Connectedness between US industry level credit markets and determinants

Accepted Manuscript Connectedness between US industry level credit markets and determinants Syed Jawad Hussain Shahzad, Ghulam Mujtaba Kayani, Syed Ali Raza, Nida Shah, Khamis H. Al-Yahyaee

PII: DOI: Reference:

S0378-4371(17)30952-4 https://doi.org/10.1016/j.physa.2017.09.060 PHYSA 18665

To appear in:

Physica A

Received date : 23 April 2017 Revised date : 6 August 2017 Please cite this article as: S.J.H. Shahzad, G.M. Kayani, S.A. Raza, N. Shah, K.H. Al-Yahyaee, Connectedness between US industry level credit markets and determinants, Physica A (2017), https://doi.org/10.1016/j.physa.2017.09.060 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

*Highlights (for review)

HIGHLIGHTS 

Connectedness between US industry-level credit markets



Total, net directional and pairwise spillovers are estimated



Strong interactions for CDS spread change and volatility among all ten industries



Consumer services and Basic Materials industries are the significant risk transmitters



Economic policy uncertainty and volatilities significant determinants

*Manuscript Click here to view linked References

Connectedness between US Industry Level Credit Markets and Determinants Syed Jawad Hussain Shahzad Montpellier Business School, 2300 Avenue des Moulins, 34080 Montpellier, France Tel: +33467102500; E-mail address: [email protected]; [email protected]

Ghulam Mujtaba Kayani COMSATS Institute of Information Technology, Islamabad Pakistan E-mail address: [email protected]

Syed Ali Raza Iqra University, Main Campus: Defence View, Shaheed-e-Millat Road, Karachi Pakistan E-mail address: [email protected]

Nida Shah Iqra University, Main Campus: Defence View, Shaheed-e-Millat Road, Karachi Pakistan E-mail address: [email protected]

Khamis H. Al-Yahyaee Sultan Qaboos University, Muscat, Oman E-mail address: [email protected]

Abstract We examine the connectedness between US industry-level credit markets, using both Credit Default Spread (CDS) changes and volatilities, over the period December 17, 2007, till November 13, 2015. The total, net directional and pairwise spillovers are estimated based on the generalized VAR framework developed by Diebold and Yilmaz (2012). The empirical analysis shows strong interactions for CDS spread change and volatility among all ten industries. Consumer Services and Basic Materials are the significant risk transmitters. Economic policy uncertainty and different market volatilities significantly determine the credit market risk spillovers which also increase during market turbulence situations indicating a possible contagion effect. Implications of the findings are discussed.

Keywords: Credit default swaps, spillover index approach, network connectedness

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1. Introduction The connectedness between industry-level Credit Default Swaps (hereafter CDSs) is important for credit risk management and portfolio rebalancing decisions. CDS contracts play a vital role in inferring eventual defaults for traders, practitioners, and regulators. The size, structure opacity, and interconnectedness of the CDS market may pose systemic risks to financial institutions and market stability (Shahzad et al. 2017a). The literature on the interconnectedness between CDS markets is relatively young and growing, especially after the global financial crisis of 2008–2009 and the subsequent European sovereign debt crisis of 2010–2012. These crises resulted in an unprecedented increase in the levels of fear, destabilizing the financial markets and creating uncertainty in economic policy making. Increasing credit spreads may be a result of an increase in liquidity risk and a reduction in risk appetite (Beirne & Fratzscher, 2013). We utilize the spillover approach developed by Diebold and Yilmaz (2012) to estimate the total and net directional spillovers between US industry-level credit markets. Diebold and Yilmaz (2009, 2012, 2015a & 2015b) propose a new set of spillover indices from which the direction of spillovers based on forecast error variance decompositions (FEVDs) may be estimated using vector autoregressions (VARs). The method offers several advantages over recently used multivariate models. First, the aggregating and offsetting invariant FEVD does not depend on the Cholesky factor identification of VAR. Therefore, the estimates of variance decomposition are not impacted by the ordering of the variables (Awartani & Maghyereh, 2013). Second, the spillover approach allows measurement of the returns and volatility spillover over time and across multiple classes of assets and markets (Zhang & Wang, 2014). Third, this approach allows measurement of the direction of spillovers from one market to any other market as well as the net spillovers in the reverse direction (Wang et al., 2016). These distinctive features provide more 2

information on dynamic directional spillovers than would a mere measurement of the significance of a parameter estimated within a variance-covariance matrix framework, as in the case of the MGARCH models (Zhou et al., 2012). Our analysis of return and volatility spillovers provides a bridge to the works carried out by Bostanci and Yilmaz (2015) who apply the Diebold-Yilmaz connectedness index methodology to understand the network effects between global sovereign credit risk, via credit default swap spreads, from developed, developing and emerging countries markets. However, our study focuses in particular on the industry-level US credit markets and has the comparative advantage of determinants analysis to better understand the important macro-financial variables that explain the directional spillover effects between the various credit market industries. The spillovers across CDS spreads changes and volatilities may substantially vary because each industry has its own specific characteristics such as the average level of tangible assets, the degree of leverage, perceived credit risk, the cyclical or counter-cyclical behavior, the volume of CDS contracts in each industry or the typical position in the CDS market of firms within the industry. Other studies in this direction include for instance Augustin and Tédongap (2016), Shahzad et al. (2017b), Shahzad et al. (2017c), among others. This study contributes to the literature in a number of ways. Firstly, this study overcomes the problem of dimensionality because we have taken all those industries in the analysis whose data are available from December 17, 2007 until November 13, 2015. Secondly, we use the Diebold and Yilmaz (2012) spillovers index technique to measure the dynamic cross-industry linkages which is previously used by the Diebold and Yilmaz in numerous studies. In 2009 for 19 global equity markets, in 2012 for four U.S. asset markets, in 2014 for U.S financial institutions, in 2015 for six advanced economies and in 2016 for the European and American financial entities, 3

