Credit Risk Determinants of Insurance Companies - Centro de Finanzas

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Our data set is composed of end of quarter CDS quotes obtained from .... include the risk-‐free rate, the yield slope,
Credit  Risk  Determinants  of  Insurance  Companies*     LILIANA  GONZALEZ     ESSEC  Business  School     LORENZO  NARANJOΏ   ESSEC  Business  School       March,  2014   ABSTRACT   This  paper  investigates   the  determinants  of  credit  risk  in  insurance  companies  in  the  U.S.  and   Europe.   Consistent   with   recent   results   for   non-­‐financial   firms   in   the   U.S.,   we   find   that   equity   volatility   is   a   major   determinant   and   predictor   of   CDS   spreads   for   both   U.S.   and   European   insurers,   even   after   controlling   for   the   composition   of   their   investment   portfolios   and   other   firm-­‐specific   characteristics   such   as   leverage   and   macro   controls.   Furthermore,   we   find   macroeconomic  factors  to  affect  the  credit  risk  of  European  but  not  U.S.  insurers,  whereas  cash   holdings  seem  to  be  relevant  in  explaining  the  credit  spreads  of  U.S.  insurance  companies.  We   find   that   cash  holdings   and   credit   spreads   of  U.S.   insurers   are  positively   correlated.   However,   the   availability   of   cash   reduces   the   credit   risk   of   firms   experiencing   positive   solvency   shocks.   Overall,   our   results   are   economically   significant   and   suggest   that   equity   and   credit   markets   incorporate  quickly  relevant  information  on  the  creditworthiness  of  large  insurers.   Keywords:  Insurance  Companies,  Credit  Risk,  Credit  Default  Swaps,  Financial  Crisis   JEL  Codes:  G11,  G12,  G22

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 We  would  like  to  thank  Andras  Fulop  and  Rik  Sen  for  helpful  comments  and  suggestions.  We  also  thank  the  ESSEC   Research  Center  for  financial  support.   Ώ  Corresponding  Author:  Avenue  Bernard  Hirsch,  95000  Cergy,  France.  Email:  [email protected]  

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Introduction  

The   subprime   crisis   that   started   in   2007   put   the   financial   sector   at   risk.   Because   of   their   leverage,   insurance   companies   endured  severe   market   stress,   and   major   insurers   such   as   AIG   had   to   be   rescued   by   the   U.S.   government   as   they   were   considered   ͞ƚŽŽ ďŝŐ ƚŽ ĨĂŝů.͟   The   financial  crisis  of  2007-­‐08  proved  that  major  insurers  can  be  the  source  of  financial  fragility  and   systemic   risk   at   the   global   level.   The   situation   has   created   pressure   on   both   academics   and   regulators  to  understand  the  causes  of  such  credit  events.  However,  little  work  has  been  done   so  far  in  understanding  the  drivers  of  credit  risk  for  large  insurance  corporations.   We   contribute   to   this   debate   by   analyzing   the   determinants   of   credit   spreads   for   insurance   companies   in   the   U.S.   and   Europe.   Past   work   on   the   credit   risk   of   insurance   companies   has   mostly   focused   ŝŶ ƉƌĞĚŝĐƚŝŶŐ ĚĞĨĂƵůƚƐ ƵƐŝŶŐ ĨŝŶĂŶĐŝĂů ĐŚĂƌĂĐƚĞƌŝƐƚŝĐƐ Žƌ ĞƐƚ͛Ɛ ƌĂƚŝŶŐƐ 1,   but   no   studies   have   analyzed   the   risk   of   the   debt   issued   by   large   insurers.   Moreover,   even   though   some   authors   have   analyzed   the   determiŶĂŶƚƐ ŽĨ ďĂŶŬƐ͛ ĐƌĞĚŝƚ ƐƉƌĞĂĚƐ ;e.g.   Annaert   et   al.,   2013),   to   the   best   of   our   knowledge   we   are   the   first   to   look   at   the   determinants   of   credit   spreads   of   insurance   companies.   Finally,   existing   research   on   CDS   spreads   for   non-­‐financial   firms  has  studied  mostly  the  U.S.  market  (e.g.  Zhang  et  al.,  2009),  while  research  on  banks  has   focused  mostly  on  Europe  (e.g.  Annaert  et  al.,  2013).  In  this  paper  we  look  both  at  major  U.S.   and  European  insurers  together.    

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 See  for  example  Trieschmann  and  Pinches  (1973),  Shaked  (1985),  Ambrose  and  Carroll  (1994),  Carson  and  Hoyt   (1995),  and  Lee  and  Urrutia  (1996).  

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Insurance   companies   possess   firm-­‐specific   characteristics   that   differentiate   them   from   other   firms  in  the  economy,  such  as  the  structure  of  their  balance  sheet,  the  regulatory  environment,   and   the   way   in   which   they   transfer   risk   to   other   sectors   of   the   economy.   As   many   financial   firms,  insurance  companies  operate  under  high  leverage  as  the  result  of  their  large  fraction  of   insurance   liabilities.   The   resulting   leverage   on   the   balance   sheet   makes   their   debt   risky,   increasing  the  possibility  of  default  and  bankruptcy.  Such  risks  are  usually  exacerbated  in  times   of   market   stress.   As   a   consequence,   the   cost   of   debt   is   a   widely   used   indicator   to   assess   the   financial   health   of   insurers.   As   such,   we   believe   that   it   is   important   to   understand   the   main   drivers  of  credit  risk.   Our  main  findings  can  be  summarized  as  follows.  First,  we  find  that  that  equity  volatility  is  the   most  important  determinant  and  predictor  of  credit  spreads  for  U.S.  and  European  insurers,  a   result  that  is  consistent  with  recent  findings  for  CDS  spreads  of  U.S.  firms  (Zhang  et  al.,  2009).   We   believe   that   this   result   is   relevant   for   investment   professionals   and   macro-­‐prudential   regulators,   because   it   suggest   an   additional   simple   tool   to   monitor   and   assess   the   financial   health   of   large  insurers.   From   a   market  efficiency   perspective,   this   finding   suggests   that   both   debt   and   equity   markets   quickly   incorporate   relevant   information   on   credit   events   for   such   companies.  The  effect  we  uncover  is  also  economically  significant.  According  to  our  estimates,  a   one  standard  deviation  increase  in  the  firm  equity  volatility  can  increase  CDS  spreads  by  around   1.5%  for  U.S.  insurers  and  by  0.9%  for  European  insurers.   The   fact   that   volatility   affects   CDS   spreads   of   insurance   companies   challenges   the   commonly   held  view  that  variables  that  are  known  to  explain  credit  spreads  of  non-­‐financial  firms  usually  

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lose  their  explanatory  power  when  applied  to  financial  firms  (see  e.g.  Boss  and  Scheicher,  2002;   Raunig   and   Scheicher,   2009;   Grammatikos   and   Vermeulen,   2012).   For   example,   research   on   European  banks  by  Annaert  et  al.  (2013)  finds  no  direct  relation  between  firm-­‐specific  volatility   and  credit  spreads.     Furthermore,   we   find   that   the   ratios   of   debt   and   insurance   liabilities   to   total   assets,   respectively,   and   the   distance-­‐to-­‐default   measure   of   Vassalou   and   Xing   (2004),   are   also   important   determinants   of   credit   spreads   for   insurance   companies.   The   results   hold   after   controlling   for   the   composition   of   their   portfolio   investments,   and   when   we   separate   the   sample   between   U.S.   and   European   insurance   companies.   Since   the   effect   of   volatility   holds   after   controlling   for   leverage,   this   provides   indirect   evidence   that   the   volatility   of   assets   is   relevant  in  explaining  the  level  of  credit  risk  for  insurance  entities.   We   also   uncover   differences   between   the   credit   spread   determinants   of   U.S.   and   European   insurers.   First,   we   find   that   macroeconomic   factors   such   as   the   risk-­‐free   rate   and   the   swap   spread   are   important   in   explaining   the   credit   spreads   of   European   insurers.   Hence,   credit   spreads   of   European   insurers   comove   more   with   the   business   cycle   than   spreads   of   U.S.   insurers.   Second,   we   uncover   a   strong   positive   correlation   between   cash   reserves   and   credit   spreads   for   U.S.   insurers.   This   positive   comovement   is   consistent   with   the   recent   findings   of   Acharya   et   al.   (2012)   who   show   that   companies   holding   cash   at   optimal   levels   will   do   so   for   precautionary  motives.     We   argue   in   the   paper   that   regulation   might   explain   why   cash   holdings   are   more   relevant   in   explaining  the  variation  of  CDS  spreads  for  U.S.  insurers.  In  the  U.S.,  insurance  companies  are  

