Noise Trading in the Credit Default Swap Market
Felix Irresberger∗
TU Dortmund University
Gregor N.F. Wei߆
University of Leipzig
February 1, 2016
ABSTRACT
We show that noise trading affects the cross-section and time-series of credit default swap (CDS)
spreads. We employ internet search volume data to proxy for an exogenous shock to the pessimistic
sentiment of households about credit risk at the market level. Consistent with the conjecture of
significant noise trading existing in the CDS market, we find our proxy of household sentiment to
be a significant driver of CDS spreads. Our results are in line with theories that emphasize the role
noise trading plays in providing market liquidity with higher levels of sentiment being associated
with lower average CDS spreads.
Keywords: Credit default swaps, noise trading, household sentiment, market liquidity.
JEL Classification Numbers: G21, C58, G01.
∗
Otto-Hahn-Str. 6, D-44227 Dortmund, Germany, telephone: +49 231 755 8212, e-mail: [email protected].
†
Corresponding author: Grimmaische Str. 12, D-04107 Leipzig, Germany, telephone: +49 341 97 33820, e-mail:
[email protected].
1 Introduction
Economic intuition suggests that noise trading should have a weaker impact (if any) on the prices
of derivatives than on the prices of the underlying securities. As institutional (and thus presumably better informed) investors dominate derivatives markets, prices should not be influenced
as strongly by investors’ attention and sentiment as it is well documented for stocks (see, e.g.,
Baker and Wurgler, 2006, 2007; Tetlock, 2007; Da et al., 2011, 2015). This view has been challenged by empirical evidence by Han (2008) who finds that investor sentiment about the stock
markets significantly affects the prices of stock options. Perhaps even more surprising is the finding of Hilscher et al. (2015) that informed traders are primarily active in the equity market rather
than the market for credit default swaps (CDS) with stock returns leading returns on CDS contracts. Despite these initial results, however, no study has so far analyzed the role of noise trading
for the pricing of credit default swaps.
In this paper, we study how noise trading affects the cross-section and time-series of CDS
spreads. We construct a proxy for households’ pessimism about overall credit risk in the economy
(“default sentiment”) and show that it is significantly drives CDS premia. Surprisingly, default
sentiment is negatively correlated with CDS spreads. Our proxy for default sentiment does not
only explain a significant portion of the cross-sectional and time series variation in CDS premia,
but it also complements rather than substitutes competing macroeconomic covariates. We then test
and confirm the hypothesis that sentiment shocks influence CDS spreads via the market liquidity
channel where uninformed traders overreact to pessimistic sentiment, thereby boosting market
liquidity and lowering CDS spreads.
We use weekly internet search volumes for queries on Google that are related to pessimism
concerning default risk to build our proxy for household sentiment at the market-level. A key
advantage of using search volume data from Google is that it provides us with the opportunity to
construct a proxy of household pessimism that should be exogenous to institutional investors in
the CDS market. Although it is quite possible that traders in the CDS market also use Google
searches to gain information on expected default probabilities of individual firms, these biasing
1
influences should be smoothed out by the aggregated search volumes of millions of Google users.
We then regress the CDS spreads of 228 firms over a period of 351 weeks from 2004 to Q3:2010 on
our sentiment proxy and various control variables and find the following key results: First, higher
sentiment lowers average CDS spreads even when controlling for various proxies of average credit
risk in the economy. Second, the correlation between default sentiment and CDS spreads in our
regressions is highly sensitive to the inclusion of proxies for CDS market liquidity.
We test two different channels through which sentiment might affect CDS spreads. The first
hypothesis that we test then postulates that shocks in household pessimism reflect actual increases
in the default risk of firms in the economy. In other words, the pessimistic sentiment of households
turns out to be a true information signal to which both informed and uninformed traders react.
There are (at least) two reasons for why household sentiment could reflect actual overall credit
risk. One reason could be that households are able to observe pervasive increases in default risk in
the economy, a fact, which is then revealed by their concerns expressed via their Google searches.
Alternatively, it could simply be that both household sentiment and overall credit risk are jointly
driven by macroeconomic covariates. In both cases, household sentiment (reflecting an increase
in default risk) should unambiguously be positively related to CDS spreads as both informed and
uninformed traders buy protection against genuinely higher credit risk.
The second hypothesis, the one which we empirically confirm in our study, is concerned with
the increasing effect of noise trading on market liquidity. In case the concerns of households
regarding overall credit risk are not justified by fundamentals, uninformed traders could still (over) react to the pessimistic signal while informed traders take short positions. In contrast to the first
scenario described above, the effect of sentiment on CDS spreads then becomes ambiguous. As
shown by Baker and Stein (2004) in their model, the overreaction of uninformed traders can have
a positive effect on market liquidity in such a situation. Consequently, the increasing effect of
sentiment on CDS spreads might be offset by the side-effect caused by an increased liquidity in
the CDS market (and thus a higher liquidity of the individual CDS contract).
All our empirical findings support this second explanation. Our proxy for default sentiment
2
significantly drives changes in CDS spreads in both time series and panel regressions. Increases
in default sentiment are associated with decreases in CDS spreads. This finding is robust to the
inclusion of various idiosyncratic and macroeconomic variables with one important exception: the
negative correlation between our sentiment proxy and CDS spreads immediately becomes insignificant as soon as we include a proxy for CDS market liquidity. Analyzing this result in more detail,
we find our proxy of default sentiment to be strongly negatively correlated with the bid-ask spread
of a CDS market index. Moreover, default sentiment explains a significant portion of the variation
in individual CDS contracts’ liquidity, especially in times of higher limits to arbitrage as observed
during the financial crisis. Finally, we find this liquidity-induced negative relation between sentiment and CDS spreads to be limited to sentiment shocks that are closely related to default risk as
proxies of households’ sentiment towards the general economic outlook are expectedly positively
correlated with CDS spreads. The negative relation between default sentiment and CDS spreads,
however, remains unchanged in its significance.
The results that we find in this study are important for several reasons. First, analyzing the
impact of noise trading on CDS spreads helps explain the puzzle why observable firm-specific and
macroeconomic covariates fail to explain the variation in CDS spreads despite their usefulness for
predicting default.1 Second, from a risk management perspective, understanding the role sentiment
plays in the pricing of credit risky securities is important for trading and hedging CDS contracts,
especially in times of higher limits to arbitrage. Next, the results we document are also of significant value to regulators for understanding the mechanics driving the systemic importance of the
CDS market. Finally, and perhaps most importantly, our results add a wholly new argument to
the current discussion of the unclear role liquidity and liquidity risk play in the pricing of CDS
contracts (see Tang and Yan, 2014; Bongaerts et al., 2011; Junge and Trolle, 2015).
Our study is related to several studies in the literature on asset pricing that have addressed the
question whether investor attention and investor sentiment are priced in the cross-section of stock
returns. While traditional models of asset pricing assume an instantaneous incorporation of news
1
See, e.g., the studies of Shumway (2001), Duffie et al. (2007), Chava et al. (2011), and Galil et al. (2014). As a
solution for this puzzle, Doshi et al. (2013) propose a discrete-time no-arbitrage model with observable covariates.
3
into asset prices, in reality, asset prices could be influenced by limits on attention due to scarce
cognitive resources of investors (see, e.g., Kahneman, 1973; Sims, 2003; Peng and Xiong, 2006).
Quite similarly, asset prices could also be influenced by investor sentiment as irrational noise
traders and limits on arbitrage will lead to mispricings and excess volatility (see De Long et al.,
1990). Although earlier studies do not find attention to be generally priced in stock returns (see,
e.g., Cutler et al., 1989; Kogan et al., 2006, 2009), there now seems to be a consensus in the literature that increases in both investor attention and investor sentiment can lead to large movements in
asset prices at least in the short-run (see, e.g., Baker and Wurgler, 2006; Tetlock, 2007; Da et al.,
2011, 2015).
In contrast, empirical evidence on noise trading in derivatives markets is much less clear than
for equity markets. In one of the first studies on this subject, Figlewski (1989) finds standard
arbitrage in the market for stock index options to be heavily exposed to risk and transaction costs.
In a related study, Figlewski and Green (1999) point out in their empirical study that imperfect
pricing models can induce considerable risk exposure to option writers. More recently, Ofek et al.
(2004) find empirical evidence of limited arbitrage in options market. However, explicit tests for
the presence of investor sentiment in options markets are scarce with the work of Han (2008)
being a recent exception. All of these studies, however, have so far concentrated on stock options
neglecting the possibility that corporate default and the prices of credit risky securitites are as well
sensitive to changes in investor or creditor sentiment.
Our study is also related to a large body of literature that tries to explain the variation of credit
spreads and credit default swap premia (and their changes) in the cross-section and time series.
Although early studies in this strand of literature have questioned the explanatory power of observable covariates for credit spreads,2 recent studies by Ericsson et al. (2009), Doshi et al. (2013),
and Meine et al. (2015b) show that macroeconomic and firm-specific information can explain a
significant portion of the variation in CDS spreads over time and across firms. To the best our
knowledge, however, only the study by Hilscher et al. (2015) has investigated the role of noise
2
See, e.g., Collin-Dufresne et al. (2001); Campbell and Taksler (2003); Cremers et al. (2008).
4
trading in CDS markets so far. We complement their work by building on the rich asset pricing literature on investor sentiment and identify market-level sentiment as a previously neglected
covariate in regressions of CDS premia.
The rest of this paper is organized as follows. In Section 2, we quickly review the literature
and build our two main hypotheses on the expected effect of default sentiment on CDS spreads.
Section 3 presents our empirical strategy. The construction of our sample and our empirical results
are discussed in Section 4. Section 5 concludes.
2 Theoretical background and hypothesis building
In this section, we first derive and discuss the two main hypotheses that we test in out study.
Then, we shortly comment on the usefulness of internet search volume data for measuring sentiment.
2.1 Default sentiment and the pricing of credit derivatives
In theory, market-level sentiment could have both a positive and negative effect on prices of
credit derivatives.
On the one hand, market-level sentiment is likely to have a significant impact on credit risk as
it reflects consumer and creditor behaviour.3 More precisely, search volumes at the market-level
for terms like “credit risk”, “default”, or “bankruptcy” reveal concerns of investors and creditors
about losses from their investments and loans. These concerns could be caused by earlier spikes
in default probabilities making our proxy for market-level sentiment an indicator of contemporaneous market-wide increases in credit risk. Alternatively, creditors could search for credit-related
terms simply out of fear of future losses. However, increases in market-level sentiment should in
both cases coincide with a decline in private consumption and investments and thus ultimately a
worsening of the overall state of the economy.
3
In this respect, our Google-based proxy for market-level sentiment is similar to, e.g., the University of Michigan
Consumer Sentiment Index.
5
In fact, there exist at least two channels through which the sentiment of households at the
market-level could influence default risk. First, higher market-level sentiment and worsening
macroeconomic conditions should influence corporate default via average recovery rates in the
economy. As shown in the model of Shleifer and Vishny (1992) and confirmed empirically by
Acharya et al. (2007), firms that default amidst a time of poor macroeconomic conditions have
lower asset values at liquidation and thus lower recovery rates (see, e.g., Duffie and Singleton,
1997, 1999). Market-level sentiment, on the other hand, should capture declines in the overall state of the economy better than measures that are only available at low frequencies (e.g.,
GDP growth rates) or indirect proxies (e.g., option implied volatilities in the form of the VIX, or
mutual fund flows). Second, corporate default itself is closely associated with macroneconomic
conditions. Several studies have empirically tested and confirmed that default probabilities are
in large part determined by macroeconomic covariates like, e.g., the quarterly growth in industrial production, interest rate levels, or stock market indexes (see, e.g., Blume and Keim, 1991;
McDonald and Van de Gucht, 1999; Hillegeist et al., 2004; Pesaran et al., 2006).4
Alternatively, just like equity markets (see Shleifer and Vishny, 1997; Baker and Wurgler,
2006, 2007), credit derivative markets could be influenced by limits to investor attention and limits
to arbitrage. As CDS markets are dominated by institutional investors, CDS prices should suffer
to a lesser extent from mispricings caused by noise traders and limits to arbitrage. However, limits
to arbitrage could just as well exist in credit derivative markets with sentiment driving (at least in
part) the relative demand for speculative investments in selected CDS contracts. This reasoning is
in line with a large number of studies on investor behavioral biases in options markets. 5.