but to the best of our knowledge, has not been used in the literature related to industry-level CDS markets. Thirdly, this study doesn’t look at the determinants that cause the credit default spreads rather add to the literature by analyzing the impact of macro-financial variables on the credit risk association. Fourth, we use daily frequency data because this will help to observe the daily jumps and its connection with the credit default spreads, which is not instigated by the macroeconomic shocks. The findings suggest that the US industry-level credit markets are strongly connected and that Consumer Services and Basic Materials industries are significant risk transmitters. The volatility spillovers from Basic Materials to the other credit industries is particularly high during global financial crisis sub-sample. Economic policy uncertainty and different market volatilities significantly explain the credit market risk spillovers that also increase during market turbulence, indicating a possible contagion effect. The remainder of the study is organized as follows. Section 2 discusses the econometric methodology. Section 3 describes the data and analyzes the empirical findings. Section 4 provides some concluding remarks. 2. Methodology We use graphical networks of interdependence, in the first step of our analysis, based on the variance decomposition algorithm for large vector autoregressive (VAR) model developed by Diebold and Yilmaz (2012) to show the spillovers of CDS spread changes and their associated volatilities across US industries. Next, we estimate the rolling window pair-wise spillover series through DY approach and in the second step of analysis, we use ordinary least square (OLS)

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regression approach to examine the predictability of overall and industry-level pair-wise spillovers through macro-financial variables. 2.1. Diebold and Yilmaz (2012) spillovers approach The spillover approach of Diebold and Yilmaz (2012) is followed, which assumes a covariance stationary n-variable VAR ( p ) as: yt  i 1 i yt 1   t , p

(1)

where yt is the n1 vector of the endogenous variable,  i is the n  n autoregressive coefficient matrix, and  t is a vector of error terms that are assumed to be serially uncorrelated. If the above VAR model represents a covariance stationary process, then the moving average can be illustrated as yt   j 0 Aj t , where the n  n is coefficient matrix A j follows a recursion of 

the form Aj  1 Aj 1   2 Aj  2 

  p Aj  p , with A0 being the n  n identity matrix and

A j  0 for j  0 . The generalized forecast error variance decomposition has been used to

determine the aggregate directional and overall spillovers. It is a representation of the moving averages of the VAR model. The framework eliminates the chances of any dependence of the results on the order of the variables. The below H -step-ahead generalized forecast-error variance decomposition was proposed by Pesaran and Shin (1998);  jj1  h 0  eiAh  e j  H 1

ij  H  

  eA  Ae  H 1

h 0

i

h

2

(2)

,

h i

5

Where  is the variance matrix of the vector of errors  , and  jj is the standard deviation of the error term of the j th equation. Lastly, ei is a vector of selection with a value of one for the i th element and zero otherwise. The spillover index yields a n  n matrix   H   ij  H   where all the entry θij provides the contribution of the variable j to estimate the error variance of the variable i . The variable’s own impact is captured in the main diagonal, while the contributions of the cross variables become part of the off-diagonal elements of the  H  matrix. Since the own and cross variable variance contribution shares remain separate under the generalized decomposition (i.e.,



n

  H   1 ), each item of the variance decomposition matrix is normalized by its row sum

j 1 ij

as follows:

ij  H  

With



n

ij  H 



n

 H 

,

(3)

j 1 ij

  H   1 and

j 1 ij



n

  H   n by construction. This provides the freedom to define

j 1 ij

an aggregate spillover index as:

 TS  H    n

i , j 1,i  j n

ij  H 

 H  i , j 1

 100 

n i , j 1,i  j

ij  H 

n

100.

(4)

This index measures the average contribution of spillovers from shocks to all (other) variables to the overall forecast error variance. The index allows recognition of the directional spillovers between the variables and is more elastic. The directional spillovers taken by one variable i from other variables j are characterized as:

6

 H   

n

DSi  j

j 1, j  i n

ij  H 

 H  i , j 1 ij

  100 

n j 1, j  i

ij  H 

n

 100.

(5)

Below are the directional spillovers that have been transmitted by one variable i to other variables j :

 H   

n

DSi  j

j 1, j  i n

 ji  H 

 H  i , j 1 ji

  100 

n j 1, j  i

 ji  H 

n

 100.

(6)

The set of directional spillovers provides a decomposition of total spillovers into those coming from (or to) a particular variable, for example, in the current application, the spillover matrix

 H  contains diagonal components that reflect own-variable spillovers and off-diagonal components that reflect cross-variables spillovers. The net volatility spillovers from one variables to the entire system is calculated by subtracting Eq. (6) from Eq. (5); NS i H   DS i  j H   DS i  j H .

(7)

The net spillover illustrates whether a variable is a transmitter or a receiver of spillovers. To inspect the net pairwise spillover (NPS), apply the following equation;    H  ji NPSij  H    n    H   ik    i ,k 1

 ij  H     100. n   jk  H    j , k 1 

(8)

The NPS among the variables i and j is the variance of the overall shocks that have been transferred from variable j to i and vice versa.