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required   to   maintain   their   adjusted   capital   above   a   minimum   required   level   that   depends   on   the   risk   of   their   assets   and   their   insurance   liabilities.   In   contrast,   under   the   E.U.   directive   Solvency  I,  which  was  still  under  place  at  the  end  of  our  sample,  insurers  are  required  to  keep   their   adjusted   capital   above   a   regulatory   level   that   depends   on   futurĞ ƉƌĞŵŝƵŵ ĂŶĚ ĐůĂŝŵƐ͛ liabilities.   As   a   consequence,   we   can   expect   cash   holdings   of   U.S.   insurers   to   be   more   informative   about   their   credit   condition  than   cash   reserves   of   European   insurers,   and   to   vary   more  in  response  to  market  events.  Empirically,  we  observe  that  U.S.  insurers  hold  on  average   four  times  less  cash  to  total  asset  than  European  insurance  companies,  and  that  cash  reserves   for  U.S.  insurers  vary  more  over  time  than  the  ones  of  their  European  counterparts.     To   gain   further   understanding   on   the   role   that   cash   holdings   play   for   U.S.   insurers,   we   study   how  credit  spreads  react  for  firms  that  hold  a  larger  proportion  of  cash  after  they  experience  an   unexpected  improvement  in  their  solvency.  To  achieve  this,  we  use  a  differences-­‐in-­‐differences   approach   in   which   we   perform   a   cross-­‐sectional   comparison   of   cash   holdings   of   firms   which   have   unexpectedly   improved   their   financial   position   compared   to   those   who   have   not.   Our   results   are   robust   to   several   specifications,   and   confirm   the   intuition   that   companies   holding   more  cash  become  safer  if  their  financial  health  suddenly  improves.     The   rest   of   the  paper  is  organized   as   follows.   In   Section   2   we   describe   the   data  and   variables   that  we  use  in  the  empirical  analysis.  In  Section  3  we  analyze  the  determinants  of  CDS  spreads   for   U.S.   and   European   insurance   firms.   Section   4   analyzes   in  detail   the   relation  between   cash   holdings  and  credit  risk  of  U.S.  insurance  companies.  Section  5  concludes.  

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Data  and  Variables  

2.1   Data  Sources   Our  data  sample  covers  the  period  from  July  2002  to  June  2012.  We  collect  quarterly  data  on   CDS   spreads   and   balance   sheet   information   for   twelve   U.S.   insurance   companies   and   eight   European   insurers.   The   data   is   obtained   from   Bloomberg,   Compustat   and   official   regulatory   filings.   To   measure   credit   risk,   we   focus  on   credit-­‐default   swaps   (CDS)   spreads   rather   than   corporate   bond  yields.  The  CDS  is  an  agreement  in  which  the  seller  of  the  contract  will  compensate  the   buyer  in  the  event  of  a  credit  event.  The  buyer  of  the  CDS  makes  a  series  of  periodic  payments   to  the  seller  and,  in  exchange,  receives  compensation  if  the  underlying  security  defaults.  Such   periodic  payments  are  called  the  spread  of  the  contract.  In  the  case  of  corporate  debt,  investors   use  default  swaps  to  express  their  views  about  the  creditworthiness  of  the  firm,  and  to  protect   themselves  in  the  event  of  default,  debt  restructuring,  or  a  drop  in  the  credit  rating.   Even   though   in   a   frictionless   world   CDS   and   bond   spreads   should   be   closely   related   to   each   other  (Duffie  and  Singleton,  1999),  in  practice  we  observe  significant  differences  that  are  due  in   part   to   the   illiquidity   of   corporate   bonds   (Sarig   and   Warga,   1989;   Chen   et   al.,   2007),   and   different  tax  treatments  of  coupon  payments  (Elton  et  al.,  2001).  Furthermore,  by  focusing  on   CDS   rather   than   bond   spreads   we   avoid   the   problem   of   arbitrarily   choosing   the   risk-­‐free   benchmark   (Houweling   and   Vorst,   2005).   Finally,   CDS   spreads   react   more   quickly   to   new   information   compared   to   corporate   bond   yields   (Hull   et   al.,   2004;   Blanco   et   al.,   2005;   Zhu,   2006),   seem   to   anticipate   changes   in   corporate  bond   ratings   (Norden   and   Weber,   2004),   and  

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incorporate   information   faster   than   bond   yields   in   periods   of   market   stress   (Delatte   et   al.,   2012).   Our   dataset   is   limited   because   of   CDS   data   availability.   For   the   U.S.,   there   are   28   insurance   companies   in   the   S&P   500   among   which   only   12   have   sufficient   data   on   CDS   spreads.   For   Europe,   we   select   insurance   companies   that   are   traded   in   the   most   representative   indexes   of   each  country.  Among  these  insurance  companies,  we  keep  only  firms  having  enough  CDS  data.     Table   I   presents   the   list   of   insurance   companies   that   we   use   in   the   paper   and   summarizes   relevant   balance   sheet   information.   The   table   presents   time-­‐series   averages   of   assets   (total   assets,  cash  and  investments)  and  liabilities  (insurance  liabilities,  debt  and  equity)  for  insurance   companies  in  the  U.S.  and  Europe.  On  average,  total  assets  of  U.S.  companies  are  smaller  than   their  European  counterparts.  Similarly,  average  cash  and  investments2  held  by  U.S.  companies   are  smaller  than  for  European  companies.  For  liabilities,  U.S.  companies   have  lower  insurance   liabilities  as  well  as  debt  than  European  insurers.  However,  there  is  more  dispersion  in  size  for   U.S.   than   for   European   companies.   Except   for  Scor,   all  other   European   insurers   in  our   sample   are  of  the  same  order  of  magnitude  in  terms  of  total  asset  size.   We   sample   the   CDS   data   quarterly   because   this   is   the   frequency   at   which   balance   sheet   information  is  available  for  companies,  and  in  particular  insurers.  In  contrast,  some  authors  like   Acharya  et  al.  (2012)  use  monthly  credit  spreads  combined  with  quarterly  balance  sheet  data.   The   disadvantage   of   this   alternative   method   is   that   each   balance   sheet   observation   is   kept                                                                                                                               2

 Investments  include  long  positions  in  financial  securities  other  than  cash  such  as  short-­‐term  debt,  fixed-­‐income,   equity,  loans,  and  others.  

