Network Topology of Dynamic Credit Default Swap Curves of Energy Firms and the Role of Oil Shocks

Abstract

Using network analysis on the connectedness of default factors in a credit default swap (CDS) dataset of U.S. and European energy firms, we provide the first evidence of differences in the shape and dynamics of the interconnectedness of the level, slope, and curvature, representing long-, short- and middle-term default factors, respectively. The interconnectedness of the three default factors increases during the European sovereign debt crisis (ESDC), whereas only the interconnectedness of the level factor increases during the oil price crash, and the interconnectedness of both level and slope factors spikes during COVID19. European firms contribute more to the transmission of long-term and short-term default risk from early 2011 till the beginning of the 2014–2105 oil price crash; afterwards, U.S. firms are major default transmitters despite some periods of parity with European firms. The impacts of oil demand and supply shocks on the various interconnectedness are quantile-dependent and more pronounced in the long term for the credit risk of the energy firms.

Keywords

Energy firms CDS curves Default risk Dynamic Nelson-Siegel (DNS) network analysis Connectedness Oil shocks COVID19

References

Balcilar

Hammoudeh

Toparli

E.A.

(2018). “On the risk spillover across the oil market, stock market, and the oil related CDS sectors: A volatility impulse response approach.” Energy Economics 74: 813–827. https://doi.org/10.1016/j.eneco.2018.07.027.

Balcilar

Ozdemir

Z.A.

Ozdemir

Wohar

M.E.

(2020). “Spillover effects in oil-related CDS markets during and after the sub-prime crisis.” The North American Journal of Economics and Finance 54:101249. https://doi.org/10.1016/j.najef.2020.101249.

Barfuss

Massara

G.P.

Di Matteo

Aste

(2016). “Parsimonious modeling with information filtering networks.” Physical Review E 94(6): 062306. https://doi.org/10.1103/PhysRevE.94.062306.

Bouri

Kachacha

Roubaud

(2020). “Oil market conditions and sovereign risk in MENA oil exporters and importers.” Energy Policy 137:111073. https://doi.org/10.1016/j.enpol.2019.111073.

Bouri

Shahzad

S.J.H.

Raza

Roubaud

(2018). “Oil volatility and sovereign risk of BRICS.” Energy Economics 70: 258–269. https://doi.org/10.1016/j.eneco.2017.12.018.

Christensen

A.P.

Kenett

Y.N.

Aste

Silvia

P.J.

Kwapil

T.R.

(2018). “Network structure of the Wisconsin Schizotypy Scales–Short Forms: Examining psychometric network filtering approaches.” Behavior Research Methods 50(6): 2531–2550. https://doi.org/10.3758/s13428-018-1032-9.

Dai

Serletis

(2018). “Oil Price Shocks and the Credit Default Swap Market.” Open Economies Review 29(2): 283–293. https://doi.org/10.1007/s11079-017-9454-z.

Demirer

Ferrer

Shahzad

S.J.H.

(2020). “Oil price shocks, global financial markets and their connectedness.” Energy Economics 88:104771. https://doi.org/10.1016/j.eneco.2020.104771.

Diebold

F.X.

(2006). “Forecasting the term structure of government bond yields.” Journal of Econometrics 130(2): 337–364. https://doi.org/10.1016/j.jeconom.2005.03.005.

10.

Diebold

F.X.

Yilmaz

(2012). “Better to give than to receive: Predictive directional measurement of volatility spillovers.” International Journal of Forecasting 28(1): 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006.

11.

Domanski

Kearns

Lombardi

M.J.

Shin

H.S.

(2015). “Oil and debt.” BIS Quarterly Review 55–65.

12.

Doshi

Elkamhi

Ornthanalai

(2018). “The Term Structure of Expected Recovery Rates.” Journal of Financial and Quantitative Analysis 53(6): 2619–2661. https://doi.org/10.1017/S0022109018000558.

13.

Geng

J.B.

Y.J.

Zhang

(2021). “Modeling return and volatility spillover networks of global new energy companies.” Renewable and Sustainable Energy Reviews 135:110214. https://doi.org/10.1016/j.rser.2020.110214.

14.

Hammoudeh

Liu

Chang

C.L.

McAleer

(2013). “Risk spillovers in oil-related CDS, stock and credit markets.” Energy Economics 36: 526–535. https://doi.org/10.1016/j.eneco.2012.10.010.

15.

Han

Linton

Oka

Whang

Y.J.

(2016). “The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series.” Journal of Econometrics 193(1): 251–270. https://doi.org/10.1016/j.jeconom.2016.03.001.

16.

Han

Zhou

(2011). “Term structure of credit default swap spreads and cross-section of stock returns.” McCombs Research Paper Series No. FIN-01-11, University of Texas at Austin and Florida State University. https://doi.org/10.2139/ssrn.1735162.

17.

Huang

A.Y.

Shen

C.H.

Chen

C.C.

(2011). “The Impact of Major Events from the Recent FinancialCrisis on Credit Default Swaps.” The Journal of Fixed Income 21(3): 31–43. https://doi.org/10.3905/jfi.2012.21.3.031.

18.

Jiang

Liu

Xie

(2021). “Oil price shocks and credit spread: Structural effect and dynamic spillover.” The North American Journal of Economics and Finance 58:101467. https://doi.org/10.1016/j.najef.2021.101467.