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As outlines in the start of methodology section, we examine the spillovers using both CDS spread changes and their associated volatilities. The volatility of CDS spread changes has been calculated in several ways in the literature. In line with Taylor (1986) and Nelson (1992), among others, the most broadly used technique, which was proposed by Schwert (1989a, 1989b), is applied in this study. The Schwert volatility technique has three steps. First, the 24th order autoregression of the CDS changes is estimated, where the lag order is selected based on the Schwarz information criterion. In the second step, the 24th order autoregression estimates for the absolute values of the errors obtained from the first step. In the third step, the fitted values in step two are extracted, which is eventually the conditional standard deviation of the CDS changes. This procedure is adopted for all ten industries. Following Nelson (1988) and Schwert (1989b), all absolute errors are multiplied by the constant (2/π)-1/2 ≈ 1.2533. 2.2. Determinants of CDS spillovers through OLS The next step in this research is the identification of the main determinants of the spillovers across overall and pairwise U.S. industry CDS spread changes and volatilities. In doing so, a set of volatilities in different markets is utilized as potential determinants of the spillovers across CDS markets. The specified OLS model has the following form:

(9) where

denotes the DY based rolling-window overall and net directional spillover to industry i

from all other industries in period t. In turn,

are the parameters to be estimated and

is the random error term, which has a normal distribution with a mean of zero and a constant variance. We use volatilities in four different markets i.e., the Chicago Board Options Exchange

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(CBOE) volatility index (VIX) which measures the implied volatility of the S&P 500 index options over the next 30 calendar days, the Merrill Option Volatility Expectations Index (MOVE) to reflect future Treasury bond yield volatility, CBOR crude oil volatility index (OVX) and CBOE/COMEX gold volatility index (GVX). We also use economic policy uncertainty (EPU) index, a news-based index, compiled and disseminated by Baker et al. (2016) using data from over 1,000 newspapers from Access World News’s News Bank comprised of terms related to “legislation” or “deficit” or “regulation” or “Congress” or “Federal Reserve” or “White House”. These different markets’ volatility indices are often referred to as the fear indices and are widely recognized as the measures of investors’ risk aversion. High values of these indices reflect a high level of fear in the respective markets and typically coincide with sharp declines in the respective price markets. Thus, a high value, through inherent greater uncertainty and risk aversion of investors, is also associated with a higher probability of defaults, leading to a significant widening of CDS spreads (e.g., Shahzad (2017a)). Accordingly, it seems reasonable to think that changes in these risk indices may impact the risk management strategies and induce revisions in asset allocation decisions, which in turn may significantly impact the industry-wise CDS spillovers. The data on these variables are collected from Thomson Reuters DataStream. It is also reasonable to think that spillovers may increase during the times of higher uncertainty and financial stress and that the spillover effects across financial markets tend to be stronger. Therefore, two dummy variables having a value of one and zero otherwise are also included representing the global financial crisis (2007-09) and the Euro Area recession (2011-13) periods1.

1

The global financial crisis (2007-09) is according to the US recessions dated by National Bureau of Economic Research (NBER) from December 1, 2007 till June 30, 2009. The Euro Area recessions (2011-13) dated by Centre for Economic Policy Research (CEPR) from October 1, 2011 till March 31, 2013.

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3. Data and findings The empirical analysis is carried out using the closing values of the CDS index spreads for the ten industries of USA. These industries include Consumer Goods, Financials, Consumer Services,

Healthcare,

Industrials,

Technology,

Basic

Materials,

Oil

and

Gas,

Telecommunications and Utilities. The data is from December 17, 2007 till November 13, 2015 (a total of 2066 daily observations) and is obtained from Thomson Reuters Datastream. The industry level CDS indices were launched on the start date of our sample and hence, the start date is dictated by the availability of the liquid data. The 5-year industry-level CDS index spreads are frequently traded, equally weighted, rebalanced every six months and show the average mid-spreads of the firms within each industry. The breakdown of industries follows the Industry Classification Benchmark (ICB) which is a widely used global standard for company classification developed by Dow Jones and Financial Times Stock Exchange. Figure 1 plots the time evolution of the industry level CDS spreads over the study period. The light blue area indicates the period of the US recession in 2007-2009 and the light gray area indicates the period of the Euro area recession of 2011-2013. The figure shows that the CDS spreads were high during the US recession period in all industries. The descriptive statistics of the industry-level CDS spreads are shown in Table 1. The Financials have the highest mean CDS spread indicating a general high level of credit risk for the Financial industry. The lowest mean CDS spread is for the Consumer Goods industry which indicates the strength of this sector in controlling credit risk. The Basic Materials industry shows the highest standard deviation value of the CDS spread possibly due to a significant increase in the CDS spread towards the end of sample period (See Figure 1). The Jarque-Bera test statistics reject the null hypothesis of normality at a significance level of 1% for all ten industries. The unit root properties of the CDS 10

spread are examined using the Augmented Dickey-Fuller (ADF) unit root test and the test statistics reject the null hypothesis for the first differenced series meaning that the change in CDS spreads is stationary and can be used for spillover analysis through DY approach. << Insert Figure 1 about here >> << Insert Table 1 about here >>