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constant  for  three  months,  introducing  serial  correlation  in  the  regression  residuals.  In  order  to   avoid   this   problem,   we   choose   to   perform   our   analysis   using   quarterly   data.   Our   results,   however,  are  robust  to  conducting  the  empirical  analysis  at  the  monthly  frequency.   2.2   Variables   2.2.1   CDS  Spreads     Our  data  set  is  composed  of  end  of  quarter  CDS  quotes  obtained  from  Bloomberg  for  five  year   single  name  CDS,  which  are  known  to  be  the  most  liquid  contracts.  CDS  quotes  available  for  the   sample  period  are  richer  for  the  U.S.  than  for  Europe.   Table  II  reports  descriptive  statistics  on   CDS  spreads  for  U.S.  and  European  insurance  companies.  In  the  table,  CDS  spreads  are  reported   for  each  firm  and  also  aggregated  by  region  (U.S.  and  Europe).  The  mean  spread  for  U.S.  firms  is   135.24   bp,   while   the   standard   deviation   is   228.08   bp.   The   mean   spread   of   94.51   bp   and   standard   deviation   of   131.01   bp   for   European   insurers   are   lower   than   the   ones   for   U.S.   insurance  companies,  respectively.   2.2.2   Investment  Variables   We   include   in   our   analysis   different   types   of   investments   in   financial   instruments   made   by   insurers,   as   a   percentage   of   total   assets:   cash,   short-­‐term   investments,   fixed-­‐income,   equity   and   loans.   These   variables   represent   the   asset   allocation   performed   by   insurers   in   order   to   maximize  the  profitability  of  the  funds  obtained  by  selling  insurance  policies  and  issuing  debt.   All  variables  are  scaled  by  total  assets.  

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In   our   analysis,   cash   represents   the   total   amount   of   cash   held   by   the   company.   We   do   not   consider   as   cash   short-­‐term   investments   that   could   easily   be  liquidated   and   turned  into   cash.   The  intuitive  prediction  is  that  firms  holding  more  cash  should  be  safer,  and  hence  have  lower   CDS   spreads.   However,   recent   research   (Acharya   et   al.,   2012)   suggests   that   if   cash   levels   are   determined  endogenously   as   part   of   an  optimization  process,   a   regression  of   CDS   spreads   on   cash  holdings  might  reveal  a  positive  correlation  between  cash  and  credit  risk.  In  other  words,   an  increase  in  cash   might  be  interpreted  as   a  negative  signal  by  market  participants  since  the   insurer  could  be  increasing  the  cash  for  precautionary  motives.  In  such  case,  we  should  expect  a   positive   coefficient   for   cash   when   regressed   with   CDS   spreads.   Nevertheless,   exogenous   variations   in   cash   should   correlate   negatively   with   credit   risk.   Furthermore,   cash   levels   might   also  be  constrained  by  country  regulations  and  not  impact  CDS  spreads.     Figure  1  plots  cash  reserves  to  total  assets  and  quarterly  CDS  spreads  for  U.S.  insurers  from  July   2002   to   June   2012.   Figure   2  plots   the   same   variables   for   European   insurers.   We  observe   that   overall   cash   holdings   of   U.S.   insurers   display   a   strong   positive   correlation   with   CDS   spreads,   especially   during   the   2007   subprime   crisis.   The   correlation   between   cash   holdings   and   credit   spreads   seems   weaker   in   Europe,  and   the   time   variation   in   cash   reserves   is   less   pronounced.   Table  III  shows  cash  holdings  as  percentage  of  total  assets  for  U.S.  and  European  insurers.  We   observe   that   U.S.   firms   hold   on   average   0.77%   of   their   total   assets   as   cash,   while   European   firms   hold   3.63%   of   their   total   assets   as   cash   holdings.   Therefore,   European   insurers   hold   on   average  four  times  more  cash  than  their  U.S.  counterparts.    

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Besides   cash,   we   also   include  in   the   analysis   investments   in   corporate  debt   that  we   divide  by   maturity.   Short-­‐term   investments   represent   the   total   amount   invested   in   deposits   and   investments  with  original  maturities  within  one  year,  such  as  commercial  paper.   On  the  other   hand,   fixed-­‐income   represents   the   total   amount   invested   in   fixed-­‐income   securities   with   maturities  over  one  year.  Both  items  were  hand-­‐collected  from  official  filings.  Given  the  low  risk   of  these  securities,  we  should  expect  a  negative  effect  on  the  credit  risk  of  insurers.  However,   for   the   same   reasons   stated   for   cash   holdings,   investment   on   such   securities   could   also   be   perceived  by  market  participants  as  way  to  anticipate  future  losses.   Insurers  also  take  direct  equity  stakes  in  other  companies.  Given  that  these  securities  increase   the  risk  of  the  portfolio,  we   hypothesize  that  this  variable  should   correlate  positively  with  the   level   of   CDS   spreads.   Finally,   we   include   loans   that   correspond   to   mortgage   loans   issued   by   insurers.  As  the  2007-­‐09  period  is  usually  associated  with  the  bursting  of  a  real  estate  bubble,   we  expect  this  variable  to  have  a  positive  effect  on  CDS  spreads,  especially  after  the  collapse  of   Lehman  Brothers.   Table   III,   Panel   A   displays   summary   statistics   for   these   variables.   For   both   U.S.   and   European   insurers,   investments   in   fixed-­‐income   securities   represent   the   largest   share   with   respect   to   total  investments,  although   the   figure   is  larger   in   the   U.S.   (49%)   than   in   Europe   (38%).   Short-­‐ term   investments   are   more   predominant   in   the   U.S.   than   in   Europe,   whereas   the   opposite   is   true  for  equity  investments.  There  is,  however,  some  dispersion  around  the  mean  as  shown  by   the  standard  deviation  of  these  variables.  

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2.2.3   Firm-­‐Specific  Variables   We   also   collect   from   Bloomberg   and   10-­‐Q   forms   company   specific   variables   that   allow   us   to   compute   quantities   that   are   known   to   impact   CDS   premia,   such   as   leverage,   equity   volatility   and  distance-­‐to-­‐default.     First,  we  want  to  analyze  to  what  extent   leverage  is  an  important  determinant  of  CDS  premia   for  insurance  companies  since  it  is  known  to  explain  credit  risk  premia  for  companies  in  general.   We   construct   two   measures   of   leverage,   one   representing   just   the   long-­‐term   debt   as   a   percentage  of  total  assets,  and  one  representing  the  proportion  of  insurance  liabilities  of  each   insurer  to  total  assets.  The  reason  for  doing  this  relies  on  the  fact  that  insurance  firms  have  a   large   exposure   to   the   assets   that   they   insure   (casualty,   life   and   property)   and   we   want   to   understand  which  form  of  leverage  matters  most  for  CDS  premia.   This  is  something  specific  to   the   insurance   industry   that   to   the   best   of   our   knowledge   has   not   been   analyzed   in   previous   literature.   The   data   for   debt   was   hand   collected   from   regulatory   filings   whereas   insurance   liabilities  were  obtained  from  Bloomberg.   Second,  we  also  want  to  analyze  the  impact  of  equity  volatility  on  the  CDS  spread  of  insurers.   We  use  a  90ʹday  historical  volatility  that  we  obtain  from  Bloomberg.  This  quantity  is  calculated   as   the   annualized   standard   deviation   of   the   stock   percentage   change   for   the   90   most   recent   trading   days.   The   measure   uses   closing   prices   for   its   computation.   The   choice   aims   to   be   consistent  with  the  fact  that  we  use  quarterly  data  in  our  regressions.   Finally,   we  also   include   in   our   analysis   Vassalou   and   Xing   (2004)   distance-­‐to-­‐default   measure.   This   variable   represents   the   distance,   measured   in   standard   deviations,   from   default   in   a  

11    

Merton   (1974)   setup.  A  higher  distance-­‐to-­‐default   translates   in  a  lower  probability   of  default.   We   compute   our   measure   of   distance-­‐to-­‐default   (DD)   using   the   method   of   Bharath   and   Shumway   (2008).   The   naïve   distance-­‐to-­‐default   measure   of   Bharath   and   Shumway   (2008)   is   defined  as:  

݀݀ ൌ 

Ž

ሺ‫ ܧ‬൅ ‫ܨ‬ሻ ൅ ൫‫ݎ‬௜ǡ௧ିଵ െ ͲǤͷƒÃ˜‡ߪ௩ ଶ ൯ܶ ‫ܨ‬ ǡ   ƒÃ˜‡ߪ௩ ξܶ

where  ‫ ܧ‬ represents  the  value  of  the  market  equity  calculated  as  the  product  of  share  price  at   the   end   of   each   quarter   and   the   number   of   shares   outstanding;  ‫ ܨ‬ is   the   face   value   of   debt;   ா

