask spreads. However, the adjusted \(R^2\) of this regression is lower than the one obtained from the regressions of CDS bid-ask spreads.³⁸