Finally, CDS premia could also depend directly on macroeconomic covariates as shown in the
study of Doshi et al. (2013). They propose a discrete-time no-arbitrage model which allows for
a closed-form solution for CDS prices based on observable macroeconomic and firm-specific co4
In the study by Duffie et al. (2007), only the three-month Treasury bill rate and the trailing one-year return on the
S&P 500 index are identified as significant covariates for explaining estimated default. Recent studies on CDS pricing
have adopted this set of macroeconomic covariates (see Ericsson et al., 2009; Doshi et al., 2013).
5
See, for example, Figlewski (1989), Stein (1989), Figlewski and Green (1999), Poteshman (2001),
Poteshman and Serbin (2003), Bollen and E. (2004), Mahani and Poteshman (2008) and Ofek et al. (2004)
6
variates. Although they test the significance of various macroeconomic indices (e.g., the consumer
price index (CPI), producer price index (PPI), real GDP, etc.) in their empirical study, the potential
impact of a direct measure of investor and creditor sentiment on CDS premia remains unexplored.
Consistent with what we refer to as the default risk hypothesis, sentiment could thus be positively related to CDS premia either because of the correlation of our sentiment measure with
macroeconomic covariates and recovery rates, the price impact of limits to arbitrage and noise
traders, or because sentiment genuinely reveals increases in overall default risk:
H1 : Increases in default sentiment lead to higher CDS spreads (default risk hypothesis).
On the other hand, default sentiment could lower CDS spreads by increasing CDS market
liquidity to such an extent that the price impact of noise trading is off-set. Let us assume that
both informed and uninformed investors trade in the CDS market, and that inattentive traders (e.g.,
insurers, pension funds, but also banks) are short-sales-constrained. As shown by the model of
Baker and Stein (2004), sentiment could cause market liquidity to increase causing an ambiguous
impact on asset prices. In the context of the CDS market, their reasoning reads as follows. Uninformed traders could overreact to unjustified, pessimistic signals about overall default risk in the
economy and be tempted to buy credit protection. Informed traders would take short positions.
When informed investors trade on a subsequent date, irrational investors will also believe their
information to be better than that of the informed traders and thus will the market more liquid
even in the face of such informed trading. In case the inattentive traders receive positive signals
about default risk, however, their short-sales-constraint will keep them out of the market. As a
consequence, the overreactions of uninformed traders could have a positive effect on market liquidity and default sentiment could lower CDS spreads to some extent as it increases overall market
liquidity (and thus the liquidity of CDS contract’s with higher liquidity risk). Considering the importance of liquidity for CDS pricing (see, e.g., Tang and Yan, 2014), it is safe to assume that such
a liquidity effect would more offset the positive price impact of noise trading.
For this reasoning to be true, of course, both of the above made assumptions should be
valid. However, both assumptions appear to be reasonable in the context of the CDS market.
7
Hilscher et al. (2015) confirm in their empirical study that the proportion of inattentive investors
in the CDS markets in fact seems to be larger than in the equity market. Moreover, especially
inattentive traders that only enter the CDS market to hedge their credit exposure are likely to be
short-sales-constrained (either voluntarily or due to regulatory rules). This conjecture is also underlined by Mengle (2007) and Bongaerts et al. (2011) who note that long-term investors usually
sell credit protection while commercial banks only appear to use CDS contracts for hedging.
In summary, we formulate the following hypothesis that explains the correlation between default sentiment and CDS spreads via the market liquidity channel:
H2 : Increases in default sentiment lead to higher CDS market liquidity and thus lower CDS
spreads (market liquidity hypothesis).
We test both hypotheses by using proxies of default sentiment based on internet search volume
data.
2.2 Measuring sentiment
Measuring investor sentiment in financial markets has been a relatively challenging mission
to accomplish so far. Many studies propose the use of aggregated informationen and opinions
on the market from written publication such as newsletters, column articles, or financial forums’
message boards (see, e.g., Tetlock, 2007; Tetlock et al., 2008; Antweiler and Frank, 2004) and
define implicit measures that reflect the general market sentiment.
6
Other approaches include
surveying, analyzing trading activity, extreme returns, and measuring valuation errors where prices
deviate from rational pricing models (see, e.g., Barber and Odean, 2008; Han, 2008). In this paper,
we make use of data on internet search volume in the United States to study the impact of household
sentiment on prices in the credit default swap market. More precise, we employ information on
the time evolution of specific search queries by internet users related to credit risk and the default
6
The effect of news coverage is an important factor for financial markets. For example Dang et al. (forthcoming)
use a huge database on firm news around the world to explain co-movements in stock returns and stock liquidity.
8
of corporations. Also, in our robustness checks, we control for the effect of individual investor
attention to underlying equities and market-wide household sentiment on CDS spreads.
The use of search volume data yields several advantages over other common methods to derive
measures of investor sentiment. For example, data on internet search volume is available on a daily
and weekly basis and is easily accessible. This would not be viable with, e.g., survey data, which
is hard to obtain and not scalable and thus, is limited to a fraction of the population. 7 Also, search
volume data is especially insightful for the population in the U.S. because of its high percentage of
active internet users.8
In addition to this, the relative search volume on specific phrases is an indicator of interest for
a broader audience and represents the active information retrieval of households. Measures based
on written publications (that are, e.g., derived by using linguistic techniques (see Tetlock et al.,
2008) reflect rather passive information procurement of investors. While such proxies for investor
sentiment may be able to capture market sentiment, as perceived by the authors of the texts, it is
flawed in that way that there is no information on the actual interest and attention of investors to
such outlets. With search volume data, however, we are able to infer the importance of specific
topics to households and investors.
The usefulness of such data to detect trends and forecast economic indicators has been well
documented in the recent literature. One of the first successful applications of internet search volume data from the Google Trends analytics tool can be found in Ginsberg et al. (2009) who predict
influenza epidemics. Choi and Varian (2011) also provide possible applications when analyzing
internet search volume data. In the finance literature, the most prominent and most related studies
are given in Da et al. (2011, 2015). 9 Google Trends lets its user type in search phrases and returns
information on the time evolution of search phrases for a given time period, relative to the number
7
Also note that survey data may contain biased information, which is not the case with anonymous search behavior
of internet users (see Singer, 2002).
8
As of 2013, over 82% of the population in the United States use the internet for informational use (see
http://data.worldbank.org/indicator/IT.NET.USER.P2). From that percentage of the population, around 72%
use Google’s search engine as their primary source of information (see Hitwise, 2009).
9
For other examples see, e.g., Kristoufek (2013); Preis et al. (2013); Hamid and Heiden (2014), or Ding and Hou
(2013), who illustrate the usefulness of Google Trends when studying portfolio selection, volatility, Value at Risk
forecasting, and stock liquidity.
9
of all searches with Google’s search engine.
10
Further, we are able to restrict the output to cer-
tain geographic areas, in our case the United States. In our empirical study, we employ the Google
Trends analytics tool to proxy for the sentiment of households concerning increases in market-wide
average default risk.
3 Empirical strategy
In this section, we introduce our main variables of interest and give some motivation on their
relation to credit default swap spreads. Specifically, we construct a measure of default sentiment
and related variables based on internet search volume data obtained from Google Trends. Afterwards, we define and explain known determinants of CDS spreads from the previous literature.
Finally, we present our econometric design to test our main hypotheses.
3.1 Default sentiment index
We intend to capture the specific sentiment of households to the credit and lending business and
the default of corporations. When measuring the attention to a narrow topic with internet search
volume data the question arises which set of search phrases is most appropriate. To accomplish
this task, we build a fundamental set of search phrases and bi-grams that reflect the circumstance
of a firm or credit to default. This approach yields more focused information on internet users’
motivation to search for specific themes rather than general phrases like “recession”, which are
hard to interpret. In order to decide for the most suitable search terms, and to find additional ones,
we use Google Trends and its “related search terms” feature to see which phrases are actually
searched for and to what extent. Our goal is to create a market-level index that reflects the sentiment
of households and creditors towards failures or defaults of corporations and the credit business. We
do this by combining similar expressions for business entities and creditor activities with phrases
describing default, failure, or bankruptcy. As our basis, we follow Da et al. (2015) and make
10
The resulting time-series is then scaled to a maximum of 100.
10
use of the Harvard dictionary and filter all words that have the tags “Econ@” or “ECON” (743
words). First, we screen our initial list for synonyms of the phrase “firm”, such as “business”,
“company”, or “corporation”. Second, we select from the list all terms associated with the credit
and lending business. Finally, we manually search the list for unambiguous negative phrases that
describe the failure of corporation or financial products. However, we only have lists of single
phrases (1-grams) but would also like to receive information on 2-grams or 3-grams. Information
on only “bankruptcy” is too broad and needs to be associated with a meaning, in this case “business
bankruptcy”. Therefore, we perform any combination of the words on each list, such as “corporate
bond” or “corporate bond default”. We add to our list of words every combination that is sensible
(e.g., not “broker accrue” or “accrue dealer”).
Combinations of the synonyms for firm or financial products are typed into Google Trends and
compared using the following system: For each default word, we compare its respective combination with a firm word, e.g., “business bankruptcy and “firm bankruptcy” via Google Trends to gain
insights on which word is actually used. This makes sense since “business default”, and “company
default”, etc. express the same circumstance of a default only with a synonym for the business
entities. The 2-gram with the highest average search volume between 01/2004 and 09/2010 of a
certain group of words is then chosen. If the search volume of a 2-gram has lower average search
volume than the top phrase but exceeds the search volume of the most searched phrase at least once
over the time period, it is also included in future considerations. Doing so, we can guarantee that
the search volume of a word is relevant enough. If none of the search terms has sufficient search
volume to be shown in the Google Trends tool, the 2-gram is omitted. Similarly, we proceed with
the combinations of our credit words and default words. This approach could suffer from a simple
issue: One could expect that people search for “personal bankruptcy” and so on that do not arise
from the methodology used. Therefore, we look for related search terms, which also include 3grams and more, to mitigate the concern that we omitted relevant combinations. We perform this
comparison for the business and default words and the credit and default words and arrive at our
initial list of search terms.
11
Then, we type into Google Trends every search term and collect every related search terms. We
then proceed by removing all duplicates from our set of search phrases. After that, we download
all search volume data and exclude from our list all terms with insufficient search volume data
(e.g., no or only monthly data). Also, we exclude every related search terms that are clearly not
expressing the expectation of a business or credit-like default, e.g., “motors liquidation company”.
In total, we end up with a list of thirty search phrases that have weekly search volume for the time
period from 01/2004 to 10/2010.
Following Da et al. (2015), we winsorize each weekly time series at the 5% level. To mitigate
concerns about seasonality and heteroskedasticity, we regress the individual time series on month
dummies, take the residual, and then divide by the standard deviation of the time series. Our final
Default Sentiment Index is the average of all thirty time-series.
3.1.1 Retail investor attention
To complement our proxy of default sentiment, we also employ measures of investor attention
in our analysis. In their pioneering study, Da et al. (2011) find that retail investor attention predicts
higher returns and over time leads to price reversals in equity prices. Since it is possible that there
are spillover effects from the equity market to the credit markets (see Hilscher et al., 2015), we
cannot rule out that individual investor attention on specific firms or the presence of noise traders
in the equity market does not affect CDS spreads. Therefore, we control for equity attention by
employing the search volume index (SVI) for stock ticker symbols from Google Trends.
11
We
download all SVI data on stock ticker symbols in our sample for the period from 01/2004 to
09/2010 and restrict the output to the United States. In cases where the ticker symbol may have
ambiguous meaning (e.g., “CAT” or “GPS”) and thus, a unusually high search volume, we exclude
the ticker from our sample. We omit all ticker symbols that yield only monthly or no search volume
data. Then, we calculate abnormal search volume as introduced in Da et al. (2011) by taking the
11
Using stock ticker symbols is less ambiguous than, e.g., a company’s name since internet users might search for
a certain product or the company for other reasons than information on the equity.
12
log differences of SVI and the median SVI over the previous eight weeks.12
3.1.2 Household sentiment - FEARS
To account for sentiment on the household level in our analyses, we use the Financial and Economic Attitudes Revealed by Search (FEARS) index proposed by Da et al. (2015). It is composed
of thirty economics related search phrases such as “gold prices” or “social security card” and reflects the general negative sentiment among the population. The log changes of the respective SVI
values are winsorized at the 5% level and are adjusted for seasonality and possible heteroskedasticity. FEARS is then calculated as the average of the respective thirty time-series.