ி

‫ݎ‬௜ǡ௧ିଵ  is  the  return  of  equity  of  firm  ݅  in  the  previous  period;  ƒÃ˜‡ߪ௩ ൌ ாାி ߪ௘ ൅ ாାி ƒÃ˜‡ߪௗ ;   ƒÃ˜‡ߪௗ ൌ ͲǤͲͷ ൅ ͲǤʹͷ ‫ߪ כ‬௘ ;  and  ܶ  is  the  forecasting  horizon  of  1  year.   We   collect   the   inputs   to   the   distance-­‐to-­‐default   model   of   Bharath   and   Shumway  (2008)   from   different  sources.  The  volatility  of   stock  returns  ߪ௘  is  obtained  from  Bloomberg  and  estimated   as  the  annualized  standard  deviation  of  the  relative  price  change  for  the  30  most  recent  trading   days,  expressed  as  a  percentage.  The  market  value  of  equity  for  each  insurer  is  calculated  as  the   product  of  the  share  price  at  the  end  of  the  month  and  the  number  of  shares  outstanding  using   data  from  Bloomberg.  As  in  Bharath  and  Shumway  (2008),  the  face  value  of  debt  is  estimated  to   be   the   short-­‐term  debt  plus  one-­‐half   of  long-­‐term   debt   that   we  obtain   from   COMPUSTAT   for   U.S.  insurers  and  from  regulatory  filings  for  European  insurers.   Table  III,  Panel  B  presents  summary  statistics  of  these  four  variables.  On  the  one  hand,  we  can   observe  that  corporate  debt  is  relatively  low  as  a  percentage  of  total  assets   both  for  U.S.  (5%)   and   European   (7%)   insurers.   On   the   other,   insurance   liabilities   represent   a   large   share   of   the   12    

balance  sheet  and  are  quite  similar  in   the  U.S.  (71%)  and  Europe  (69%).  Volatility  is  also   quite   similar  on  average  in   the  U.S.  (37%)  and  Europe  (37%),  although  there  is  more  cross-­‐sectional   variation   in   the   U.S.   Finally,   in   terms   of   distance-­‐to-­‐default,   U.S.   insurers   appear   safer   than   European  insurers.   2.2.4   Macro  Variables   The   literature   has   also   determined   the   importance   of   common   macro   factors   in   determining   the   level   of   CDS   spreads.   In   our   empirical   analysis,   we   will   alternatively   use   time-­‐effects   to   capture   any   common   trend   in   the   series   that   is   not   captured   by   our   macro   factors.   Our   macroeconomic  control  variables  were  obtained  from  Bloomberg  for  the  U.S.  and  Europe,  and   include  the  risk-­‐free  rate,  the  yield  slope,  the  implied  stock  market  volatility  and  stock  market   skew,  and  the  swap  spread.   As  pointed  out  by  Collin-­‐Dufresne  et  al.  (2001),  an  increase  in  the  risk-­‐free  rate  should  produce   a  decrease  in  CDS  spreads.  Following  Longstaff  et  al.  (2005)  and  Raunig  and  Scheicher  (2009),   we  use  the  five-­‐year  swap  rate  in  USD  and  EUR  as  a  proxy  for  the  risk-­‐free  rate.  Collin-­‐Dufresne   et   al.   (2001)   also   show   that   an   increase   in   the   slope   of   the   yield   curve   should   decrease   CDS   spreads.   We   compute   the   slope   of   the   yield   curve   in   the   U.S.   and   Europe   as   the   difference   between  the  ten  and  one-­‐year  USD  and  EUR  swap  rate,  respectively.   We  also  know  from  previous  literature  that  an  increase  in  stock  market  volatility  should  affect   positively  CDS  spreads  to  compensate  investors  for  more  expected  losses  from  default.  We  use   the  VIX  implied  volatility  index  (Coudert  and  Gex,  2008;  Raunig  and  Scheicher,  2009)  to  proxy  

13    

for  this  variable  in  the  U.S  and  use  the  V2X  index  to  proxy  for  the  volatility  of  the  Euro  STOXX  50   index.     We  also  include  a  measure  of  tail  risk  since   the  period  studied  in  our  paper  covers  the  recent   2007-­‐2009  financial  crisis.  For  this  we  consider  the  implied  stock  market  skew.  Similarly  to  stock   market   volatility,   an   increase   in   the   skew   should   proxy   for   a   higher   probability   of   a   market   crash,  hence  increasing  CDS  spreads.  We  use  the  SKEW  index  provided  by  the  CBOE  (SKEW)  to   proxy   for   the   implied   skew.   There   is   no  such   index   for   Europe,   so   we   use   the   same   index   for   both  U.S.  and  European  insurers.   Finally,  we  follow  Longstaff  et  al.  (2005)  and  include  the  swap  spread  between  swap  rates  and   government   bond   yields   to   proxy   for   flight-­‐to-­‐liquidity.   We   use   the   Bloomberg   2-­‐year   swap   spread  in  USD  and  EUR.   Table   III,   Panel   C   presents   summary   statistics   for   these   variables.   The   values   reported   in   the   table   are   just   a   time-­‐series   average   since   the   variables   are   common   to   all   insurers   in   their   respective   region.   We   find   that   all   macro   variables   are   similar   for   both   the   U.S.   and   Europe,   except  for  the  implied  volatility  that  is  higher  in  Europe.  

3  

Credit  Spreads  Determinants  of  Insurance  Companies  

3.1   Methodology   In   this   section   we   analyze   which   variables   are   significant   determinants   of   CDS   spreads   for   all   insurance   companies   in   our   sample:   investment   portfolio   variables,   firm-­‐specific   variables   or   macroeconomic  factors.     14    

As   mentioned   before,   existing   empirical   studies   have   found   that   macro   variables   such   as   the   level  and  the  slope  of  the  term-­‐structure  of  interest  rates,  or  the  implied  market  volatility  are   important   determinants   of   CDS   spread   changes.   Furthermore,   these   studies   have   also   confirmed   that   firm-­‐specific   characteristics   such   as   leverage   and   idiosyncratic   volatility   also   matter  for  the  level  and  changes  of  CDS  spreads.     To  test  which  variables  impact  CDS  spreads  of  insurance  companies,  we  run  first  a  basic  panel   regression   model   allowing   for   fixed-­‐effects   and   firm-­‐specific   variables   (equity   volatility,   debt,   insurance   liabilities   and   distance-­‐to-­‐default).   We   also   include   macro-­‐controls   that   have   been   shown  to  matter  for  credit  risk  (risk-­‐free  rate,  slope  of  the  yield  curve,  stock  market  volatility,   stock   market   skew   and   swap   spread),   or   alternatively   time-­‐effects,   to   remove   unwanted   systematic  trends.  We  also  add  a  set  of  investment  portfolio  variables  such  as  cash,  short-­‐term   and   fixed-­‐income   investments,   equity   and   loans,   to   test   whether   the   portfolio   risk   of   these   investments  has  an  effect  on  credit  risk.  The  empirical  specification  of  the  analysis  is  as  follows:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܸܰܫ‬௜ǡ௧ ൅ ߛ ᇱܺ௜ǡ௧ ൅ ߜ ᇱ ܼ௧ ൅ ߝ௜ǡ௧  

(1)  

where  ‫ܵܦܥ‬௜ǡ௧  represents   the   CDS   quote   for   entity   i   at   the   end   of   period   t;  ‫ܸܰܫ‬௜ǡ௧  is   is   a   set   of   investment  portfolio  variables  available  for  entity   i  at  the  end  of  period   t;  ܺ௜ǡ௧  is  a  set  of  firm-­‐ specific  variables;  and  ܼ௧  is  a  vector  of  macroeconomic  or  time-­‐effects  variables.   Since   the   previous   regression   might   reflect   equilibrium   between   credit   risk   and   firm-­‐ characteristics,   we   also   test   for   Granger   causality   between   credit   spreads   and   firm-­‐specific   variables.  Hence,  we  run  the  same  panel  regression  allowing  for  fixed-­‐effects  and  lagged  firm-­‐ specific  variables.  We  also  test  whether  lagged  macro-­‐controls  predict  the  level  of  CDS  spreads.  