Table 2 reports the full sample spillover analysis using the CDS spread changes for all industries. The total spillover index across all US industries CDS spreads changes is 15%. The Consumer Services industry has the highest net transmission to all other industries and Oil & Gas has the highest net reception from other industries. The results of industry-level CDS volatilities for the full sample are reported Table 3. The total volatility spillover across US credit industries is 10.6%. The Healthcare (Oil & Gas) industry is the highest net transmitter (receiver) of spillover to (from) all other industries. The Oil & Gas industry has the highest level of spillover reception both in terms of CDS spread change and volatility, with a higher magnitude of the latter. The full sample analysis does not provide a complete picture of the spillover since it provides an aggregate measure of risk spillovers. An obvious limitation of the full sample spillover analysis, best described as “average spillover” effects, as noted by Diebold and Yilmaz (2009) is ignorance of any cyclical movements and time-variation in spillovers. To explore such time-evolution of spillovers across US credit industries, we use a rolling window based estimation of DY (2012) approach. The time-varying behaviors of the total spillover index across all industry-level credit markets for the CDS spread change and volatility are shown in Figures 2 and 3, respectively. In these figures, we plot the spillover index by re-estimating the VAR models for the full-sample period using a 200-day

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rolling window. Overall, as in the full sample analysis, the total spillover is higher for CDS spread changes than for CDS spread volatilities. The total spillover across all industries is significantly higher during the global financial crisis of 2008–2009 and again during the European sovereign debt crisis of 2010–2012. Both the total spillover indexes of the CDS spread change and volatilities show their respective highest values of 78.46 and 62.10 on May 7, 2010. It is important to note that on May 6, 2010 the New York Stock Exchange witnessed the "flash crash", a temporarily depletion of 1,000 points off of the Dow Jones Industrial Average2 (DJIA) and it was the largest intra-day fall ever. This finding suggests that the nature of total spillover and net directional spillovers changed, relatively increasing, during the crisis episodes and thus require further analyses in terms of net contributors and receivers of spillovers. << Insert Table 2 & 3 about here >> << Insert Figure 2 & 3 about here >>

Although the DY (2012) spillover approach is not sensitive to VAR ordering, the estimation window size and forecast horizon may yield different results. Therefore, before moving forward towards the sub-sample analysis and determinants of spillovers, we examine the robustness of rolling window estimates under different forecast horizons (where h denotes the forecast horizon in days) and under different estimation window sizes (window size in days). The results shown in Figure 4a and 4b confirm that our main estimates are robust under different specifications and hence are reliable. << Insert Figure 4 about here >>

2

A flash crash is a very rapid, deep, and volatile fall in security prices occurring within an extremely short time period. A flash crash frequently stems from trades executed by black-box trading, combined with high-frequency trading, whose speed and interconnectedness can result in the loss and recovery of billions of dollars in a matter of minutes and seconds.

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Figure 5a-5d shows the network diagram of the pairwise directional spillovers using CDS spread change between all the US industry-level credit markets. Figure 5 consists of four panels where these panels show the pairwise connectedness during full sample periods, the global financial crisis (2007-2009), the Eurozone crisis period (2011-2013) and post Eurozone crisis sub-periods. The total spillover index is 15.0% during full sample periods, 62.6% during the GFC, 34.6% during the Eurozone crisis and 8.0% during the post crisis sub-period. It is evident that the total spillover across the credit markets significantly increased during the crisis periods especially during GFC. During GFC sub-period (Figure 5b), Consumer Services, Basic Materials, Consumer Goods, Financials and Oil & Gas sectors are the net transmitters of spillovers. Industrials, Healthcare, Utilities, Technology, and Telecommunications are the shock receiver industries. The connectedness among the US credit markets during the period of the Eurozone crisis (Figure 5c) shows that Consumer Services, Financials, and Healthcare were the shock transmitter industries and Consumer Services and Healthcare industries have the strongest pairwise connectedness between them, whereas Utilities show the lowest connectedness and weak ties with other industries. << Insert Figure 5 about here >> The network plots of the pairwise directional volatility spillover from/to the US industry-level credit markets is shown in Figure 6a-6d. The total spillover index is 10.6% during full sample periods, 45.0% during the GFC, 24.4% during the Eurozone crisis and 9.2% during the post crisis sub-period once again confirming that the spillovers increase during turbulence times. During GFC sub-period (5b), the Basic Materials industry has strong connectedness and the Utility industry has the lowest connectedness with other industries. The highest transmission is from Basic Materials to the Oil & Gas industry. The Technology, Industrials, Utilities, and 13

Telecommunications industries are the volatility shock receivers on a net basis during the GFC. The Consumer Services industry is also categorized as a net volatility shock receiver. Figure 5c shows the volatility spillover connectedness among the US credit markets during the period of the Eurozone crisis. All the industries relate to each other; however, Consumer Services industry has

the

strongest

pairwise

connectedness

with

Healthcare

and

Basic

Materials.