13
In this regard,
higher values of FEARS indicate a higher level of negative sentiment.
3.2 Additional determinants of CDS spreads
In addition to our proxies of creditor attention and sentiment, we shortly comment on additional variables that should in theory drive default probabilities and thus prices of default-sensitive
securities. All variable definitions and data sources are provided in Appendix I.
The set of potential determinants of CDS spreads can broadly be classified into variables that
are derived from structural models of credit risk and variables that relate to the trading and hedging of CDS contracts. In the first group, theoretical determinants are extracted from Merton’s
1974 classical model of credit risk and subsequent extensions (see, e.g., Longstaff and Schwartz,
1995; Leland and Toft, 1996; Duffie and Lando, 2000). All of these models center around a firm’s
leverage as the key determinant of a firm’s probability to default. Consequently, leverage (or equivalently firm value) should be a major driver of CDS spreads with increases in leverage driving up
premia for credit protection via a CDS. Two common problems associated with a firm’s usual
leverage ratio are that it cannot be directly measured at higher frequencies and that does not capture off-balance sheet items. We therefore follow Christie (1982) and Collin-Dufresne et al. (2001)
12
Note that we lose value for the first eight weeks when including this variable in future regression analyses.
Note that we download weekly search volume data for the list of thirty words and adjust the winsorized time
series using month dummies.
13
13
and employ a company’s firm value instead of the leverage ratio and proxy a firm’s change in firm
value by using weekly arithmetic stock returns. To compute our variable Firm value, we use daily
stock prices taken from Datastream.
A second potential determinant of default risk that emanates from most structural models is a
firm’s asset volatility. In these models, an increase in asset volatility leads to an increase in default
risk and should thus be reflected in a higher CDS spread of the respective firm. In our empirical
study, we follow Ericsson et al. (2009) and include a proxy for individual equity volatility. The
volatility is calculated with GARCH(1,1)-models for conditional variances using a rolling window
of 52 weeks.14
In addition to asset volatilities, CDS spreads should also in theory be significantly influenced
by changes in the risk-free rate. As the risk-free rate increases, the firm’s asset value process should
move away from the default barrier leading to lower CDS spreads. We employ the two-year U.S.
Treasury Benchmark yield, as our variable Risk-free interest rate.
The second set of theoretical determinants comprises variables that are only indirectly connected to a firm’s default risk. For example, studies by Tang and Yan (2013, 2014); Bongaerts et al.
(2011); Lesplingart et al. (2012), and Junge and Trolle (2015) all document a premium in CDS
spreads for the contracts’ illiquidity. We account for a possible influence of illiquidity on CDS
spreads in our empirical study by performing separate regressions on bid and ask quotes of CDS
contracts in unreported (successful) robustness checks (see also Ericsson et al., 2009; Meine et al.,
2015b).
In addition to liquidity, CDS spreads could also be influenced by the contract’s joint tail risk
with the CDS market. As Meine et al. (2015b) show in their study for European banks, protection
sellers receive a premium for bearing the risk of extreme upward co-movements in default risk.
While it is not clear that CDS of non-financial firms carry a similar tail risk premium, we neverthe14
Various studies on the drivers of credit and CDS spreads (see, e.g., Collin-Dufresne et al., 2001; Cremers et al.,
2008; Alexander and Kaeck, 2008; Junge and Trolle, 2015) employ volatilities extracted from individual equity options. Sometimes, values of the VIX implied volatility index to measure the market’s expectation of future stock
market volatility are used as well. Alternatively, one could also have used historical volatilities as it is done, e.g., by
Cremers et al. (2008).
14
less include a variable Tail risk in our regressions that is built by estimating the upper tail dependence between individual CDS spreads and a relevant CDS market index. The CDS tail risk estimates are computed using the Dynamic Asymmetric Copula (DAC) model of Christoffersen et al.
(2012) and Christoffersen et al. (2013) based on the skewed t-copula with both the copula correlations and the degrees of freedom being time-varying.
Complementing these potential drivers of CDS spreads, there is also ample evidence in the
empirical literature (see, e.g., Longstaff et al., 2005; Zhang et al., 2009) that credit risk premia are
sensitive to changes in the business climate in which a firm operates and additional state variables.
We thus include end-of-week values of the S&P 500 index in our regressions to account for general
stock market momentum. We expect increases in the S&P 500 to mirror decreases in overall
default risk as well as increases in recovery rates. Positive index changes should thus be negatively
correlated with CDS spreads in our sample.
Finally, we also employ the slope of the yield curve as a further explanatory variable in our
regression analyses. Here, we use the yield curve slope as an indicator for the country-wide future
economic activity of a bank’s home country. Our variable Slope is defined as a country’s respective
10-year minus 2-year government bond benchmark yields. Data on government bond yields are
taken from Datastream. In theory, spot rates converge to their long-term counterparts, thereby
increasing the risk-neutral drift of the asset value process making default less likely to occur (see
Longstaff et al., 2005). Nevertheless, also monetary policy measures may be reflected in the slope
coefficient. Hence, we expect the direction of the effect of the yield curve slope on CDS spreads
to be unrestricted.
3.3 Econometric design
We now briefly present the empirical approach to test our hypotheses on the relation between
default sentiment and CDS spreads. We first test whether default sentiment drives the time-series
and cross-section of CDS spreads. Therefore, we perform a series of time-series regressions of
CDS spreads on independent variables including our proxy for default sentiment and control vari15
ables such as the three Merton-type variables proposed by structural approaches. For each CDS
contract, we run regressions in first differences of the following type:
ΔCDSt = α + β × ΔDefault Sentimentt + γ × ΔMerton-typet + Θ × ΔControlst + εt ,
where CDSt is the spread of a CDS mid-quote, Default Sentimentt is the sentiment measures
constructed from Google Trends data and Merton-typet and Controlst are various control variables
that have been identified as significant drivers of CDS spreads in the theoretical and empirical literature (see Section 3.2). We calculate corresponding t-statistics as outlined in Collin-Dufresne et al.
(2001) and Ericsson et al. (2009) and estimate all regressions in first differences to remove the
non-stationarity in our time-series (see also Meine et al., 2015a,b).15 Further, we perform panel
regressions in first differences, similar to the regression equation above, that also account for crosssectional effects and clustering on the firm level. In most of our regressions, we include firm-fixed
effects and also employ year dummies to capture unobserved heterogeneity over time. By employing fixed effects, we mitigate the bias that might arise from omitted variables that jointly determine
our measure of default sentiment and other covariates. Further, it is very unlikely that overall levels
of search volume on generic phrases such as “loan default” are driven by CDS market participants,
e.g., as a result from downward CDS market trends.16 We therefore believe that our regressions of
(differences in) CDS spreads on default sentiment do not suffer from reverse causality.
To better understand the channel through which households’ sentiment might affect CDS
prices, and therefore test the hypothesis that sentiment increases CDS market liquidity, we also
estimate additional regressions in which we include several measures of individual or market-level
CDS liquidity to test whether default sentiment is in fact partially replacable by liquidity effects
or whether it accounts for unobserved effects that determine CDS spreads (credit risk hypothesis).
15
As Ericsson et al. (2009) point out, it is often harder to explain differences than levels, which is why we opt for
this specification.
16
For example, comparing the search volume on the phrase “loan default” with a more frequent word such as
“entrepreneurship” reveals that a relatively high search frequency is needed to generate the displayed relation.
16
Similar to our other regressions, we run panel regressions in first differences of the following form:
ΔCDSit = αi + μt + β × ΔDefault Sentimentt + γ × ΔLiquidityit + Θ × ΔControlsit + εit ,
where αi and μt are firm-fixed effects and year dummies, respectively.17 In further analyses, we
also include interaction terms between default sentiment and our liquidity proxies.
4 Empirical analysis
In this section, we discuss the central features of our data sample and present our empirical
results.
4.1 Sample construction and descriptive statistics
Our data set consists of all single-name CDS time series for U.S. firms that have a traded CDS
contract on their debt available in the Credit Market Analysis (CMA) database via Thomson Reuters
Financial Datastream. 18 For these firms, we download CDS data with available daily mid, bid, and
ask quotes. We use CDS contracts with a maturity of five years, as these are considered the most
liquid, and those that refer to senior-debt issues denominated in U.S. dollar.
We start with the full set of U.S. firms that have a traded CDS contract and are listed on an U.S.
stock market.19 Afterwards, we match the CDS symbols from Thomson Reuters with the respective
equity symbol to obtain stock price data later on. 20 Our final sample consists of 228 CDS contracts
from 19 different industries over 351 weeks which leaves us with a maximum of 80,028 firmweek observations. For increased transparency, all firm names and their ticker symbols are listed
in Appendix IV. Finally, for each firm i in our sample, we then calculate a corresponding CDS
17
In our set of robustness checks, we also include more granular time effects such as week dummies.
The CMA database has several advantages over other providers such as reliability and reporting of only sufficiently often quoted CDS contracts (see Mayordomo et al., 2014).
19
We further exclude U.S. sovereign debt issues.
20
We follow Meine et al. (2015b) and extract from each CDS identifier the firm name and add a prefix (“U:”) to
get the identifier of the firm’s equity. Whenever we find an ambiguous match or do not find significant variation in the
time series of CDS quotes, we delete it from our sample.
18
17
market index by equally-weighting all individual CDS contracts in our sample excluding the CDS
of firm i.
Appendix I shows data sources, definitions of variables used in this study, and respective descriptive statistics. Average CDS spreads for the full sample period are 152.44 basis points (bps
hereafter). The minimum is 1 bps and the maximum is 12,577 bps indicating that the sample is
characterized by strong cross-sectional variation. Mean stock returns are around 0.19% and the
range is from -84.7% and +295.12%. Looking at the spread of the bid- and ask-quotes also reveals
a wide range between the minimum and maximum value and a high standard deviation of 31.26
bps. The market’s bid-ask-spreads, however, are slightly narrower.
We now present empirical evidence on the interplay of our variables of interest. Figure 1 shows
the time evolution of CDS spreads, CDS bid-ask-spreads, and the Default Sentiment Index (DSI)
from 2004 to Q3 2010. The grey-shaded areas present the 20%- and 80%-quantile of CDS spreads
(upper panel) and CDS bid-ask-spreads (lower panel).
Insert Figure 1 about here.
We observe that the mean CDS spread is very close to the 80% quantile almost all the time, which
hints at the presence of few firms with extremely high spreads in our sample. The evolution of
CDS spreads is relatively flat until the beginning of 2008, when mean values spike at around 200
bps. In the end of 2008 and beginning of 2009, however, CDS spreads are even higher with 80%
quantiles being above 600 bps before dropping to an average of 200 bps after the crisis.
The mean bid-ask-spread of CDS contracts for the full sample is around 11 bps, but the spreads
we observe before the crisis period are constantly below 10 bps indicating a highly liquid CDS
market. In late 2007, the market dried up a little with increasing bid-ask-spreads until 2009, when
we find extremely high mean values of CDS bid-ask-spreads, possibly driven by few outliers.
Additionally, we plot the time evolution of the weekly Default Sentiment Index (DSI). The index starts at relatively high values but has a downward trend until the beginning of 2007. Throughout the pre-crisis period, weekly default sentiment is more volatile while CDS (bid-ask-)spreads
18
are low and stable during this time. Starting in 2007, we observe an upward trend of the index
during the crisis, but it reaches its maximum after the crisis in 2010, when bid-ask-spreads and
CDS premia were low again.
4.2 Does default sentiment drive CDS spreads?
In this section, we present our benchmark results from time-series and panel regressions in first
differences of CDS spreads and control variables to see whether default sentiment significantly
drives CDS premia.
4.2.1 Time-series regressions
The results from the time-series regressions are given in Table I.
Insert Table I about here.
First, we run time-series regressions for every firm including the first differences of firm value,
interest rate, and equity volatility as explanatory variables. These are the three determinants proposed by the structural approach derived from the work of Merton (1974). The average coefficients
are reported in columns (1) to (3). Here, higher firm value of the reference entity is associated with
a lower spread of its credit default swap. This is the opposite of what is suggested by theoretical
models and found, e.g., by Ericsson et al. (2009). The difference in our study is the weekly frequency of the data as opposed to monthly values in Ericsson et al. (2009). However, we find that
the coefficient of firm value remains negative in all of our regressions.21 Similarly, the average coefficient of the interest rate variable is highly significant with a negative sign. Higher asset (equity)
volatility, on the other hand, is significantly, positively correlated with CDS premia, which confirms results found in previous studies as well (see, e.g., Ericsson et al., 2009; Meine et al., 2015b).