15    

We   also   run   the   panel   regression   using   time-­‐effects   to   remove   unwanted   systematic   trends.   The  empirical  specification  of  the  analysis  is  as  follows:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܸܰܫ‬௜ǡ௧ିଵ ൅ ߛ ᇱܺ௜ǡ௧ିଵ ൅ ߜ ᇱ ܼ௧ିଵ ൅ ߝ௜ǡ௧  

(2)  

where  ‫ܵܦܥ‬௜ǡ௧  represents  the  CDS  quote  for  entity  i  at  the  end  of  period  t;  ‫ܸܰܫ‬௜ǡ௧ିଵ  is  is  a  set  of   lagged  investment  portfolio  variables  available  for  entity  i  at  the  end  of  period  t-­‐1;  ܺ௜ǡ௧ିଵ  is  a  set   of   firm-­‐specific   variables   with   lags;   and  ܼ௧ିଵ  is   a   vector   of   lagged   macroeconomic   or   time-­‐ effects  variables.   3.2   Results  for  the  Full  Sample   We   run   the   panel-­‐data   model   in   equation   (1)   for   the   full   sample   at   the   quarterly   frequency.   Table   IV   presents   the   regression   results.   All   p-­‐values   are   calculated   using   robust   standard   errors.  Column  (1)  presents  the  results  for  a  regression  of  CDS  spreads  on  investment  variables   allowing   only   for   fixed-­‐effects.   Column   (2)   adds   macro   variables,   while   column   (3)   uses   time-­‐ effects   instead   of   macro   controls.   We   repeat   the   same   process   but   this   time   including   other   firm-­‐specific  variables  in  columns  (4),  (5)  and  (6).   We   do   not   find   that   portfolio   investment   variables   have   a   significant   effect   in   explaining   CDS   spreads.   Even   though   cash,   fixed-­‐income   and   equity   come   significant   under   some   specifications,   they   lose   their   explanatory   power   when   macro   variables   and   time-­‐effects   are   included.   On   the   contrary,   we   find   that   firm-­‐specific   variables   are   significant   determinants   of   credit   spreads.   The   coefficients   on   volatility   and   insurance   liabilities   are   positive   as   expected.   Distance-­‐to-­‐default  and  debt  are  significant  under  most  specifications,  except  when  we  include   time-­‐effects.   As   expected,   debt   has   a   positive   coefficient   while   distance-­‐to-­‐default   has   a   16    

negative  coefficient.  Finally,  we  find  that  macroeconomic  factors  such  as  the  risk-­‐free  rate,  the   yield  slope  and  the  stock  market  skew  are  significant  determinants  of  credit  spreads.  The  swap   spread  loses  explanatory  power  when  we  include  firm-­‐specific  variables  in  the  regrssion.   We  also  analyze  if  lagged  values  of  these  variables  can  predict  the  variation  of  CDS  spreads.  As   explained   before,   we   run   the   panel-­‐data   model   in   equation   (2)   for   the   full   sample   at   the   quarterly   frequency.   Table   V   presents   the   results.     Again,   all   p-­‐values   are   calculated   using   robust   standard   errors.   Column   (1)   presents   the   results   for   a   regression   of   CDS   spreads   on   investment   variables   allowing   only   for   fixed-­‐effects.   Column   (2)   adds   macro   variables,   while   column   (3)   contains   time-­‐effects.   We   repeat   the   same   process   but   this   time   including   firm-­‐ specific  variables  in  columns  (4),  (5)  and  (6).   Among   the   portfolio   investment   variables,   we   find   that   these   variables   are   not   significant   predictors  of  CDS  spreads.  Even  though  lagged  values  of  short-­‐term  investments,  fixed-­‐income   and  equity  come  significant  under  some  specifications,  they  lose  their  explanatory  power  when   firm-­‐specific  variables,   macro  variables  and  time-­‐effects  are  included.  In  terms  of  firm-­‐specific   variables,   we   find   that  debt   and   volatility   are   significant  in   predicting   CDS   spreads.   They   have   both   positive   coefficients   as   expected.   Lagged   values   of   distance-­‐to-­‐default   and   insurance   liabilities  are  significant  under  most  specifications,  except  when  we  include  time-­‐effects.  Finally,   we  find  that  past  values  of  macroeconomic  factors   such  as  the  swap  rate,  the  yield  slope  and   stock  market  skew  are  also  significant  predictors  of  CDS  spreads.     Overall,   our   results   confirm   the   findings   of   Hang   et   al.   (2009)   in   that   equity   volatility   risk   predicts   a   large   part   of   the   variation   in  CDS   spreads   for   non-­‐financial   firms.   This   result  is   also  

17    

consistent   with   Campbell   and   Taksler   (2003)   that   find   that   equity   volatility   is   a   significant   determinant  of  corporate  bond  yields.  Hence,  our  results  challenge  the  view  that  variables  that   are  known  to  explain  credit  spreads  of  non-­‐financial  firms  usually  lose  their  explanatory  power   when  applied  to  financial  firms  (see  e.g.  Boss  and  Scheicher,  2002;  Raunig  and  Scheicher,  2009;   Grammatikos   and   Vermeulen,   2012).   Equity   volatility   seems   to   be   an   important   predictor   of   credit  spreads  for  insurance  companies  as  it  is  for  non-­‐financial  firms.   We  also  find  that  the  level  of  debt  is  a  crucial  predictor  of  credit  spreads.  However,  our  results   show   that   it   is   important   to   distinguish   between   the   level   of   debt   and   the   level   of   insurance   liabilities,   which   does   not   have   the   same   strong   forecasting   power.   Contemporaneously,   we   find  that  all  firm-­‐specific  variables  other  than  investment  variables  including  cash  are  significant   in  explaining  CDS  spread  variation.   Since  in  our  regressions  we  have  pooled  together  both  U.S.  and  European  insurers,  in  the  next   section   we   analyze   the   differences   in   credit   spreads   determinants   when   we   separate   the   insurance  companies  by  region.   3.3   Results  by  Region   We   run   the   panel-­‐data   model   in   equation   (1)   for   the   U.S.   and   Europe   separately.   Table   VI   presents   the   regression   results   for   U.S.   firms,   and   Table   VII   reports   the   results   for   European   firms.   All   p-­‐values   are   calculated   using   robust   standard   errors.   Column   (1)   in   both   tables   presents  the  results  for  a  regression  of  CDS  spreads  on  investment  variables  allowing  only  for   fixed-­‐effects.   Column   (2)   adds   macro   variables,   while   column   (3)   contains   time-­‐effects.   We  