Telecommunication and Utilities are shock receivers. The Industrials and Technology industries which are shock receivers in the CDS spread change network become shock transmitters in the volatility spillovers network. << Insert Figure 6 about here >> Finally, we examine the possible determinants of total and net directional spillovers. In doing so, we first report the descriptive statistics and unit root properties of the rolling-window based overall spillover indices, the rolling-window based industry-wise net directional spillovers and the considered explanatory variables in Table 4. All the variables are stationary at levels and hence the regression analysis (see equation 10) can be performed using the variables in levels. The regression estimates for the CDS spread change and volatilities are reported in Table 5 and 6, respectively. Results indicate that the selected market volatilities and EPU significantly explain the overall spillovers across all credit industries as well as the net directional spillovers among them. The explanatory power of markets’ volatilities and EPU for the US credit markets’ spillovers varies across industries. It is highest for net directional CDS spread change spillovers from/to Basic Materials and Healthcare industries and spillover index (SOI) across all industries, respectively. The highest predictability of CDS volatility spillovers through market volatilities and economic policy uncertainty is found for the Basic Materials, industrials and overall spillover index. The sign and significance of the coefficients in determining the 14

connectedness/spillovers across US credit markets varies. The dummy variables representing the periods of the global financial crisis and Eurozone recession are also significant and in particular positive for the overall spillover index implying that spillovers across credit industries increase during the turbulence market situations. This last finding is also in line with our network topology analysis which indicates an increase in spillovers during times of financial stress. << Insert Table 4, 5 & 6 about here >>

4. Concluding remarks The US mortgage crisis in 2007 turned into a global financial crisis with many ramifications. The shock of this crisis affected individual assets, markets and the rest of the world. In this study, we examine the US credit markets’ connectedness using daily data from December 14, 2007 till November 13, 2015 using the spillover approach proposed by Diebold and Yilmaz (2012). This is the first study that examines spillovers across US credit markets to understand the spillover nature of each industry. The results show that majority of the industries has strong connectedness with each other. Consumer Services industry appears to be the significant transmitter, using CDS spread change, for the full sample period and during crisis situations. Basic Material industry show significant volatility transmission to the other industries specifically during global financial crisis sub-sample. Stock, bond, gold and oil market volatilities along with economic policy uncertainity are significant determinants of credit markets’ risk spillovers and the spillovers significantly increase during market turbulence indicating a possible contagion effect. It is worth mentioning that both Consumer Services and Basic Materials industries are sensitive to changes in the business cycle and thus a higher transmission from these to the other industries results from the fact that credit risk of industries depends on the state of economy. This higher 15

connectedness of Consumer Services industries with other industries can also be useful to hedge the credit risk. The finding that the connectedness between some industries is very low e.g., Utilities and Technology industries indicates possible diversification opportunities. The Utilities industry due to strong regulatory control by the policy makers is a natural monopoly and counter-cyclical industry and thus the low connectedness of Utilities with the other credit markets may provide arbitrage and risk diversification opportunities for the credit market investors. These findings on the strength of interdependencies and spillovers across credit markets provide useful insights for the bond investment hedging (on the investor side), adequate borrowing and management of debt (on the borrower side) and adequate lending practices and money pricing. A better understanding of the key drivers of the credit market spillovers may also be valuable for the policy makers as it helps them take the most appropriate policy actions to minimize the propagation of shocks across credit markets, which becomes particularly important during periods of financial turmoil.

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Shahzad, S. J. H., Nor, S. M., Mensi, W., & Kumar, R. R. (2017c). Examining the efficiency and interdependence of US credit and stock markets through MF-DFA and MF-DXA approaches. Physica A: Statistical Mechanics and its Applications, 471, 351-363. Taylor, S. (1986). Modelling Financial Time Series. Wiley, New York Wang, G. J., Xie, C., Jiang, Z. Q., & Stanley, H. E. (2016). Who are the net senders and recipients of volatility spillovers in China’s financial markets?. Finance Research Letters, 18, 255–262. Zhang, B., & Wang, P. (2014). Return and volatility spillovers between china and world oil markets. Economic Modelling, 42, 413-420. Zhou, X., Zhang, W., & Zhang, J. (2012). Volatility spillovers between the Chinese and world equity markets. Pacific-Basin Finance Journal, 20(2), 247-270.

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Figure 1: Time trend of industry-level CDS spreads over the sample period Consumer Goods

Financials

Consumer Services

Healthcare

Industrials

Technology

Basic Materials

Oil and Gas

Telecommunications

Utilities

Note: The figures show the time evolution of industry-level CDS spreads over the sample period 14 December 2007 till 13 November 2015; a total of 2066 daily observations are plotted. The light blue area indicates the 2007/09 US recession dated by the National Bureau of Economic Research (NBER). The light green area indicates 2011/13 Euro Area recession dated by Centre for Economic Policy Research (CEPR).

19

Figure 2: Rolling window total spillover index – CDS spread change

Note: This figure displays the time-varying behavior of the total returns spillover index across all industry-level credit markets. The total returns spillover index shows on average the percentage of the forecast error variance in all the series that come from spillovers. This index starts on November 28, 2008, and a 200-day rolling window is used to obtain its evolution over time.

Figure 3: Rolling window total spillover index – CDS spread volatility

Note: This figure displays the time-varying behavior of the total volatility spillover index across all industry-level credit markets. See notes to Figure 2.

20

Figure 4: Robustness analysis a). Rolling returns spillover index – different step sizes 70

h=12 h=24 h=36

60 50 40 30 20 10 0

b). Rolling returns spillover index – different rolling window sizes 80

days=150 days=200 days=250

70 60 50 40 30 20 10 0

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Figure 5: Network structure of net pairwise directional spillovers - CDS spread change Panel A: Full Sample 2007-15

Panel B: Global financial crisis 2007-09

(SOI = 15.00%) Panel C: Eurozone crisis 2011-13

(SOI = 62.60%) Panel D: Post crisis 2013-15

(SOI = 34.60%)

(SOI = 8.00%)

Note: These figures present the network plot of the pairwise directional spillovers, using change in CDS spreads from/to the industry-level credit markets. The size of the node indicates the overall magnitude of transmission/reception of spillovers for each variable. Larger node size implies higher transmission/reception of spillover effects. The color of each node indicates whether a variable is a net transmitter/receiver of spillovers. Net transmitters are in red, and net receivers are in green. The thickness of the arrows reflects the strength of the spillover between a pair of variables. Thicker arrows indicate stronger pairwise spillovers.