Column (4) shows results from regressions including all three variables at once and confirms our
21
This incompability with theory is also found in Meine et al. (2015b) and Meine et al. (2015a) using quarterly
data.
19
previous results. However, the average adjusted R 2 is only 8%, which underlines the limitation of
the structural approach to explain CDS spreads in a comprehensive way.
In column (5) and (6), we include in our regressions the proxy for default sentiment as our
only explanatory variable and along with the Merton-type variables, respectively. The resulting
coefficient in (5) is -1.069 and statistically significant at the 1% level. According to these timeseries regressions, our proxy for default sentiment is negatively related to CDS premia and enters
the analysis with a highly significant coefficient. As sentiment is not supposed to influence a market
dominated by institutional traders, we ask the question whether the significance of our sentiment
measure is due to confounding effects caused by the absence of variables that proxy for the general
business climate in capital markets. To shed more light on the predictive power of the DSI, we thus
include our macroeconomic variables introduced in Section 3.2. Higher values of option implied
market volatility (VIX) are associated with higher CDS premia, which is similar to the effect of
the equity volatility of the reference entity. Better business climate, represented by higher values
of the S&P 500 benchmark index, and a higher slope of the yield curve are negatively related to
CDS spreads with the coefficients being statistically significant at the 1% level. Including these
variables in columns (8) and (9) weakens the statistical significance of the DSI slightly but does
not alter the result found in previous regressions. Interestingly, the average adjusted R 2 increase
from 8% to around 15% and 18%, respectively, indicating that these macroeconomic controls
posses considerable explanatory power. In column (10), we report the coefficients from timeseries regressions involving the CDS tail beta of the individual default swap. The positive relation
with CDS spreads is also found in Meine et al. (2015b) and the influence of DSI is qualitatively
the same. In summary, our sentiment proxy remains a significant driver of CDS spreads in all
regression specifications.
4.2.2 Panel regressions
Next, we turn to panel regressions of first differences in credit default swap spreads on first
differences in default sentiment and control variables. This time, we are able to include firm-fixed
20
effects (firm FE) and year dummies in our model to account for unobserved heterogeneity across
firms and time. The results are shown in Table II with t-statistics corrected for clustering at the
firm level.
Insert Table II about here.
As before, including the variables firm value and interest rate reveals the same negative and significant relation to CDS spreads as in the time-series regressions. The third Merton-type control variable, equity volatility, however, enters the regressions with an insignificant coefficient, although it
is positive as predicted. Including all three Merton-type variables and the DSI in one regression
does not change our conclusions from the analysis before. Our proxy for default sentiment significantly lowers CDS spreads. However, we notice that the coefficient of the DSI in our panel
regressions does not drop as much as in the time-series regressions when we include additional
variables in our setup.
We repeat the last four regressions from Table I and report the findings of the panel regressions
in column (6) to (9). The additional control variables are, again, highly statistically significant with
the respective coefficient carrying the expected sign. Most notably, our proxy of default sentiment
remains highly relevant in all of the regressions.
Concluding these first analyses, we notice a highly significant negative relation between CDS
spreads and the default sentiment index based on internet search volume data. This index represents the procurement by households in the U.S. of information on bankruptcy and credit related
topics and thus, indicates worries concerning rises in default risk. Naturally, one would not expect
that household sentiment could spill over to credit markets, which are dominated by institutional
investors rather than individual traders and thus, prices should not be affected by it. The fact that
we find such highly significant correlation raises the question on the possible channels through
which the market-wide sentiment could influence the prices in the CDS market.
One of these possible channels identified in the theoretical framework in section 2 is summarized in our market liquidity hypothesis, which explains the reduction in CDS spreads when default
sentiment is high. We further explore this hypothesis in the next section.
21
4.3 Default sentiment and the market liquidity hypothesis
Our next set of regressions analyzes the interplay of CDS prices and (market) liquidity in
the credit default swap market. If the channel through which default sentiment influences CDS
spreads is the CDS market’s liquidity, we expect that the DSI and liquidity are interchangeable
when explaining CDS premia. To test the market liquidity hypothesis, we include in our regressions
several variables that account for liquidity effects in the CDS market along with our measure of
default sentiment and other controls.
The liquidity proxies include a contracts Bid-Ask-Spread BASabs , which is the end-of-week
absolute CDS bid-ask spread, calculated as daily CDS ask quote minus CDS bid price and denoted
in bps, the Sector Bid-Ask-Spread BASIabs calculated as the cross-sectional average of absolute
bid-ask spreads of all firms within an industry, and the Market Bid-Ask-Spread BAS M
abs calculated
as the cross-sectional average of relative bid-ask spreads of all sample firms excluding the respective firm. CDSILLIQ is the end-of-week value of the CDS market illiquidity measure proposed
by Junge and Trolle (2015). Table III shows results from panel regressions that include first differences of the three Merton-type variables, the default sentiment proxy, and one of the liquidity
proxies as explanatory variables at a time.
Insert Table III about here.
First, we employ the individual bid-ask-spread of a CDS contract along with the DSI. The former
of the two variables has a statistically significant positive coefficient in our regression while the
latter enters the regression with a negative sign. Different from our previous analysis, we find that
the DSI is only significant at the 10% level indicating that the individual (il)liquidity of a CDS
contract partially replaces the explanatory power of default sentiment. 22
Next, we use the cross-sectional average of (absolute) bid-ask spreads within an industry and
include it along with default sentiment in our regression. Using the sector bid-ask-spread, we are
able to see whether liquidity effects on CDS spreads that are specific to a certain industry interact
22
The positive relation of illiquidity and CDS spreads is also documented by, e.g., Tang and Yan (2014).
22
with the DSI. Similar to the individual bid-ask-spread, we find a significant and positive relation to
CDS premia. The relevance of default sentiment, however, is not reduced in this case.
Including both the CDS market bid-ask-spread and default sentiment in our regressions paints
an interesting picture of the relation between the two variables: While market illiquidity and CDS
spreads have a strong positive relation with a statistically significant coefficent at the 1% level,
the significance of default sentiment vanishes. This supports the rationale of the market liquidity
hypothesis with sentiment appearing to drive aggregate CDS market liquidity. The presence of
high default sentiment could be associated with a more liquid CDS market and thus, reduces the
average spread of CDS contracts as liquidity commonalities (see Meine et al., 2015a) and liquidity
risk (see Junge and Trolle, 2015) directly impact CDS spreads. Using the end-of-week value of
CDSILLIQ immensely reduces our sample size compared to the other three models. The results
are nevertheless similar: liquidity and CDS spreads and default sentiment and CDS spreads are
negatively related at a very high significance level.
For the three Merton-type variables, we find the exact same signs for the coefficients as in
our baseline panel regressions. Again, firm value and interest rate are highly significant while
volatility is not relevant for explaining CDS spreads in our sample. We repeat these four analyses
including year dummies to capture time effects. However, the results reported in column (5) to (8)
are qualitatively and quantitatively very similar to the results from the regressions in (1) to (4).
4.4 Exploring the market liquidity hypothesis
In our regressions including the proxies for liquidity in the CDS market, we notice that the
Default Sentiment Index may be partially replacable by these variables when explaining CDS premia. As our next step, we analyze the dynamics of the relation between CDS (market) liquidity
and default sentiment. First, we filter out the sentiment-caused component from the liquidity proxies by running time-series regressions (for each firm) in first differences of the original liquidity
I
variables BASabs , BASM
abs , and BASabs on first differences of the Default Sentiment Index. From
each regressions, we extract the respective residual and include these along with the DSI in further
23
panel regressions. The results of these analyses are given in Table IV.
Insert Table IV about here.
The residuals of the liquidity measures represent the portion of liquidity effects that are not explained by default sentiment from outside of the credit default swap market.
Including only the liquidity residuals and default sentiment along with fixed effects and year
dummies yields the following result: the DSI coefficient enters all regressions with a negative sign
with the coefficients being significant at the 1% level. Thus, the effect induced by default sentiment
remains relevant for the pricing of credit default swaps. The effect of (il)liquidity residuals on CDS
spreads is also highly statistically significant with the well known positive sign of the coefficients.
We observe that the change in the CDS market bid-ask-spread has the strongest effect on CDS
premia with a coefficient of 2.326.
If CDS liquidity and default sentiment were completely unrelated (which is what we would
expect intuitively), the significance of our default sentiment variable would remain an unsolved
puzzle. However, we do find that there is a strong correlation between household level default
sentiment and aggregate liquidity in the CDS market.
Assuming that DSI indeed drives CDS market liquidity, 23 we conclude that even if we remove
the sentiment from the bid-ask-spreads by taking the residuals, we find a significant relation to
CDS spreads. This is an indicator for the fact that CDS market liquidity is not entirely driven by
sentiment measures but may be influenced by it in certain times.
We conclude that liquidity may influence CDS prices on its own, but the magnitude may be
higher in times of higher default sentiment. These observations motivate a more detailed analysis
into the interaction of default sentiment with our liquidity proxies. Table V shows panel regressions
(in first differences) of CDS spreads on interaction terms of CDS liquidity proxies and the default
sentiment variable.
Insert Table V about here.
23
It is very unlikely that such sentiment is created from institutional investors alone, since it is necessary to generate
a high search volume for these rather generic terms to be displayed by Google Trends. Also, we compare our search
phrases to very general word combinations to assess the magnitude of the respective search volume.
24
We interact first differences of default sentiment and individual or market bid-ask-spreads and
include these along with the two variables in respective regression models. Most strikingly, we
observe that the DSI is insignificant in the absence of market illiquidity. Neither of the coefficients
of DSI in model (1) to (6) shows any relevant statistical significance.
The main effects of individual and market bid-ask-spreads, however, are highly statistically significant on the 1% level when explaining CDS premia. When there is zero sentiment, illiquidity of
CDS contracts will still increase their premia, which is in line with the findings of Bongaerts et al.
(2011); Junge and Trolle (2015). This also complements our findings of the analyses including the
residuals of the regressions of bid-ask-spreads on default sentiment.
We find that all coefficients of interaction terms enter the regressions with a negative sign that
is statistically significant at least at the 10% level. Obviously, when default sentiment is high, the
effect of the bid-ask-spreads on CDS premia is reduced. In terms of liquidity, we observe that
default sentiment increases the effect of liquidity (since it reduces the effect of illiquidity) and thus
find support for our hypothesis on the channel of the effects of such sentiment on CDS prices. This
relation is even more pronounced for the market bid-ask-spreads and the DSI.
4.5 Are some CDS contracts more sensitive to default sentiment than others?
Looking at the significant interaction of default sentiment and CDS liquidity, we investigate
whether the sensitivity of the market participants towards default sentiment plays a role in determining CDS prices. We argue that when firms are more sensitive to such specific sentiment, it
should be reflected in their CDS prices. As a standard measure of sensitivity or systematic risk, we
employ the betas and R2 s of the following regressions:
BASit = α + βDSI × Default Sentimentt + εit ,
25
where BASabs is the end-of-week Bid-Ask-Spread of a CDS contract. Figure 2 shows plots of the
resulting average R2 s for each industry sector in our sample.24
Insert Figure 2 about here.
In total, we have 18 average R2 s for different sectors with at least four firms.25 In most cases, the
average R2 is highest in the crisis years 2008 and 2009. However, we find several differences in the
sensitivity of bid-ask-spreads to default sentiment across sectors. Industrial goods and services is
the sector with the most firms in our sample. Its mean R 2 ranges from approximately 2% in 2004
to around 7.5% in 2006 before the crisis. The maximum value is about 15% in 2009. Similar
patterns can be found in the technology, chemicals, retail and construction and material sector.
Compared to other sectors, insurance companies’ R 2 s from regressions of CDS bid-askspreads on default sentiment appear to be less volatile over time. Values in the three years before
the crisis are slightly above 5% and peak around 15% in 2008 before dropping to pre-crisis levels in
2009. One interpretation is that parts of the insurance sector have been involved more heavily with
the credit markets than other sectors and thus, could be more exposed to default sentiment. Interestingly, we find a different pattern in the healthcare sector, where the highest influence of default
sentiment on CDS illiquidity can be found in 2006. CDS contracts on reference entities in the sectors basic resources, personal and household goods, and automobile and parts are influenced by
default sentiment during the two crisis years with R 2 s surpassing values of 15%. Goodness-of-fit
is very low in 2007 and similar to other sectors in 2006 with values of about 10%.