18    

repeat   the   same  process   but   this   time   including   firm-­‐specific   variables  in   columns   (4),   (5)   and   (6).   Among   the   portfolio   investment   variables   for   U.S.   firms,   we   find   that   cash,   short-­‐term   investments   and   fixed-­‐income   have  positive   signs   in   all   specifications,   which   means   that   they   are  positively  correlated  with  CDS  spreads.  Among  these  variables,  cash  come  significant  in  all   specifications,   fixed-­‐income   is   significant   in   four   out   of   six   specifications,   and   short-­‐term   investments  are  significant  in  only  two  specifications.     The   results   for   cash   are   counter   intuitive   since   more   cash   should   be   associated   with   a   lower   probability   of   default   and   hence   lower   credit   risk.   However,   recent   work   by   Acharya   et   al.   (2012)   indicates   that   standard   OLS   regressions   used   in   empirical   studies   of   credit   spreads   should   predict   a   positive   correlation   between   cash   holdings   and   credit   risk.   Furthermore,   as   predicted  by  Acharya  et  al.  (2012),  we  find  that  the   economic  significance  of  the  coefficient  is   stronger  when  no  credit-­‐risk  controls  are  included  in  the  regression   since  cash  holdings  proxy   for  credit  risk.  However,  this  significance  decreases  by  more  than  half  when  credit-­‐risk  controls   are  included.  Our  results  suggest  that  one  of  the  most  important  determinants  of  CDS  spreads   for  U.S.  insurers  are  their  cash  reserves.  Short-­‐term  investments,  on  the  other  hand,  do  not  play   such  a  prominent  role  even  though  they  could  be  seen  as  close  substitutes.   In   terms   of   firm-­‐specific   variables,   we   find   that   the   amounts   of   debt   and   equity   volatility   are   significant  determinants  of  the  credit  risk  of  U.S.  insurers.  They  have  both  positive  coefficients   as  expected.  The  distance-­‐to-­‐default  coefficient  is  negative  in  all  specifications  although  is  only   significant  when  we  include  time-­‐effects.  On  the  other  hand,  credit  risk  of  U.S.  insurers  seems  

19    

to  be  insensitive  to  the  level  of  their  insurance  liabilities.  Finally,  we  do  not  find  macroeconomic   factors  to  be  significant  determinants  of  CDS  Spreads  for  U.S.  insurers  when  we  control  for  firm-­‐ specific  factors.   Table  VII  presents  results  for   credit  spreads  regressions  of  European   insurance  firms.  First,  we   can   observe   that   contrary   to   U.S.   firms,   investment   portfolio   variables   do   not   seem   to   consistently   explain   CDS   spreads   across   all   specifications.   The   statistical   significance   of   cash   holdings,   short-­‐term   investments   and   equity   disappears   when   we   allow   for   time-­‐effects.   For   firm-­‐specific   variables   we   find   that   equity   volatility   and   distance-­‐to-­‐default   have   a   significant   effect  for  European  insurers,  even  after  controlling  for  time-­‐effects.  As  expected,  volatility  has  a   significant   positive   coefficient,   while   distance-­‐to-­‐default   has   a   negative   correlation   with   CDS   spreads.   Contrary   to   what   was   observed   for   U.S.   firms,   two   macroeconomic   factors   seem   to   explain  CDS  spreads:  the  risk-­‐free  rate  and  the  swap  spread.  Interest  rates  are  high  when  the   economy   is   booming   and   low   in   recessions,   suggesting   a   negative   correlation   with   credit   spreads.  On  the  contrary,  the  swap  spread  widens  in  periods  of  market  stress  because  of  flight-­‐ to-­‐liquidity,   suggesting   that   credit   spreads   should   also   increase   in   such   periods.   As   expected,   the   risk-­‐free   rate   has   a   negative   coefficient   while   the   swap   spread   has   a   positive   correlation   with  credit  risk.  Hence,  credit  spreads  of  European  insurers  are  more  sensitive  to  the  business   cycle  than  spreads  of  U.S.  insurers.   3.4   Comparison  between  CDS  Determinants  in  the  U.S.  and  Europe   Our  results  suggest  that  there  is   a  difference  between  U.S.  and  European  insurers  in  terms  of   determinants  of   credit  spreads.  CDS  spreads  in  the  U.S.  seem  to  be  driven  more  by  individual  

20    

characteristics  such  as  cash,  debt  and  equity  volatility,  rather  than  observable  macroeconomic   factors.   However,   CDS   spreads   in   Europe   seem   to   be   explained   better   by   equity   volatility,   distance-­‐to-­‐default,  and  macroeconomic  factors  such  as  the  risk-­‐free  rate  and  the  swap-­‐spread.   Investments   in   financial   assets   do   not   seem   to   matter   for   the   credit   spread   of   European   insurance  companies.   The  results  show  that  equity  volatility  is  the  only  firm-­‐specific  variable  that  comes  significant  in   all  specifications  for  both  U.S.  and  European  insurers.  The  effect  is  also  economically  significant   for  both  U.S.  and  European  insurers.  This  finding  confirms  the  results  of  Zhang  et  al.  (2009)  in   that  equity  volatility  is  the  most  important  determinant  of  credit  spreads.  We  can  observe  that   the  effect  of  equity  volatility  on  credit  spreads  relates  to  individual  characteristics  not  related  to   market   wide   volatility   as   the   coefficients   on   the   VIX   and   V2X  are   not   significant,  and   that   the   effect  holds  after  controlling  for  leverage.   We   also   find   interesting   that   cash   holdings   are   of   such   importance   in   explaining   the   credit   spread   variation   of  U.S.   insurers.   Interestingly,   very   little   is   known   about   the  determinants   of   cash   holdings   for   U.S.   insurers.   An   exemption   is   Colquitt   et   al.   (1999),   who   investigate   the   differences   in   cash   holdings   across   property-­‐liability   insurers.   They   conclude   that   larger   and   high  quality  insurers  hold  less  cash,  and  that  insurers  with  a  higher  variance  of  cash  flows  tend   to  hold  more  cash.     We   believe   that   the   differences   of   the   impact   of   cash   reserves   on   the   credit   risk   for   U.S.   compared   to   European  insurers   might   be   due   to   regulation.   In   the  U.S.,   insurance   companies   are   required   to   maintain   their   adjusted   capital   above   a   minimum   required   level   called   risk-­‐

21    

based   capital   (RBC),   which   depends   on   several   risk   factors,   as   established   by   the   National   Association   of   Insurance   Commissioners   (NAIC).   dŚĞ ƌŝƐŬ ĨĂĐƚŽƌƐ ĨŽƌ ƚŚĞ E/͛Ɛ Z ĨŽƌŵƵůĂƐ focus   on   three   major   areas:   asset   risk,   underwriting   risk,   and   other   risks.   The   weight   of   each   factor  in  the  RBC  formula  differs  depending  on  the  type  of  insurance,  but  asset  risk  remains  an   important  determinant  of  minimum  capital  requirements.   In   EU   countries,   insurance   entities   are   required   to   maintain   minimum   solvency   margins   according   to   the   existing   Solvency   I   legislation.   Solvency   I   capital   is   calculated   as   a   fixed   percentage   of   premiums,   claims,   reserves   and/or   net   amounts   at   risk.   The   required   minimum   solvency   margin   for   general   insurers  depends   on  premiums   written   for   the   year  or   the   three-­‐ year  average  of  claims  incurred.  Life  insurance  companies  are  required  to  maintain  a  minimum   solvency  margin  generally  of  4  percent  of  insurance  reserves,  plus  0.3  percent  of  the  amount  at   risk   under   insurance   policies.   The   same   minimum   capital   requirements   are   applicable   for   insurance  entities  operating  in  Switzerland.3   As  a  consequence,  in  our  sample  the  regulatory  capital  for  U.S.  insurers  is  more  sensitive  to  the   risk   of   the   assets   than   for   European   insurers,   which   may   explain   why   cash   reserves   of   U.S.   entities  increase  in  times  of  market  stress  and  decrease  when  the  asset  risk  is  reduced.  Hence,   cash  holdings  of  U.S.  insurers  are  more  informative  about  their  credit  condition  than   the  cash   reserves   of   European   entities,   and   vary   more   in   response   to   market   events.   Empirically,   we   observe   that   U.S.   insurers   hold   on   average   four   times   less   cash   to   total   asset   than   European                                                                                                                               3

 The  new  Solvency  II  legislation  was  scheduled  to  replace  Solvency  I  on  January  2013,  but  was  recently  postponed   until  January  2016.  The  new  Swiss  Solvency  Test  became  fully  mandatory  in  January  2011.    

22    

insurance   companies,   and   that   cash   reserves   for   U.S.   insurers   vary   more   over   time   than   the   ones  of  their  European  counterparts.    