22

Figure 6: Net pairwise directional spillovers – CDS spread volatilities Panel A: Full Sample 2007-15

Panel B: Global financial crisis 2007-09

(SOI = 10.60%) Panel C: Eurozone crisis 2011-13

(SOI = 45.00%) Panel D: Post crisis 2013-15

(SOI = 24.40%)

(SOI = 9.20%)

Note: These figures present the network plot of the pairwise directional spillovers, using CDS spreads’ volatilities, from/to the industry-level credit markets. See notes to Figure 5.

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Table 1: Descriptive statistics of industry-level CDS spreads. Industry Consumer Goods Financials Consumer Services Healthcare Industrials Technology Basic Materials Oil and Gas Telecommunications Utilities

Standard Mean deviations J-B 142.87 46.940 648.48*** 345.35 167.55 1298.95*** 288.34 167.06 7770.24*** 323.43 135.71 1098.96*** 168.85 120.61 14219.26*** 180.01 102.07 10473.48*** 302.00 179.35 12057.20*** 147.25 66.770 1497.49*** 226.65 101.64 1150.84*** 225.31 147.88 712.09***

ADF Level First difference -1.798 -28.64*** -1.917 -40.58*** -1.950 -14.32*** -1.863 -43.15*** -2.261 -23.13*** -2.347 -14.95*** 0.278 -10.50*** -2.074 -8.00*** -1.924 -22.10*** -2.969 -19.70***

Note: The table reports the descriptive statistics of the variables. J-B stands for Jarque-Bera test of normality. ADF are the empirical statistics of the Augmented Dickey-Fuller (1979) unit root test. *** indicates significance at 1% level.

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Table 2: Full sample spillover analysis – CDS spread change CSMG Consumer Goods (CSMG) Financials (FIN) Consumer Services (SVS) Healthcare (HC) Industrials (INDST) Technology (TECH) Basic Materials (BM) Oil and Gas (O&G) Telecommunications (TCOM) Utilities (UTIL) Contribution to others From Others Net (To - From) Contribution including own

FIN

SVS

HC

INDST

TECH

BM

O&G

TCOM

UTIL

From Others

77.0 2.2

2.1 90.0

2.1 1.7

2.7 1.3

1.4 0.7

3.1 1.1

2.7 0.8

2.7 0.3

5.8 1.6

0.6 0.4

23.0 10.0

2.4 2.6 1.5 3.5 2.8 2.8

1.0 1.5 0.8 1.1 0.9 0.2

85.6 3.5 1.0 1.7 1.0 4.2

3.4 80.3 1.1 2.8 2.4 1.6

0.6 0.9 87.1 1.5 0.9 2.4

1.9 1.7 1.5 83.7 1.6 0.9

0.9 1.9 0.5 1.4 87.5 1.1

1.0 1.4 2.1 0.9 1.2 85.7

3.1 6.0 3.9 3.3 1.6 1.0

0.1 0.2 0.4 0.1 0.2 0.1

14.0 20.0 13.0 16.0 13.0 14.0

5.6 0.7 24.0 23.0 1.0

1.7 0.3 10.0 10.0 0.0

2.8 0.2 18.0 14.0 4.0

6.2 0.3 22.0 20.0 2.0

3.1 0.2 12.0 13.0 -1.0

2.9 0.1 15.0 16.0 -1.0

1.3 0.2 11.0 13.0 -2.0

0.8 0.1 10.0 14.0 -4.0

75.4 0.4 27.0 25.0 2.0

0.4 25.0 97.4 3.0 2.0 Total 3.0 Spillover Index -1.0

101

100

104

102

99

99

98

96

102

100

15.00%

Note: The table presents the percentage of contribution to the forecast error variance of variable i coming from shocks to variable j using the entire sample period. The column titled “Contribution from others” is the sum of the percentage of contribution of each variable except the given variable. Similarly, the row named “Contribution to others” is the sum of the percentage of contribution of each variable except the given variable. The total spillover index - SOI (expressed as a percentage) appears in the lower right corner of the table. This index is calculated as the sum of all the contributions in the “Contribution to others” column (or the sum of the entire “Contribution to others” row) divided by the number of variables included in the model. The net directional spillover is computed as the difference between the “Contribution to others” and the “Contribution from others” for each variable, i.e., a variable is net transmitter (receiver) when the value is positive (negative). In the above table, CSMG means consumer goods, FIN means financials, SVS means consumer services, HC means health care, INDST means industrials, TECH means technology, BM means basic materials, O&G means oil and Gas, TCOM means telecommunications, UTIL means utilities.