19.

Kerste

Gerritsen

Weda

Tieben

(2015). “Systemic risk in the energy sector—Is there need for financial regulation?” Energy policy 78: 22–30. https://doi.org/10.1016/j.enpol.2014.12.018.

20.

Kilian

(2009). “Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market.” American Economic Review 99(3): 1053–69. https://doi.org/10.1257/aer.99.3.1053.

21.

Leccadito

Tunaru

R.S.

Urga

(2015). “Trading strategies with implied forward credit default swap spreads.” Journal of Banking & Finance 58: 361–375. https://doi.org/10.1016/j.jbankfin.2015.04.018.

22.

Lee

Naranjo

Velioglu

(2018). “When do CDS spreads lead? Rating events, private entities, and firm-specific information flows.” Journal of Financial Economics 130: 556–578. https://doi.org/10.1016/j.jfineco.2018.07.011.

23.

Mantegna

R.N.

(1999). “Hierarchical structure in financial markets.” The European Physical Journal B-Condensed Matter and Complex Systems 11(1): 193–197. https://doi.org/10.1007/s100510050929.

24.

Massara

G.P.

Di Matteo

Aste

(2017). “Network filtering for big data: Triangulated maximally filtered graph.” Journal of Complex Networks 5(2): 161–178. https://doi.org/10.1093/comnet/cnw015.

25.

Moch

(2018). “The contribution of large banking institutions to systemic risk: What do we know? A literature review.” Review of Economics 69(3): 231–257. https://doi.org/10.1515/roe-2018-0011.

26.

Naifar

Shahzad

S.J.H.

(2021). “Tail event-based sovereign credit risk transmission network during COVID-19 pandemic.” Finance Research Letters 45:102182. https://doi.org/10.1016/j.frl.2021.102182.

27.

Nelson

C.R.

Siegel

A.F.

(1987). “Parsimonious modeling of yield curves.” Journal of Business, 60(4): 473–489. https://doi.org/10.1086/296409.

28.

Pan

Singleton

K.J.

(2008). “Default and Recovery Implicit in the Term Structure of Sovereign CDS Spreads.” The Journal of Finance 63(5): 2345–2384. https://doi.org/10.1111/j.1540-6261.2008.01399.x.

29.

Pavlova

De Boyrie

M.E.

Parhizgari

A.M.

(2018). “A dynamic spillover analysis of crude oil effects on the sovereign credit risk of exporting countries.” The Quarterly Review of Economics and Finance 68: 10–22. https://doi.org/10.1016/j.qref.2018.03.003.

30.

Ready

R.C.

(2018). “Oil prices and the stock market.” Review of Finance 22(1): 155–176. https://doi.org/10.1093/rof/rfw071.

31.

Restrepo

Uribe

J.M.

Manotas

(2018). “Financial risk network architecture of energy firms.” Applied Energy 215: 630–642. https://doi.org/10.1016/j.apenergy.2018.02.060.

32.

Sengupta

Marsh

W.B.

Rodziewicz

(2017). “Do Adverse Oil-Price Shocks Change Loan Contract Terms for Energy Firms?” Federal Reserve Bank of Kansas City, Economic Review, 102(4): 59–85. https://doi.org/10.18651/ER/4q17senguptamarshrodziewicz.

33.

Shahzad

S.J.H.

Nor

S.M.

Ferrer

Hammoudeh

(2017). “Asymmetric determinants of CDS spreads: US industry-level evidence through the NARDL approach.” Economic Modelling 60: 211–230. https://doi.org/10.1016/j.econmod.2016.09.003.

34.

Sharma

S.S.

Thuraisamy

(2013). “Oil price uncertainty and sovereign risk: Evidence from Asian economies.” Journal of Asian Economics 28: 51–57. https://doi.org/10.1016/j.asieco.2013.06.001.

35.

Szafranek

Kwas

Szafrański

Wośko

(2020). “Common Determinants of Credit Default Swap Premia in the North American Oil and Gas Industry. A Panel BMA Approach.” Energies 13(23): 6327. https://doi.org/10.3390/en13236327.

36.

Tumminello

Aste

Di Matteo

Mantegna

R.N.

(2005). “A tool for filtering information in complex systems.” Proceedings of the National Academy of Sciences 102(30): 10421–10426. https://doi.org/10.1073/pnas.0500298102.

37.

Wegener

Basse

Kunze

von Mettenheim

H.J.

(2016). “Oil prices and sovereign credit risk of oil producing countries: an empirical investigation.” Quantitative Finance 16(12): 1961–1968. https://doi.org/10.1080/14697688.2016.1211801.

38.

Zhang

(2021). “Systemic risk and financial contagion across top global energy companies.” Energy Economics 97:105221. https://doi.org/10.1016/j.eneco.2021.105221.

39.

Chen

C.Y.H.

Härdle

W.K.

(2019). “Dynamic credit default swap curves in a network topology.” Quantitative Finance 19(10): 1705–1726. https://doi.org/10.1080/14697688.2019.1585560.

40.

Zhu

Lin

Liu

(2020). “Magnitude and persistence of extreme risk spillovers in the global energy market: A high-dimensional left-tail interdependence perspective.” Energy Economics 89:104761. https://doi.org/10.1016/j.eneco.2020.104761.