4  

Cash  Holdings  and  CDS  Spreads  for  U.S.  Insurers  

4.1   Methodology   Given   the   importance   of   solvency   for   insurance   companies   and   the   complexity   of   their   business,   we   explore   in   more   detail   the   relationship   between   cash   holdings   and   credit   risk.   Acharya  et  al.  (2012)  show  that  cash  holdings  affect  credit  risk  through  two  channels:  a  direct   one  given  the  endogenous  nature  of  cash  holdings,  and  an  indirect  channel  through  exogenous   variations  in  cash  levels.  On  the  one  hand,  they  find  that  endogenous  variations  in  cash  result  in   a  robust  positive  correlation  of  corporate  cash  holdings  with  credit  spreads  and   with  the  long-­‐ term  probability  of  default.  On  the  other  hand,  exogenous  variations  in  cash  levels  unrelated  to   credit  risk  factors  should  be  negatively  correlated  with  credit  spreads  since  in  that  case  the  firm   becomes  effectively  safer.   In  this  section  we  focus  on  U.S.  insurers  given  that  cash  holdings  do  not  impact  significantly  the   credit  risk  of  European  insurance  companies.  We  do  not  proceed  to  directly  identify  exogenous   variations   in   cash   levels,   but   rather   we   study   how   credit   spreads   react   for   firms   that   hold   a   larger  proportion   of   cash   after   they   experience  an   improvement   in   their   solvency.   To   achieve   this,   we   use   a   differences-­‐in-­‐differences   approach   in   which   we   perform   a   cross-­‐sectional   comparison  of  cash  holdings  of  firms  which  have  unexpectedly  improved  their  financial  position   compared   to   those   who  have  not.   We   test   three   related   empirical   specifications   in   which   we  

23    

study   the   interaction   of   unexpected   improvements   in   solvency   with   contemporaneous   cash   levels.   We   use   unexpected   increases   in   cash   reserves,   as   well   as   unexpected   reductions   of   insurance  liabilities,  as  proxies  for  improvements  of  ŝŶƐƵƌĞƌƐ͛solvency.   Our  first  empirical  specification  is  as  follows:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܪܵܣܥ‬௜ǡ௧ ൅ ߚଶ ͳ൛୼஼஺ௌு೔ǡ೟ வ଴ൟ ൅ ߚଷ ͳ൛୼஼஺ௌு೔ǡ೟ வ଴ൟ ‫ܪܵܣܥ‬௜ǡ௧ ൅ ߛ ᇱܺ௜ǡ௧ ൅ ߜ ᇱ ܼ௧

(3)  

൅ ߤ௜ǡ௧   where  ‫ܵܦܥ‬௜ǡ௧  represents   the   CDS  quote   for   entity   i   at   the   end  of   period   t;  ‫ܪܵܣܥ‬௜ǡ௧  is   the   cash   holdings   to   total   assets   ratio   for   entity   i   at   the   end   of   period   t;  ͳ൛୼஼஺ௌு೔ǡ೟ வ଴ൟ  is   an   indicator   function  equal  to  one  if  ȟ‫ܪܵܣܥ‬௜ǡ௧ ൐ Ͳ  and  zero  otherwise;  ܺ௜ǡ௧  is  a  set  of  firm-­‐specific  variables   that  includes  all  other  investment  variables;  and  ܼ௧  is  a  vector  of  macroeconomic  or  time  fixed-­‐ effects  variables.  We  use  the  interaction  of  cash  holdings  with  changes  in  cash   with  respect  to   the  previous  period  as  our  identification  strategy.   Since  cash  holdings͛  changes  are  difficult  to   predict,   the   interaction   term   captures   the   relative   relevance   of   cash   holdings   after   a   positive   solvency   shock.   We   also   use   a   reduction   in   insurance   liabilities   as   a   proxy   for   an   unexpected   improvement  in  the  liabilities  of  the  firm:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܪܵܣܥ‬௜ǡ௧ ൅ ߚଶ ͳ൛୼௅ூ஺஻೔ǡ೟ ழ଴ൟ ൅ ߚଷ ͳ൛୼௅ூ஺஻೔ǡ೟ ழ଴ൟ‫ܪܵܣܥ‬௜ǡ௧ ൅ ߛ ᇱܺ௜ǡ௧ ൅ ߜ ᇱ ܼ௧

(4)  

൅ ߤ௜ǡ௧   as  well  as  their  combined  effect:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܪܵܣܥ‬௜ǡ௧ ൅ ߚଶ ͳ൛୼஼஺ௌு೔ǡ೟ வ଴Ƭ୼௅ூ஺஻೔ǡ೟ ழ଴ൟ ൅ ߚଷ ͳ൛୼஼஺ௌு೔ǡ೟ வ଴Ƭ୼௅ூ஺஻೔ǡ೟ ழ଴ൟ‫ܪܵܣܥ‬௜ǡ௧ ൅ ߛ ᇱܺ௜ǡ௧ ൅ ߜ ᇱ ܼ௧ ൅ ߤ௜ǡ௧  

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(5)  

where  ͳ൛୼௅ூ஺஻೔ǡ೟ ழ଴ൟ  is  an  indicator  function  equal  to  one  if  ȟ‫ܤܣܫܮ‬௜ǡ௧ ൏ Ͳ  and  zero  otherwise;  and   ͳ൛୼஼஺ௌு೔ǡ೟ வ଴Ƭ୼௅ூ஺஻೔ǡ೟ ழ଴ൟ  is   an   indicator   function   equal   to   one   if   both   ȟ‫ܪܵܣܥ‬௜ǡ௧ ൐ Ͳ  and   ȟ‫ܤܣܫܮ‬௜ǡ௧ ൏ Ͳ.  In  the  last  specification  the  combined  effect  of  an  improvement  in  cash  reserves   and  a  reduction  of  insurance  liabilities  should  yield  the  strongest  results.   4.2   Results   Table   VIII   reports   the   results   of   the   estimation.   We   first   estimate   all   three   specifications   allowing  for  firm  fixed-­‐effects  and  macro  controls  or  time-­‐effects  in  columns  (1)  to  (6).  We  then   re-­‐estimate   all   three   specifications   including   the   other   investment   portfolio   variables   and   individual  characteristics  in  columns  (7)  to  (12).     For  the  specifications  described  in  equations  (3)  and  (4),  we  find  a  negative  coefficient  for  the   interaction  term,  although  the  coefficient  fails  to  be  statistically  significant  except  in  one  case.   For  our  final  specification  in  equation  (5),  we  find  the  interaction  coefficient  to  be  negative  and   statistically  significant.  Therefore,  the  results  show  that  firms  with  larger  amounts  of  cash  that   experience   an   unexpected   improvement   in   their   solvency   become   safer.   The   results   are   also   economically  significant  since  they  suggest  that  firms  who  hold  one  standard  deviation  more  of   cash  see  their  credit  spreads  reduced  between  30  to  42  bp  after  a  positive  solvency  shock.  

5  

Concluding  Remarks  

Our  results  on  the  determinants  of  credit  risk  for  insurance  companies  in  the  U.S.  and  Europe   reveal  three  main  results:  i)  consistent  with  recent  results  for  non-­‐financial  firms  in  the  U.S.,  we   find  that  equity  volatility  is  a  major  determinant  and  predictor  of  CDS  spreads  for  both  U.S.  and   25    

European  insurers,  even  after  controlling  for  the  composition  of  their  investment  portfolios  and   other   firm-­‐specific   characteristics   such   as   leverage   and   macro   controls;   ii)   when   analyzing   if   other   determinants   differ   for   U.S.   and   European   insurers,   we   find   macroeconomic   factors   to   affect   the   credit   risk   of   European   but   not   U.S.   insurers,   whereas   cash   holdings   seem   to   be   relevant  for  the  credit  spreads  of  U.S.  insurance  companies;  and  iii)  we  find  that  in  equilibrium,   cash   holdings   of   U.S.   insurers   and   credit   spreads   are   positively   correlated,   even   though   the   availability  of  cash  reduces  the  credit  risk  of  firms  experiencing  positive  solvency  shocks.   We  believe  that  our  results  are  relevant  for  practitioners,  investment  professionals  and  macro-­‐ prudential   regulators   worldwide.   We   show   that   minimum   capital   requirements   can   have   a   substantial  effect  on  the  cash  reserves  of  insurance  entities.  This  effect  is  captured  by  the  credit   spread  of  insurers  when  cash  reserves  are  informative  about  the  credit  situation  of  the  insurer.   When  cash  holdings  are  above  this  informative  level,  financial  markets  tend  to  focus  more  on   the   macro-­‐economic   environment   when   assessing   the   creditworthiness   of   insurance   companies.   In   conclusion,   we   find   that   equity   and   credit   markets   are   quite   efficient   in   incorporating  quickly  information  about  the  financial  solvency  of  large  insurance  companies.      