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Table 3: Full sample spillover analysis – CDS volatility CSMG Consumer Goods (CSMG) Financials (FIN) Consumer Services (SVS) Healthcare (HC) Industrials (INDST) Technology (TECH) Basic Materials (BM) Oil and Gas (O&G) Telecommunications (TCOM) Utilities (UTIL) Contribution to others From Others Net Contribution including own

FIN

SVS

HC

INDST

TECH

BM

OG

From Others

TELECOM UTL

88.3 2.0

2.2 91.8

0.2 0.1

1.2 0.4

1.1 1.9

2.4 0.4

1.3 0.2

2.3 1.5

0.8 1.4

0.3 0.4

12.0 8.0

0.8 0.8 0.1 2.6 0.9 0.8

0.1 0.3 1.7 0.4 0.0 1.6

86.9 1.3 0.1 1.1 0.0 16.4

4.0 93.3 1.0 1.9 2.8 1.8

0.4 0.3 93.6 0.4 0.1 0.7

0.3 0.4 0.1 90.2 3.5 0.2

0.3 1.9 0.0 1.9 91.7 0.7

7.1 0.5 1.6 0.5 0.3 77.3

0.1 0.9 1.5 0.7 0.5 0.4

0.0 0.2 0.1 0.1 0.2 0.1

13.0 7.0 6.0 10.0 8.0 23.0

3.2 2.2 13.0 12.0 1.0

1.0 0.4 8.0 8.0 0.0

0.6 0.3 20.0 13.0 7.0

2.9 0.1 16.0 7.0 9.0

1.9 0.9 8.0 6.0 2.0

2.3 0.1 10.0 10.0 0.0

0.5 0.0 7.0 8.0 -1.0

1.5 0.2 15.0 23.0 -8.0

85.8 1.2 7.0 14.0 -7.0

102

100

107

109

101

100

99

93

93

Note: See notes to Table 2.

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0.3 14.0 94.7 5.0 2.0 Total 5.0 Spillover Index -3.0 96

10.60%

Table 4: Statistical properties of the overall spillover index (SOI), industry-wise net directional spillovers and explanatory variables Mean Maximum Minimum Std. Dev. a). Total spillovers and industry-wise net directional spillovers – CDS changes SOI 45.46 78.46 14.36 19.15 Consumer Goods -0.70 26.11 -21.73 8.72 Financials -1.28 18.43 -18.96 7.67 Consumer Services 19.47 44.70 -14.32 9.93 Healthcare 1.09 32.51 -32.75 12.82 Industrials -2.96 32.94 -29.73 10.10 Technology -7.47 23.78 -35.49 10.90 Basic Materials -2.22 47.78 -30.52 10.29 Oil and Gas 3.89 87.71 -26.20 14.85 Telecommunications -1.61 16.75 -26.20 8.26 Utilities -8.28 22.62 -34.66 10.24 b). Total spillovers and industry-wise net directional spillovers – CDS volatilities SOI 35.71 62.10 15.31 12.01 Consumer Goods 14.69 80.14 -23.97 19.03 Financials -1.78 26.18 -32.40 10.68 Consumer Services -19.40 40.21 -46.78 15.09 Healthcare 18.36 98.86 -55.69 27.81 Industrials 4.91 118.48 -40.95 23.19 Technology 4.75 74.16 -25.73 16.08 Basic Materials 4.90 109.46 -30.78 20.97 Oil and Gas -5.22 105.50 -45.32 11.63 Telecommunications -8.90 57.59 -56.46 17.66 Utilities -12.32 6.48 -43.26 10.02 c). Explanatory variables VIX 21.60 80.86 10.32 10.81 EPU 121.99 626.03 3.32 72.53 MOVE 91.03 264.60 48.90 34.53 OVX 36.74 100.42 14.50 15.44 GVX 21.57 64.53 11.97 7.90

ADF -2.99*** -3.26*** -3.32*** -3.23*** -2.25*** -2.53*** -2.95*** -3.50*** -2.41*** -3.24*** -3.21*** -2.53*** -2.44*** -4.11*** -3.02*** -2.23*** -2.05*** -3.00*** -3.46*** -4.80*** -2.94*** -3.22*** -3.62*** -7.61*** -3.28*** -2.43*** -3.76***

Note: This table reports the descriptive statistics and unit root properties of the overall spillover index, net directional spillovers and explanatory variables of these spillovers. ADF are the empirical statistics of the Augmented Dickey-Fuller (1979) unit root test. The net directional spillovers are the spillovers from a specific sector to all other sectors. Positive (negative) values indicate that the corresponding sector is in net terms a transmitter (receiver) of spillover effects to all other sectors. Each index starts on November 28, 2008, and a 200-day rolling window is used to obtain its evolution over time. *** indicates significance at 1% level. SOI = spillover index; VIX = CBOE Volatility Index; EPU = economic policy uncertainty; MOVE = Merrill lynch Option Volatility Estimate; OVX = CBOE Crude Oil Volatility Index; GVX = CBOE/COMEX Gold Volatility Index.

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Table 5: Determinants of total and net directional spillovers – CDS spread change VIX SOI 0.937*** (0.083) Consumer Goods -0.209*** (0.045) Financials 0.128*** (0.037) Consumer Services -0.088* (0.047) Healthcare 0.113** (0.055) Industrials 0.395*** (0.051) Technology -0.300*** (0.052) Basic Materials -0.033 (0.044) Oil and Gas 0.504*** (0.081) Telecommunications -0.357** (0.041) Utilities -0.128** (0.051)

EPU 0.040*** (0.005) -0.028*** (0.003) 0.018*** (0.002) 0.023*** (0.003) 0.041*** (0.003) 0.012*** (0.003) -0.024*** (0.003) -0.025*** (0.003) 0.005 (0.005) -0.014*** (0.003) -0.007** (0.003)