Finally, we turn to the firms within the financial services, real estate, and banking sector. These
types of firms are expected to suffer the most from credit and business defaults and thus, should
be particularly prone to default sentiment. What we observe in the average R 2 s of the 17 firms
in the real estate sector is a monotonic increase in the ability of default sentiment to explain CDS
illiquidity starting at 2.5% and reaching 7% in 2007. In 2008 and 2009 we see a jump in these
numbers to 15% and 17.5%, respectively, which are among the highest values across all sectors.
We calculate the R2 for each year from 2004 to 2010 (only up to Q4 in 2010 due to data availability) and every
issuer in our sample.
25
The telecommunications is excluded as only one firm in our sample is from that sector.
24
26
This is only surpassed by the value of 20% in 2008 in the banking sector, which may be explained
by the fact that the crisis originated in that sector and thus, credit defaults were associated with
banks more heavily than with other firms. However, we only find such a high value in 2008, while
in all the other years, bank CDS bid-ask-spreads were not as affected by default sentiment as other
sectors. The pattern for the financial services sector is very similar to the real estate firms, but on
a slightly lower level.
We conclude that the impact of default sentiment based on search volume of credit related
phrases has different effects across industries and may also vary over time. Next, we take a closer
look at average CDS spreads for firms that are most sensitive to changes in market-level values
of liquidity and default sentiment. To determine the sensitivity of each CDS contract, we perform
regressions of the following type:
i
× MLIQt + εit ,
BASit = α + βLIQ-MLIQ
i
× DSIt + εit ,
BASit = α + βLIQ-DSI
i
× MCDSt + εit ,
BASit = α + βLIQ-MCDS
where MLIQt is the average value of all individual bid-ask-spreads (market illiquidity), DSI t is the
Default Sentiment Index, and MCDSt is the average CDS spreads in week t. For each reference
i
i
, βLIQ-DSI
, and
entity i, we run simple OLS regressions to obtain the coefficient estimates β LIQ-MLIQ
i
, which represent the systematic risk stemming from the sensitivity of an individual bidβLIQ-MCDS
ask-spread to market illiquidity, default sentiment, and average CDS spreads.
Afterwards, we split our sample into quintiles of these betas and calculate the weekly timeseries of average CDS spreads for each quintile. In Figure 3, we plot the time evolution of the
results.
Insert Figure 3 about here.
Comparing the plots for all three betas, we notice that they paint an almost identical picture. Indeed, the pairwise correlations of the beta vectors are about 98% and 99%. This leaves us with
27
the conclusion that the same group of firms that are sensitive to market illiquidity in their bid-askspread, are also sensitive to changes in default sentiment and average market CDS spreads. We
also see how the level of CDS spreads differs across the different quintiles of the betas introduced
above.
Generally, the higher the beta for a firm, the higher are the average CDS spreads. The difference
across the first four quintiles of betas is only marginal compared to the fifth quintile. CDS contracts
with the highest sensitivity of bid-ask-spreads to market-level values of illiquidity, sentiment, and
CDS spreads have much higher average CDS spreads. The CDS spreads are relatively constant
from 2004 to 2007 and increase during 2008. Extremely high peaks of CDS spreads can be found
shortly before and after 2009, in the midst of the financial crisis.
In a nutshell, we find that systematic risk stemming from default sentiment has very similar effects to systematic liquidity risk that has been identified in the literature before (see
Bongaerts et al., 2011; Tang and Yan, 2014; Junge and Trolle, 2015).
4.6 Credit risk hypothesis and equity market spillover
The evidence found so far speaks in favor of the market liquidity hypothesis, i.e., default sentiment from outside the CDS market causes inattentive traders in the CDS to overreact therby
increasing CDS market liquidity and reducing CDS spreads. However, we can not let go of the fact
that the DSI most likely captures more than just CDS market liquidity. Therefore, we test whether
the fraction of default sentiment that does not account for liquidity effects is able to, at least in
parts, support the validity of the credit risk hypothesis as well. To do so, we obtain residuals from
simple OLS regressions of the DSI on individual, sector-wide, and rather market-level bid-askspreads (in first differences). We employ these residuals as regressors in our panel regressions
along with the respective liquidity proxy. Results are shown in Table VI.
Insert Table VI about here.
Columns (1) to (3) show estimation results that only include the liquidity proxy, the residuals, and
28
firm-fixed effects and year dummies whereas models (4) to (6) also employ the various control
variables from previous analyses.
First of all, we find a strong, positive relation of bid-ask-spreads and CDS spreads on both the
individual and market-wide level. This is underlined by the significance level of 1% in almost each
of the regressions. Looking at the residuals from the regressions of DSI on individual bid-askspreads, we observe that it is negatively related to CDS spreads and slightly statistically significant
on the 10% level.
We find that the fraction of default sentiment that does not account for liquidity effects actually
reduces average CDS spreads as well. If this part of the DSI was to increase CDS spreads, it could
have supported a weaker form of the credit risk hypothesis. However, we find that this hypothesis
is rejected again by our regression results. Testing the robustness of this significance by including
our set of known determinants of CDS spreads leads to the vanishing of this slight significance.
A qualitatively and quantitatively similar result is obtained when we employ the sector bid-askspreads. Although we do find the same negative relation of the DSI and market bid-ask-spread
residual, we do not find any significance, which again supports the rejection of the credit risk
hypothesis in this setup.
Da et al. (2011, 2015) have shown that prices in equity markets are prone to noise trading and
household-level sentiment using measures of retail investor attention based on Google Trends data
(see section 3.1.1 and 3.1.2). If such proxies of investor sentiment have a profound influence on
equity markets, this could, in theory, spill over to other markets as well. This leading relation of
movements in equity markets on CDS markets has recently been documented by Hilscher et al.
(2015). We therefore investigate the question, whether default sentiment may influence the CDS
markets via those spill overs from equities. Moreover, we test whether the known sentiment and
attention measures also affect CDS prices in the way they drive stock prices. Our motivation to
choose the set of phrases was to capture the information demand of creditors and households on
credit related topics. We argue that such behavior is not simply captured by obtaining search
volume on single words, which might be out of context. By combining several words we are able
29
to bring specific meaning to the searches of internet users. 26 To test the validity of our constructed
measure, we include the FEARS index introduced in Da et al. (2015), which is based on single
generic search terms that might have a negative connotation.
In Table VII, we show results from panel regressions including the abnormal search volume on
a reference entity’s stock ticker symbol as introduced in Da et al. (2011) (Panel A) and the FEARS
index from Da et al. (2015) (Panel B).
Insert Table VII about here.
In column (1), we include the abnormal search volume on ticker symbols (ASVI), the DSI, and our
control variables in the regression model. The individual investor attention is positively related to
CDS spreads. One could imagine that stocks that gain higher attention by retail investors, are the
ones that experience more negative news coverage. Moreover, attention to individual ticker symbols is more likely to reflect actual changes in fundamentals in contrast to indiscriminate searches
on default risk. Consequently, attention to individual reference entities appears to reflect (perceived or actual) increases in credit risk and thus increases in CDS spreads.27 More importantly,
however, the estimate for the DSI coefficient remains negative and significant at the 10% level in
this regression.
Next, we also employ an interaction term of DSI and ASVI that accounts for the interplay
between default sentiment and individual retail investor attention. This variable, however, is insignificant in all of the regressions in Panel A and indicates that the two main effects are isolated.
In the other three models, (3), (4), and (5), we use the three bid-ask-spreads along with the other
covariates. As in our main results, the default sentiment still has a negative influence on the level
of CDS spreads, but is weakened and not significant at the usual statistical levels thereby again
confirming the market liquidity hypothesis. The effect of ASVI on CDS premia remains positive
and significant, which indicates only little interaction of (market) liquidity and investor attention,
26
For example, a search on “credit” could represent a variety of incentives whereas “credit default” provides a more
nuanced interpretation.
27
Investor attention could be directed at a firm’s CDS or its stock, in which case the effect of investor attention
could still spill over to the CDS market (see Hilscher et al., 2015).
30
compared to the strong relation between default sentiment and liquidity.
We now turn to Panel B of Table VII in which we show regression results using the FEARS
index. Higher values of FEARS indicate more negative sentiment among households and expresse
their negative expectations of the general outlook on the economy. FEARS represents economic
related views of the population but is not tied to specific themes that are necessarily close to credit
markets. First, we notice that DSI is again significant with a negative sign but the effect is weaker
when we include our liquidity variables. The FEARS index, however, is strongly positively related
to CDS spreads. Higher negative sentiment in the overall economic household environment is
associated with higher average CDS permia. This relation could, again, arise from the documented
spillover effects from the equity markets and also funds, which are influenced by the household
sentiment measure (see Da et al., 2015; Hilscher et al., 2015).
5 Conclusion
In this paper, we show that sentiment affects the cross-section and time-series of CDS spreads.
We employ internet search volume data to proxy for an exogenous shock to the pessimistic sentiment of households about credit risk at the market level. As our first result, we document the existence of significant noise trading in the CDS market as we find our proxy of household sentiment
to be a statistically significant and economically important driver of CDS spreads. In particular,
CDS spreads and sentiment are negatively correlated with buyers of credit protection earning a
premium when market-wide sentiment is higher.
After carefully testing the robustness of this finding, we test and confirm the hypothesis that
sentiment affects CDS spreads via the market liquidity channel. Consistent with theoretical models
that view spikes in market liquidity as an indicator of the presence of noise traders in the market, we
find higher household sentiment concerning increases in default risk to be significantly positively
correlated with changes in CDS market liquidity. Through liquidity commonalities and liquidity
risk, the higher CDS market liquidity then translates into lower CDS spreads. The results we find
31
thus contribute significantly to the current discussion of the unclear role liquidity risk plays in the
pricing of CDS contracts.
32
Appendix
33
34
0.02
11.25
11.28
11.25
ASVI
BASabs
BASIabs
BASM
abs
27.95
Volatility
-1.08
(×10−3 )
2.79
(Risk-free) Interest rate
FEARS
0.19
Firm value
-0.02
(×10−9 )
152.55
CDS
DSI
Mean
Variable
8.77
16.97
31.26
0.58
0.53
1.00
35.52
1.55
6.14
342.97
St. Dev.
4.11
1.00
0.00
-4.42
-2.31
-2.12
0.08
0.42
-84.7
1.00
Min.
78.91
987.80
3,201.00
4.62
1.96
2.15
2,369.14
5.25
295.12
12,577.58
Max.
80,028
79,677
80,020
62,192
351
351
79,264
351
79,570
80,020
Obs.
Cross-sectional average of relative bid-ask spreads of all sample firms. Averages
are calculated excluding the current firm and are reported in bps.
Cross-sectional average of absolute bid-ask spreads of all firms within an industry. ICB supersector industry classifications are obtained from DS. Averages are
calculated excluding the current firm and are reported in bps.
End-of-week absolute CDS bid-ask spread, calculated as daily CDS ask quote
minus CDS bid price and denoted in bps.
Weekly abnormal search volume (SVI) of a firm’s ticker symbol in Google
Trends. log [GSVIt ] − log [median(GSVIt−1 , . . . , GSVIt−8 )].
Weekly FEARS-index introduced in Da et al. (2015). FEARS is the average of
the log changes in search volume of thirty economics related phrases, adjusted
for seasonality and heteroskedasticity.
The weekly DSI is the average of the Google Search Volume Indices (GSVI)
for thirty default-related search phrases (see AppendixIII), winzorized at the 5%
level and adjusted for heteroskedasticity and seasonality.
Weekly equity return volatility, denoted in %. Volatility is calculated with
GARCH(1,1)-models for conditional variances using a rolling window of 52
weeks.
End-of-week two-year U.S. Treasury Benchmark yield, denoted in %.
Weekly return on a firm’s equity, denoted in %.
End-of-week CDS mid quote (in levels), denoted in basis points (bps).
Definition
CMA, EST
CMA, EST
CMA
GT, EST
GT, EST
GT, EST
DS, EST
DS
DS, EST
CMA
Source
The table presents summary statistics, definitions, and data sources for all dependent and independent variables that are used in our empirical study.
The data sources are Credit Market Analysis (CMA), Thomson Reuters Datastream (DS), and Google Trends (GT). EST indicates that the variable is
estimated or computed based on data from the respective data source(s). Our sample encompasses 228 financial and non-financial companies for the
period from January 2004 to September 2010.
Appendix I: Variable definitions, descriptive statistics, and data sources.
35
20.72
1205.47
0.12
1.25
S&P 500
CDS tail beta
Slope
9.10
CDSILLIQ
VIX
Mean
Variable
1.01
0.11
181.63
11.17
13.49
St. Dev.
-0.19
0.00
712.87
9.89
0.76
Min.
2.86
0.77
1562.47
74.26
78.58
Max.
351
79,787
351
351
211
Obs.
Weekly U.S. 10-year minus 2-year Treasury Benchmark yields.
Proxy of a CDS contract’s tail risk together with a CDS market index as proposed
by Meine et al. (2015b) and estimated by using the Dynamic Asymmetric Copula
(DAC) model of Christoffersen et al. (2012).
Weekly values of the S&P 500 index.
Weekly values of the Chicago Board Options Exchange Market Volatility Index
measuring the implied volatility of S&P 500 index options.
End-of-week values of the CDSILLIQ measure proposed by Junge and Trolle
(2015).
Definition
DS, EST
DS, EST
DS
DS
http://sfi.epfl.ch/trolle
Source
The table presents summary statistics, definitions, and data sources for all dependent and independent variables that are used in our empirical study.
The data sources are Credit Market Analysis (CMA) and Thomson Reuters Datastream (DS). EST indicates that the variable is estimated or computed
based on data from the respective data source(s). Our sample encompasses 228 financial and non-financial companies for the period from January
2004 to September 2010.
Appendix II: Variable definitions, descriptive statistics, and data sources (continued).
Appendix III: Default Sentiment Index word list
after bankruptcy
bank bankruptcy
bank default
bankruptcy and credit
bankruptcy credit cards
bankruptcy credit counseling
bankruptcy credit report
bankruptcy filing
bankruptcy filings
bankruptcy laws
bankruptcy loans
business bankruptcy
company bankruptcy
credit after bankruptcy
credit bankruptcy
credit card bankruptcy
credit card debt
credit card default
credit counseling
credit crunch
36
credit default
debt bankruptcy
debt consolidation
debt settlement
file bankruptcy
filing for bankruptcy
interest default
liquidation sale
loan default
personal bankruptcy
Appendix IV: Sample firms and ticker symbols
Name
Ticker
Name
Ticker
3M COMPANY
ABBOTT LABORATORIES
ACE LTD
ADVANCED MICRO DEVC INC
AES CORP
AETNA INC
AIR PRODUCTS & CHEMS INC
AK STEEL CORP
ALCOA INC
ALLERGAN INC
ALLIANT EN CORP
ALLSTATE CORP
ALTRIA GROUP INC
AMERICAN AXLE & MNFG INC
AMERICAN ELEC PWR CO INC
AMERICAN EXPRESS CO
AMERICAN INTL.GROUP
AMERICAN TOWER CORP
AMERISOURCEBERGEN CORP
ANADARKO PETROLEUM CORP
ANHEUSER-BUSCH COS. INC
APACHE CORP
ARCHER-DANIELS-MIDL. CO
ARROW ELECTRONICS INC
ASHLAND INC
AT&T CORP
AUTOZONE INC
AVALONBAY COMMNS. INC
AVNET INC
AVON PRODUCTS
BAKER HUGHES INC
BANK OF AMERICA CORP
BAXTER INTERNATIONAL INC
BEAZER HOMES USA INC
BERKSHIRE HATH.INC
BEST BUY CO INC
BORGWARNER INC
BOSTON PROPERTIES LP
BOSTON SCIENTIFIC CORP
BRISTOL-MYERS SQUIBB CO
INTERNATIONAL PAPER CO
INTERPUBLIC GP.COS. INC
FEDEX CORP
FELCOR LODGING LP
FIRST INDUSTRIAL LP
FIRSTENERGY CORP
FORD MOTOR CO
GANNETT CO INC
GAP INC
GATX FINANCIAL CORP
GENERAL DYNAMICS CORP
GENERAL MILLS INC
GOLDMAN SACHS GP INC
GOODYEAR TIRE & RUB. CO
DANAHER CORP
DARDEN RESTAURANTS INC
DEAN FOODS CO
DEERE & CO
DELTA AIR LINES INC
DEVELOPERS DIVR.REAL
MMM
ABT
ACE
AMD
AES
AET
APD
AKS
AA
AGN
LNT
ALL
MO
AXL
AEP
AXP
AIG
AMT
ABC
APC
BUD
APA
ADM
ARW
ASH
T
AZO
AVB
AVT
AVP
BHI
BAC
BAX
BZH
BRKA
BBY
BWA
BXP
BSX
BMY
IP
IPG
FDX
FCH
FR
FE
F
GCI
GPS
GMT
GD
GIS
GS
GT
DHR
DRI
DF
DE
DAL
DDR
BRUNSWICK CORP
CABLEVISION SYS CORP
CAMERON INTL CORP
CAMPBELL SOUP CO
CAPITAL ONE BANK
CARDINAL HEALTH INC
CARNIVAL CORP
CATERPILLAR INC
CENTERPOINT ENERGY INC
CHESAPEAKE ENERGY CORP
CHUBB CORP
CIGNA CORP
CITIGROUP INC
CMS ENERGY CORP
COCA-COLA CO
COLGATE-PALMOLIVE CO
COMMERCIAL METALS CO
COMPUTER SCIENCES CORP
CONAGRA FOODS INC
CONOCOPHILLIPS
CONSOLIDATED EDISON INC
COOPER TIRE & RUBBER
CORNING INC
CSX CORP
CUMMINS INC
CVS CAREMARK CORP
CYTEC INDUSTRIES INC
HALLIBURTON CO
HARTFORD FINL.SVS.GP
HEALTH CARE REIT INC
HEALTHCARE REAL.TST. INC
HERSHEY FOODS CORP
HIGHWOODS REALTY LP
HOME DEPOT INC
HONEYWELL INTL.INC
HOSPITALITY PROPS. TST
HUMANA INC
INTL.BUS.MCHS.CORP
ILLINOIS TOOL WORKS INC
INTERNATIONAL GAME TECH
PFIZER INC
PITNEY BOWES INC
POLYONE CORP
PPG INDUSTRIES INC
PRAXAIR INC
PROCTER & GAMBLE CO
PROLOGIS TRUST
PULTE HOMES INC
NABORS INDUSTRIES INC
NAVISTAR INTL.CORP
NEWELL RUBBERMAID INC
NEWMONT MINING CORP
NIKE INCO
NORDSTROM INC
NORFOLK SOUTHERN CORP
NORTHEAST UTILITIES
NORTHROP GRUMMAN CORP
NUCOR CORP
NVR INCORPORATED
OCCIDENTAL PETROLEUM CORP
BC
CVC
CAM
CPB
COF
CAH
CCL
CAT
CNP
CHK
CB
CI
C
CMS
KO
CL
CMC
CSC
CAG
COP
ED
CTB
GLW
CSX
CMI
CVS
CYT
HAL
HIG
HCN
HR
HSY
HIW
HD
HON
HPT
HUM
IBM
ITW
IGT
PFE
PBI
POL
PPG
PX
PG
PLD
PHM
NBR
NAV
NWL
NEM
NKE
JWN
NSC
NU
NOC
NUE
NVR
OXY
37
Appendix I: Sample firms and ticker symbols (continued)
Name
Ticker
Name
Ticker
DEVON ENERGY CORP
DIAMOND OFFSHORE DRL
DOMINION RESOURCES INC
DOVER CORP
DOW CHEMICAL CO
DR HORTON INC
DU PONT E.I. DE NEMO URS
DUKE ENRGY CAROLINAS
DUKE REALTY LP
EASTMAN CHEMICAL CO
EATON CORP
ELI LILLY & CO
EMERSON ELECTRIC CO
ENTERGY CORP
ENTERPRISE PRDS.PTNS LP
EVEREST RE GROUP
EXELON CORP
EXXON MOBIL CORP
PEPCO HOLDINGS INC
PEPSICO INC
METLIFE INC
MGIC INVESTMENT CORP
MOHAWK INDUSTRIES INC
JC PENNEY CO
JOHNSON & JOHNSON
JOHNSON CONTROLS INC
JPMORGAN CHASE & CO
KB HOME
KELLOGG CO
KIMBERLY-CLARK CORP
KIMCO REALTY CORP
KINDER MORGAN ENERGY PRNS
KOHLS CORP
KROGER CO
XCEL ENERGY INC
XEROX CORP
XL CAPITAL LTD
YUM! BRANDS INC
VALERO ENERGY CORP
VF CORP
VORNADO REALTY LP
VULCAN MATERIALS
WAL-MART STORES INC
WALT DISNEY CO/THE
WEATHERFORD INTL.INC
WELLPOINT INC
WELLS FARGO & CO
WEYERHAEUSER CO
WHIRLPOOL CORP
WILLIAMS COS INC/THE
WISCONSIN ENERGY CORP
WNGRTN.REALTY INVRS.
TARGET CORP
TECO ENERGY INC
DVN
DO
D
DOV
DOW
DHI
DD
DUK
DRE
EMN
ETN
LLY
EMR
ETR
EPD
RE
EXC
XOM
POM
PEP
MET
MTG
MHK
JCP
JNJ
JCI
JPM
KBH
K
KMB
KIM
KMP
KSS
KR
XEL
XRX
XL
YUM
VLO
VFC
VNO
VMC
WMT
DIS
WFT
WLP
WFC
WY
WHR
WMB
WEC
WRI
TGT
TE
OFFICE DEPOT INC
OLIN CORP
OMNICOM GROUP
ONEOK INC
OWENS-ILLINOIS INC
LENNAR CORP
LINCOLN NATIONAL CORP
LOCKHEED MARTIN CORP
LOUISIANA-PACIFIC CORP
MARATHON OIL CORP
MARSH & MCLENNAN COS INC
MARTIN MARIETTA MATS INC
MASCO CORP
MBIA INC
MCDONALD’S CORP
MCKESSON CORP
MDC HOLDINGS INC
MEADWESTVACO CORP
MEDTRONIC INC
MERCK & CO INC
TENET HEALTHCARE CORP
TESORO CORPORATION
TEXTRON INC
TIME WARNER INC
TJX COMPANIES INC/ THE
TOLL BROTHERS INC
TRANSOCEAN INC
TYSON FOODS INC
UNION PACIFIC CORP
UNISYS CORP
UNITED PARCEL SER. INC
UNITED RENTALS INC
UNITED TECHNOLOGIES CORP
UNITEDHEALTH GROUP INC
UNIVERSAL HEALTH SVS INC
UNUM GROUP
RADIAN GROUP INC
RADIOSHACK CORP
RAYTHEON CO
REPUBLIC SERVICES INC
RITE AID CORP
ROYAL CRBN.CRUISES LTD
RPM INTERNATIONAL INC
RYDER SYSTEM INC
RYLAND GROUP INC
SAFEWAY INC
SEALED AIR CORP
SEMPRA ENERGY
SHERWIN-WILLIAMS CO
SIMON PROPERTY GROUP INC
SOUTHWEST AIRLINES
STANDARD PACIFIC CORP
STARWOOD HTLS.& RSTS WWD
SUPERVALU INC
ODP
OLN
OMC
OKE
OI
LEN
LNC
LMT
LPX
MRO
MMC
MLM
MAS
MBI
MCD
MCK
MDC
MWV
MDT
MRK
THC
TSO
TXT
TWX
TJX
TOL
RIG
TSN
UNP
UIS
UPS
URI
UTX
UNH
UHS
UNM
RDN
RSH
RTN
RSG
RAD
RCL
RPM
R
RYL
SWY
SEE
SRE
SHW
SPG
LUV
SPF
HOT
SVU
38
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42
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43
Figures
44
Figure 1: Time evolution of CDS and bid-ask spreads
The figure shows the time evolution of CDS spreads, CDS bid-ask-spreads, and the Default Sentiment Index (DSI) from 2004 to Q3:2010. The grey-shaded areas present the 20%- and 80%quantile of CDS spreads (upper panel) and CDS bid-ask-spreads (lower panel).