MOVE 0.350*** (0.020) 0.045*** (0.011) 0.124*** (0.009) 0.146*** (0.012) 0.025* (0.014) -0.003 (0.013) -0.141*** (0.013) -0.040*** (0.011) 0.137*** (0.020) -0.067*** (0.010) -0.230*** (0.013)

OVX -0.525*** (0.039) 0.206*** (0.022) -0.085*** (0.018) 0.054** (0.022) -0.234*** (0.026) 0.042* (0.025) 0.016 (0.025) 0.186*** (0.021) -0.341*** (0.039) 0.050** (0.020) 0.111*** (0.024)

GVX -0.173* (0.090) 0.293*** (0.049) -0.395*** (0.040) 0.225*** (0.051) -0.351*** (0.059) 0.070 (0.056) 0.091* (0.056) -0.096** (0.047) -0.336*** (0.088) 0.543*** (0.045) -0.080 (0.055)

DM1 -11.760*** (1.749) -6.860*** (0.957) 4.024*** (0.780) -5.139*** (0.990) -18.343*** (1.160) -10.278*** (1.087) 6.587*** (1.096) 16.953*** (0.924) -3.049* (1.709) -6.892*** (0.870) 23.147*** (1.074)

DM2 -0.411 (0.869) 8.659*** (0.476) -4.415*** (0.388) 5.518*** (0.492) 1.736*** (0.576) 8.376*** (0.540) 1.734*** (0.545) -9.069*** (0.459) -9.265*** (0.849) 1.820*** (0.432) -5.115*** (0.534)

Constant Adj. R2 12.811*** 0.467 (1.539) -11.889*** 0.230 (0.843) -5.383*** 0.339 (0.687) -2.162** 0.365 (0.872) 9.200*** 0.477 (1.021) -16.385*** 0.260 (0.957) 11.185*** 0.354 (0.965) 0.564 0.484 (0.814) 1.911 0.153 (1.505) 0.696*** 0.290 (0.766) 12.454*** 0.297 (0.946)

Note: This table shows the regression estimates of total returns spillover and industry-wise net directional returns spillovers. Numbers in brackets are standard errors. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively. SOI = spillover index; VIX = CBOE Volatility Index; EPU = economic policy uncertainty; MOVE = Merrill lynch Option Volatility Estimate; OVX = CBOE Crude Oil Volatility Index; GVX = CBOE/COMEX Gold Volatility Index. DM1 = Dummy Variable for the global financial crisis (2007-09) according to National Bureau of Economic Research (NBER) dating; DM2 = Dummy Variable for the Euro Area recession (2011-13) according to Centre for Economic Policy Research (CEPR) dating. Crisis period takes the value 1 and 0 otherwise.

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Table 6: Determinants of total and net directional spillovers – CDS spread volatility VIX *** 0.859 SOI (0.054) Consumer Goods 1.039*** (0.115) Financials 0.186*** (0.055) Consumer Services -0.606*** (0.079) Healthcare 0.937*** (0.140) Industrials -0.229* (0.096) Technology 0.762*** (0.087) Basic Materials -0.548*** (0.080) Oil and Gas -0.130*** (0.072) Telecommunications -0.702*** (0.106) *** -0.710 Utilities (0.048)

EPU 0.028*** (0.003) 0.013* (0.007) 0.016*** (0.003) -0.034*** (0.005) 0.098*** (0.008) -0.034*** (0.006) 0.009* (0.005) -0.051*** (0.005) -0.043*** (0.004) 0.029*** (0.006) -0.002 (0.003)

MOVE 0.162*** (0.013) 0.229*** (0.027) 0.081*** (0.013) -0.106*** (0.019) 0.333*** (0.033) -0.018 (0.022) -0.084*** (0.020) -0.028 (0.019) 0.043** (0.017) -0.259*** (0.025) -0.190*** (0.011)

Note: See notes to Table 5.

29

OVX -0.313*** (0.025) -0.234*** (0.052) -0.101*** (0.025) 0.752*** (0.036) -1.445*** (0.063) 0.907*** (0.044) -0.749*** (0.040) 0.515*** (0.036) 0.036 (0.033) 0.212*** (0.048) 0.108*** (0.022)

GVX 0.077 (0.058) -0.854*** (0.121) -0.630*** (0.058) -0.697*** (0.084) 0.198 (0.148) -0.751*** (0.102) 0.599*** (0.092) 0.814*** (0.084) -0.016 (0.076) 0.781*** (0.112) 0.556*** (0.050)

DM1 -5.244*** (1.057) -29.451*** (2.224) 23.421*** (1.058) -7.609*** (1.533) -21.238*** (2.712) -0.983 (1.861) -16.049*** (1.688) 42.276*** (1.548) 5.968*** (1.402) 0.710 (2.053) 2.955*** (0.925)

DM2 2.056*** (0.522) 0.893 (1.099) -0.890* (0.523) -7.384*** (0.757) -27.538*** (1.340) 40.291*** (0.919) -5.983*** (0.834) -5.044*** (0.765) 6.574*** (0.693) 0.100 (1.014) -1.019** (0.457)

Constant Adj. R2 10.088*** 0.516 (1.015) 0.005 0.148 (2.134) 0.315 0.388 (1.015) -3.783** 0.356 (1.471) 13.457*** 0.406 (2.603) -10.129*** 0.598 (1.786) 12.338*** 0.312 (1.620) -13.097*** 0.660 (1.485) -4.096*** 0.092 (1.345) 1.075 0.156 (1.970) 3.916*** 0.467 (0.888)