Time evolution of cds spreads
0
400
0
−2
−1
200
CDS spread (bps)
1
600
2
20−80% quantile
Mean
DSI
2004
2005
2006
2007
2008
2009
2010
2
20−80% quantile
Mean
DSI
1
−1
0
40
−2
20
0
Bid−ask spread (bps)
60
80
Time evolution of bid−ask spreads
2004
2005
2006
2007
45
2008
2009
2010
0.20
0.10
0.00
0.30
R2
R2
2004
2006
2008
Ind. Goods and Services
(32 firms)
2010
2004
2006
2008
Healthcare
(17 firms)
2010
2004
2004
2008
2006
2008
Basic Resources
(6 firms)
2006
Technology
(6 firms)
2010
2010
2006
2008
2010
2004
2006
2008
2010
Personal and Household Goods
(21 firms)
2004
Utilities
(18 firms)
0.10
0.00
0.20
0.10
0.00
0.30
0.10
0.20
0.30
0.10
R2
0.00
0.20
0.30
2004
2004
2004
2008
2008
2006
2008
Automobiles and Parts
(6 firms)
2006
Chemicals
(11 firms)
2006
Insurance
(13 firms)
2010
2010
2010
Sector specific R2 s are the average of all sample firms’ R 2 within a sector. The Telecommunications sector is omitted since it only contains one sample firm.
The figure shows the time evolution of the annual commonality of the Default Sentiment Index and the Bid-Ask-Spreads of CDS contracts for the years from 2004
to 2010 per sector. The R 2 is obtained from running OLS regressions of a CDS contract Bid-Ask-Spread on the Default Sentiment Index for each year (and from
January to September for 2010):
BASit = α + β × Default Sentimentt + εit .
Figure 2: Default Sentiment Commonality
R2
R2
0.30
0.20
0.10
0.20
0.30
0.00
R2
0.00
R2
R2
R2
0.30
0.20
0.10
0.00
0.30
0.20
0.10
0.00
0.30
0.20
0.10
0.00
46
R2
R2
0.30
0.20
0.10
0.00
0.30
2004
2004
2004
2008
2008
2006
2008
Media
(6 firms)
2006
Oil and Gas
(20 firms)
2006
Financial Services
(5 firms)
2010
2010
2004
2004
2008
2010
2004
2008
2006
2008
Travel and Leisure
(9 firms)
2006
Food and Beverage
(11 firms)
2006
Real Estate
(17 firms)
2010
2010
2010
0.10
0.00
0.20
0.30
0.10
0.20
0.10
0.30
R2
0.20
0.00
Figure 2: Default Sentiment Commonality (continued)
R2
R2
0.30
0.20
0.10
0.00
0.30
0.00
0.10
0.20
R2
0.00
R2
R2
R2
0.30
0.20
0.10
0.00
0.30
0.20
0.10
0.00
0.30
0.20
0.10
0.00
47
2008
2006
2008
Banks
(4 firms)
2006
2010
2010
2004
2006
2008
2010
Construction and Material
(5 firms)
2004
2004
Retail
(20 firms)
2000
1500
2004
2005
2006
1st quintile
2nd quintile
3rd quintile
4th quintile
5th quintile
2007
2008
2009
2010
2004
2005
2006
1st quintile
2nd quintile
3rd quintile
4th quintile
5th quintile
2007
2008
2009
2010
Mean CDS spreads (LIQ−CSI betas)
500
0
CDS spread (bps)
Mean CDS spreads (LIQ−MLIQ betas)
CDS spread (bps)
500
1000
1500
2000
2004
2005
2006
1st quintile
2nd quintile
3rd quintile
4th quintile
5th quintile
2007
2008
2009
2010
Mean CDS spreads (LIQ−MCDS betas)
where MLIQt is the average value of all individual bid-ask-spreads (market illiquidity), DSI t is the Default Sentiment Index, and MCDSt
is the average CDS spreads in week t. For each reference entity i, we run simple OLS regressions to obtain the coefficient estimates
i
i
i
, βLIQ-DSI
, and βLIQ-MCDS
. The sample is split according to the quintiles of the respective beta estimate.
βLIQ-MLIQ
i
× MCDSt + εit ,
BASit = α + βLIQ-MCDS
i
BASit = α + βLIQ-DSI
× DSIt + εit ,
i
BASit = α + βLIQ-MLIQ
× MLIQt + εit ,
The figure shows the time evolution of average CDS spreads for firms in different quintiles of sensitivity of bid-ask-spreads. The
sensitivity is measured as the OLS coefficient estimate of the following models:
Figure 3: Time evolution CDS spreads in quintiles of DSI betas
CDS spread (bps)
0
1000
2000
1500
1000
500
0
48
Tables
49
50
Avrg. Observations
Adj. R2
Constant
ΔTail beta
ΔSlope
ΔS&P500
ΔVIX
ΔDSI
ΔVolatility
ΔInterest Rate
ΔFirm value
348.0
0.04
0.312
(11.23)***
-45.788
(-10.02)***
(1)
350.0
0.02
0.191
(8.02)***
-31.939
(-9.76)***
(2)
346.6
0.01
0.288
(8.60)***
8.341
(3.74)***
(3)
346.6
0.08
0.198
(7.32)***
-42.961
(-9.81)***
-26.912
(-9.58)***
9.029
(4.20)***
(4)
350.0
0.00
0.308
(10.90)***
-1.069
(-2.76)***
(5)
346.6
0.08
0.200
(6.91)***
-43.013
(-9.85)***
-26.812
(-9.55)***
8.981
(4.15)***
-0.813
(-1.98)**
(6)
346.6
0.15
0.179
(6.32)***
-23.598
(-6.14)***
-20.895
(-8.93)***
7.011
(3.18)***
-0.819
(-1.96)**
2.275
(9.65)***
(7)
346.6
0.18
0.240
(6.76)***
-0.312
(-8.89)***
-23.920
(-6.32)***
-16.720
(-8.62)***
6.994
(3.23)***
-0.615
(-1.49)*
(8)
346.6
0.08
0.166
(5.61)***
-19.934
(-3.29)***
-41.013
(-9.70)***
-31.981
(-8.25)***
8.521
(3.99)***
-0.826
(-2.02)***
(9)
345.6
0.08
368.831
(1.86)**
0.193
(6.32)***
- -42.688
(-9.88)***
-26.318
(-9.52)***
8.771
(4.04)***
-0.714
(-1.90)**
(10)
The main independent variable of interest is Default Sentiment Index (DSI) which is the average Google Search Volume Index of thirty
search phrases from Google Trends, adjusted for seasonality, winsorized at the 5% level, and scaled by its standard deviation. Other
regressors are defined in Appendix I. Values of corresponding t-statistics are calculated as outlined in Collin-Dufresne et al. (2001) and
Ericsson et al. (2009) and are given below the coefficients. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level,
respectively.
ΔCDSt = α + β × ΔDefault Sentimentt + γ × ΔMerton-typet + Θ × ΔControlst + εt .
The table shows the results of simple OLS time-series regressions in first differences of CDS mid quotes on first differences of the
Default Sentiment Index (DSI) and first differences of control variables. All regressions are of the following type:
Table I: Benchmark time-series regressions in first differences.
51
Firm FE
Year dummies
Observations
Adj. R2
Constant
ΔTail beta
ΔSlope
ΔS&P500
ΔVIX
ΔDSI
ΔVolatility
ΔInterest Rate
ΔFirm value
x
x
79,334
0.011
-0.314
(-4.66)***
-73.380
(-6.86)***
(1)
x
x
79,792
0.004
0.602
(5.96)***
-29.995
(-9.45)***
(2)
x
x
79,028
0.002
-0.272
(-4.11)***
5.944
(1.48)
(3)
x
x
79,792
0.002
-0.325
(-4.07)***
-1.061
(-2.74)***
(4)
x
x
79,028
0.013
0.426
(4.90)***
-70.675
(-6.56)***
-24.727
(-9.10)***
4.196
(0.93)
-1.012
(-2.59)***
(5)
x
x
79,028
0.020
0.378
(4.52)***
-53.521
(-4.99)***
-19.255
(-8.47)***
3.784
(0.87)
-1.026
(-2.62)***
2.085
(9.26)***
(6)
x
x
79,028
0.024
0.468
(5.24)***
-0.291
(-8.76)***
-53.355
(-5.18)***
-15.764
(-8.20)***
3.563
(0.83)
-0.806
(-2.08)**
(7)
x
x
79,028
0.013
0.135
(1.30)
-16.450
(-2.98)***
-68.759
(-6.60)***
-28.772
(-7.99)***
3.961
(0.89)
-1.025
(-2.63)***
(8)
x
x
78,795
0.013
23.467
(1.83)*
0.426
(4.99)***
-70.483
(-6.53)***
-24.426
(-9.03)***
4.205
(0.93)
-1.002
(-2.58)***
(9)
where αi and μt are firm-fixed effects and year dummies, respectively. The main independent variable of interest is Default Sentiment
Index (DSI) which is the average Google Search Volume Index of thirty search phrases from Google Trends, adjusted for seasonality,
winsorized at the 5% level, and scaled by its standard deviation. Other regressors are defined in Appendix I. Values of corresponding
t-statistics are corrected for clustering on the firm level and are given below the coefficients and *, **, and *** indicate statistical
significance at the 10%, 5%, and 1% level, respectively.
ΔCDSit = αi + μt + β × ΔDefault Sentimentt + Θ × ΔControlsit + εit ,
The table shows the results of OLS panel regressions in first differences of CDS mid quotes on first differences of the Default Sentiment
Index (DSI) and first differences of control variables. All regressions are of the following type:
Table II: Benchmark panel regressions in first differences.
52
Firm FE
Year dummies
Observations
Adj. R2
Constant
ΔVolatility
ΔInterest Rate
ΔFirm value
ΔCDSILLIQ
ΔBASM
abs
ΔBASIabs
ΔBASabs
ΔDSI
x
79,028
0.24
-73.250
(-5.75)***
-23.690
(-10.83)***
11.073
(1.73)*
0.214
(26.88)***
-0.643
(-1.71)*
1.734
(10.30)***
(1)
x
78,678
0.03
-70.602
(-6.61)***
-25.387
(-9.65)***
6.154
(1.34)
0.215
(20.64)***
0.941
(2.67)***
-0.814
(-2.06)***
(2)
x
79,028
0.02
-72.825
(-6.61)***
-22.615
(-9.81)***
6.244
(1.35)
0.210
(19.32)***
2.425
(5.25)***
-0.514
(-1.36)
(3)
x
47,600
0.02
1.369
(7.18)***
-83.489
(-6.71)***
-35.048
(-9.04)***
3.225
(0.64)
-0.152
(-1.90)**
-1.944
(-1.87)**
(4)
x
x
79,028
0.24
-73.813
(-5.78)***
-21.696
(-10.44)***
10.677
(1.67)*
0.362
(5.13)***
-0.648
(-1.72)*
1.732
(10.29)***
(5)
x
x
78,678
0.03
-71.201
(-6.65)***
-23.278
(-9.17)***
5.726
(1.26)
0.389
(4.64)***
0.934
(2.66)***
-0.821
(-2.08)***
(6)
x
x
79,028
0.02
-73.307
(-6.64)***
-20.792
(-9.22)***
5.831
(1.27)
0.331
(4.17)***
2.368
(5.18)***
-0.530
(-1.40)
(7)
x
x
47,600
0.02
1.249
(6.91)***
-83.443
(-6.71)***
-32.687
(-8.76)***
3.033
(0.60)
-0.650
(-5.18)***
-1.926
(-1.85)**
(8)
where αi and μt are firm-fixed effects and year dummies, respectively. The main independent variable of interest is Default Sentiment
Index (DSI) which is the average Google Search Volume Index of thirty search phrases from Google Trends, adjusted for seasonality,
winsorized at the 5% level, and scaled by its standard deviation. The liquidity proxies include a contracts Bid-Ask-Spread BAS abs , which
is the end-of-week absolute CDS bid-ask spread, calculated as daily CDS ask quote minus CDS bid price and denoted in bps, the Sector
Bid-Ask-Spread BASIabs calculated as the cross-sectional average of absolute bid-ask spreads of all firms within an industry, and the
Market Bid-Ask-Spread BASM
abs calculated as the cross-sectional average of relative bid-ask spreads of all sample firms excluding the
respective firm. CDSILLIQ is the end-of-week value of the CDSILLIQ measure proposed by Junge and Trolle (2015). Other regressors
are defined in Appendix I. Columns (1)-(4) report results from regressions including firm-fixed effects while the results in (5)-(8) also
include year dummies in the analyses. Values of corresponding t-statistics are corrected for clustering on the firm level and are given
below the coefficients and *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
ΔCDSit = αi + μt + β × ΔDefault Sentimentt + γ × ΔLiquidityit + Θ × ΔControlsit + εit ,
The table shows the results of OLS panel regressions in first differences of CDS mid quotes on first differences of the Default Sentiment
Index (DSI) and first differences of liquidity proxies. All regressions are of the following type:
Table III: Panel regressions of CDS spreads on liquidity.
Table IV: Panel regressions of CDS spreads on liquidity residuals.
The table shows the results of OLS panel regressions in first differences of CDS mid quotes on
first differences of the Default Sentiment Index (DSI) and first differences of liquidity proxies. All
regressions are of the following type:
ΔCDSit = αi + μt + β × ΔDefault Sentimentt + γ × ΔLiquidityit + Θ × ΔControlsit + εit ,
where αi and μt are firm-fixed effects and year dummies, respectively. The main independent
variable of interest is Default Sentiment Index (DSI) which is the average Google Search Volume
Index of thirty search phrases from Google Trends, adjusted for seasonality, winsorized at the 5%
level, and scaled by its standard deviation. The liquidity proxies are the residuals of regressions in
I
first differences of the original liquidity variables BASabs , BASM
abs , and BASabs on first differences of
the Default Sentiment Index. Other regressors are defined in Appendix I. Values of corresponding
t-statistics are corrected for clustering on the firm level and are given below the coefficients and *,
**, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.
ΔDSI
ΔBAS-ΔDSI residual
(1)
(2)
(3)
(4)
(5)
(6)
-1.061
(-2.73)***
1.780
(10.25)***
-1.065
(-2.73)***
-1.061
(-2.74)***
-1.031
(-2.67)***
1.732
(10.26)***
-1.022
(-2.60)***
-1.033
(-2.64)***
ΔBASI -ΔDSI residual
0.939
(2.66)***
ΔBASM -ΔDSI residual
0.938
(2.66)***
2.326
(5.14)***
ΔFirm value
-0.278
(-3.78)***
-0.308
(-3.84)***
-0.276
(-3.47)***
-73.822
(-5.78)***
-21.693
(-10.44)***
10.685
(1.67)*
0.376
(5.33)***
x
x
79,792
0.23
x
x
79,442
0.02
x
x
79,792
0.01
x
x
79,028
0.24
ΔInterest Rate
ΔVolatility
Constant
Firm FE
Year dummies
Observations
Adj. R2
53
-71.214
(-6.65)***
-23.271
(-9.17)***
5.736
(1.26)
0.397
(4.71)***
2.369
(5.17)***
-73.307
(-6.64)***
-20.791
(-9.22)***
5.831
(1.27)
0.353
(4.36)***
x
x
78,678
0.03
x
x
79,028
0.02
Table V: Panel regressions of CDS spreads on interaction terms.
The table shows the results of OLS panel regressions in first differences of CDS mid quotes on first differences of the Default Sentiment Index
(DSI) and first differences of liquidity proxies and their interaction. All regressions are of the following type:
ΔCDSit = αi + μt + β × ΔDSIt + γ × ΔLiquidityit + δ × ΔLiquidityit × ΔDSI + Θ × ΔControlsit + εit ,
where αi and μt are firm-fixed effects and year dummies, respectively. The main independent variable of interest is Default Sentiment Index (DSI)
which is the average Google Search Volume Index of thirty search phrases from Google Trends, adjusted for seasonality, winsorized at the 5% level,
and scaled by its standard deviation. The liquidity proxies include a contracts Bid-Ask-Spread BASabs , which is the end-of-week absolute CDS
bid-ask spread, calculated as daily CDS ask quote minus CDS bid price and denoted in bps, and the Market Bid-Ask-Spread BASM
abs calculated as
the cross-sectional average of relative bid-ask spreads of all sample firms excluding the respective firm. ΔLiquidityit × ΔDefault Sentiment is the
interaction term of the DSI and the respective liquidity proxy. Other regressors are defined in AppendixI. Values of corresponding t-statistics are
corrected for clustering on the firm level and are given below the coefficients and *, **, and *** indicate statistical significance at the 10%, 5%, and
1% level, respectively.
ΔDSI
ΔBASabs
ΔDSI × ΔBASabs
ΔBASM
abs
(1)
-0.574
(-1.33)
1.790∗∗∗
(10.88)
-0.578 ∗
(-1.84)
(2)
-0.365
(-0.84)
1.729∗∗∗
(11.22)
-0.619 ∗∗
(-2.04)
ΔDSI × ΔBASM
abs
ΔFirm value
ΔInterest rate
ΔVolatiltiy
ΔVIX
ΔS&P 500
ΔSlope
-56.490 ∗∗∗
(-4.84)
-16.980 ∗∗∗
(-8.39)
8.290
(1.60)
-0.151
(-0.53)
-0.260 ∗∗∗
(-6.49)
-15.550 ∗∗∗
(-3.81)
0.068
(0.70)
x
x
79,028
0.26
-0.325 ∗∗∗
(-4.02)
Firm FE
x
Year dummies
x
N
79,792
0.23
Adj. R2
t statistics in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Constant
54
(3)
0.093
(0.24)
(4)
0.163
(0.41)
2.389∗∗∗
(5.25)
-3.563 ∗∗∗
(-10.53)
1.997∗∗∗
(4.57)
-3.142 ∗∗∗
(-9.43)
-53.320 ∗∗∗
(-4.84)
-10.930 ∗∗∗
(-5.17)
3.390
(0.82)
-0.561 ∗∗
(-2.25)
-0.302 ∗∗∗
(-7.28)
-8.070
(-1.53)
-0.046
(-0.45)
x
x
79,028
0.03
-0.486 ∗∗∗
(-5.62)
x
x
79,792
0.02
Table VI: Panel regressions of CDS spreads on DSI residuals.
The table shows the results of OLS panel regressions in first differences of CDS mid quotes on first
differences of the residuals of the Default Sentiment Index (DSI) and first differences of liquidity
proxies. All regressions are of the following type:
ΔCDSit = αi + μt + β × ΔDSI residualt + γ × ΔLiquidityit + Θ × ΔControlsit + εit ,
where αi and μt are firm-fixed effects and year dummies, respectively. The Default Sentiment
Index (DSI) is the average Google Search Volume Index of thirty search phrases from Google
Trends, adjusted for seasonality, winsorized at the 5% level, and scaled by its standard deviation.
Its residuals are obtained from regressions in first differences of the original DSI variable on the
I
three liquidity proxies BASabs , BASM
abs , and BASabs .Control variables are first differences of the
Firm value, Interest rate, Volatility, VIX, S&P 500, Slope and Tail beta, which are all defined in
Appendix I. Values of corresponding t-statistics are corrected for clustering on the firm level and
are given below the coefficients and *, **, and *** indicate statistical significance at the 10%, 5%,
and 1% level, respectively.
ΔBAS
ΔDSI-ΔBAS residual
(1)
1.781∗∗∗
(10.29)
-0.735 ∗
(-1.96)
ΔMBAS
ΔDSI-ΔMBAS residual
(2)
(3)
2.332∗∗∗
(5.16)
-0.532
(-1.43)
ΔSBAS
ΔDSI-ΔSBAS residual
Constant
-0.296 ∗∗∗
(-3.99)
x
x
79,792
0.23
Firm FE
Year dummies
Controls
N
Adj. R2
t statistics in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
-0.300 ∗∗∗
(-3.76)
x
x
79,792
0.01
55
0.936∗∗∗
(2.67)
-0.693 ∗
(-1.79)
-0.319 ∗∗∗
(-3.98)
x
x
79,442
0.02
(4)
1.722∗∗∗
(10.22)
-0.526
(-1.43)
0.085
(1.00)
x
x
x
78,795
0.25
(5)
1.941∗∗∗
(4.47)
-0.421
(-1.10)
0.067
(0.66)
x
x
x
78,795
0.03
(6)
0.883∗∗
(2.54)
-0.582
(-1.49)
0.094
(0.93)
x
x
x
78,446
0.04
Table VII: Panel regressions of CDS spreads on retail investor attention and household sentiment.
The table shows the results of OLS panel regressions in first differences of CDS mid quotes on first differences of the Default Sentiment Index (DSI)
and first differences of proxies for retail investor attention (Panel A) and household sentiment (Panel B). All regressions are of the following type:
ΔCDSit = αi + μt + β × ΔDSIt + γ × ΔASVIit + δ × ΔFEARSit + Θ × ΔControlsit + εit ,
where αi and μt are firm-fixed effects and year dummies, respectively. The main independent variable of interest is Default Sentiment Index (DSI)
which is the average Google Search Volume Index (GSVI) of thirty search phrases from Google Trends, adjusted for seasonality, winsorized at the
5% level, and scaled by its standard deviation. ASVIit is the abnormal search volume on a firm’s ticker symbol in Google Trends as introduced
in Da et al. (2011). FEARS is the household sentiment index proposed by Da et al. (2015) based on the GSVI of thirty economics-related search
phrases. The liquidity proxies include a contracts Bid-Ask-Spread BASabs , which is the end-of-week absolute CDS bid-ask spread, calculated as
daily CDS ask quote minus CDS bid price and denoted in bps, the Sector Bid-Ask-Spread BASIabs calculated as the cross-sectional average of
absolute bid-ask spreads of all firms within an industry, and the Market Bid-Ask-Spread BASM
abs calculated as the cross-sectional average of relative
bid-ask spreads of all sample firms excluding the respective firm. CDSILLIQ is the end-of-week value of the CDSILLIQ measure proposed by
Junge and Trolle (2015). Control variables are first differences of the firm value, interest rate and volatility, which are defined in AppendixI. Values
of corresponding t-statistics are corrected for clustering on the firm level and are given below the coefficients and *, **, and *** indicate statistical
significance at the 10%, 5%, and 1% level, respectively.
Panel A: ASVI
ΔDSI
ΔASVI
(1)
-0.956∗
(-1.92)
0.538∗∗
(2.06)
ΔASVI × ΔDSI
(2)
-0.957∗
(-1.93)
0.543∗
(1.93)
0.036
(0.19)
ΔBASabs
(3)
-0.570
(-1.20)
0.718∗∗∗
(2.64)
0.046
(0.24)
1.887∗∗∗
(12.06)
ΔBASIabs
(4)
-0.751
(-1.50)
0.549∗
(1.91)
0.071
(0.35)
0.941∗∗
(2.43)
0.453∗∗∗
(3.31)
59,476
0.012
0.453∗∗∗
(3.31)
59,476
0.012
0.361∗∗∗
(3.36)
59,476
0.243
0.411∗∗∗
(3.10)
59,134
0.028
2.606∗∗∗
(4.45)
0.347∗∗∗
(2.75)
59,476
0.023
(1)
-1.118∗∗∗
(-2.90)
1.095∗∗
(2.16)
(2)
-0.769∗∗
(-2.09)
1.260∗∗∗
(2.73)
1.733∗∗∗
(10.29)
(3)
-0.937∗∗
(-2.39)
1.207∗∗
(2.45)
(4)
-0.659∗
(-1.77)
1.349∗∗∗
(2.66)
(5)
-2.724∗∗
(-2.55)
3.826∗∗∗
(4.63)
ΔBASM
abs
Constant
N
Adj. R2
Panel B: FEARS
ΔDSI
ΔFEARS
ΔBAS
ΔSBAS
0.935∗∗∗
(2.66)
ΔMBAS
2.372∗∗∗
(5.18)
ΔCDSILLIQ
Constant
N
Adj. R2
Firm FE
Year dummies
Controls
(5)
-0.408
(-0.85)
0.673∗∗
(2.34)
0.029
(0.15)
0.396∗∗∗
(4.60)
79,028
0.01
x
x
x
0.327∗∗∗
(4.67)
79,028
0.24
x
x
x
t statistics in parentheses
∗
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
56
0.356∗∗∗
(4.35)
78,678
0.03
x
x
x
0.294∗∗∗
(3.77)
79,028
0.02
x
x
x
1.342∗∗∗
(7.30)
-0.632∗∗∗
(-5.07)
47,600
0.02
x
x
x