 

26    

References   Acharya,  V.,  Davydenko,  S.A.  and  I.A.  Strebulaev,  2012,  ͞Cash  Holdings  and  Credit  ZŝƐŬ͕͟  Review   of  Financial  Studies,  25,  3572ʹ3609.   Ambrose,   J.M.   and   A.M.   Carroll,   1994͕ ͞Using   Best's   Ratings   in   Life   Insurer   Insolvency   WƌĞĚŝĐƚŝŽŶ͕͟  Journal  of  Risk  and  Insurance,  61,  317ʹ327.   Annaert,  J.,  De  Ceuster,  M.,  Van  Roy,  P.  and  C.  Vespro,  2013,  ͞What  Determines  Euro  Area  Bank   CDS  ^ƉƌĞĂĚƐ͍͕͟  Journal  of  International  Money  and  Finance,  32,  444ʹ461.   Bharath,  S.T.  and  T.  Shumway,  2008͕͞Forecasting  Default  with  the  Merton  Distance  to  Default   DŽĚĞů͕͟  Review  of  Financial  Studies,  21,  1339ʹ1369.   Blanco,  R.,   Brennan,  S.   and   I.W.   Marsh,  2005,   ͞An   Empirical  Analysis   of   the   Dynamic   Relation   between   Investment-­‐'ƌĂĚĞ ŽŶĚƐ ĂŶĚ ƌĞĚŝƚ ĞĨĂƵůƚ ^ǁĂƉƐ͕͟   Journal   of   Finance,   60,   2255ʹ 2281.   Boss,   M.   and   M.   Scheicher,   2002͕ ͞The   Determinants   of   Credit   Spread   Changes   in   the   Euro   ƌĞĂ͕͟  BIS  Papers,  12,  181ʹ200.   Campbell,  J.Y.  and  G.B.  Taksler,  2003,  ͞Equity  sŽůĂƚŝůŝƚLJĂŶĚŽƌƉŽƌĂƚĞŽŶĚzŝĞůĚƐ͕͟  Journal  of   Finance,  58,  2321ʹ2350.   Carson,   J.M.   and   R.E.   Hoyt,   1995,   ͞Life   Insurer   Financial   Distress:   Classification   Models   and   ŵƉŝƌŝĐĂůǀŝĚĞŶĐĞ͕͟  Journal  of  Risk  and  Insurance,  62,  764ʹ775.  

27    

Chen,  L.,  Lesmond,  D.A.  and  J.  Wei,  2007,  ͞Corporate  zŝĞůĚ^ƉƌĞĂĚƐĂŶĚŽŶĚ>ŝƋƵŝĚŝƚLJ͕͟  Journal   of  Finance,  62,  119ʹ149.   Collin-­‐Dufresne,   P.,   Goldstein,   R.S.   and   J.S.   Martin,   2001,   ͞The   Determinants   of   Credit   Spread   ŚĂŶŐĞƐ͕͟  Journal  of  Finance,  56,  2177ʹ2207.   Colquitt,   L.L.,   Sommer,   D.W.   and   N.H.   Godwin,   1999,   ͞Determinants   of   Cash   Holdings   by   Property-­‐>ŝĂďŝůŝƚLJ/ŶƐƵƌĞƌƐ͕͟  Journal  of  Risk  and  Insurance,  66,  401ʹ415.   Coudert,  V.  and  M.  Gex,  2008,  ͞Does  Risk  Aversion  Drive  Financial  Crises?  Testing  the  Predictive   WŽǁĞƌŽĨŵƉŝƌŝĐĂů/ŶĚŝĐĂƚŽƌƐ͕͟  Journal  of  Empirical  Finance,  15,  167ʹ184.   Delatte,   A.-­‐L.,   Gex,   M.   and   A.   López-­‐Villavicencio,   2012,   ͞Has   the   CDS   Market   Influenced   the   Borrowing   Cost   of   European   ŽƵŶƚƌŝĞƐ ƵƌŝŶŐ ƚŚĞ ^ŽǀĞƌĞŝŐŶ ƌŝƐŝƐ͍͕͟   Journal   of   International   Money  and  Finance,  31,  481ʹ497.   Duffie,  D.,  and  K.J.  Singleton,  1999,  ͞Modeling  dĞƌŵ^ƚƌƵĐƚƵƌĞƐŽĨĞĨĂƵůƚĂďůĞŽŶĚƐ͕͟  Review   of  Financial  Studies,  12,  687ʹ720.   Elton,   E.J.,   Gruber,   M.J.,   Agrawal,   D.   and   C.   Mann,   2001,   ͞Explaining   the   Rate   Spread   on   ŽƌƉŽƌĂƚĞŽŶĚƐ͕͟  Journal  of  Finance,  56,  247ʹ277.   Grammatikos,   T.   and   R.   Vermeulen,   2012,   ͞Transmission   of   the   Financial   and   Sovereign   Debt   Crises   to   the   EMU:   Stock   Prices,   CDS   Spreads   and   Exchange   Rates,͟ :ŽƵƌŶĂů ŽĨ /ŶƚĞƌŶĂƚŝŽŶĂů Money  and  Finance,  31,  517ʹ533.  

28    

Houweling,   P.   and   T.   Vorst,   2005,   ͞Pricing   Default   Swaps:   Empirical   ǀŝĚĞŶĐĞ͕͟   Journal   of   International  Money  and  Finance,  24,  1200ʹ1225.   Hull,   J.,   Predescu,   M   and   A.   White,   2004͕ ͞The   Relationship   Between   Credit   Default   Swap   ^ƉƌĞĂĚƐ͕ŽŶĚzŝĞůĚƐ͕ĂŶĚƌĞĚŝƚZĂƚŝŶŐŶŶŽƵŶĐĞŵĞŶƚƐ͕͟  Journal  of  Banking  and  Finance,  28,   2789ʹ2811.   Lee,  S.H.  and  J.L.  Urrutia,  1996͕͞Analysis  and  Prediction  of  Insolvency  in  the  Property-­‐Liability   Insurance  Industry:  A  ŽŵƉĂƌŝƐŽŶŽĨ>ŽŐŝƚĂŶĚ,ĂnjĂƌĚDŽĚĞůƐ͕͟ Journal  of  Risk  and  Insurance,   63,  121ʹ130.   Longstaff,  F.A.,  Mithal,  S.  and  E.  Neis,  2005,  ͞Corporate  Yield  Spreads:  Default  Risk  or  Liquidity?   New  ǀŝĚĞŶĐĞĨƌŽŵƚŚĞƌĞĚŝƚĞĨĂƵůƚ^ǁĂƉDĂƌŬĞƚ͕͟  Journal  of  Finance,  60,  2213ʹ2253.   Merton,   R.C.,   1974,   ͞On   ƚŚĞ WƌŝĐŝŶŐ ŽĨ ŽƌƉŽƌĂƚĞ Ğďƚ͗ dŚĞ ZŝƐŬ ^ƚƌƵĐƚƵƌĞ ŽĨ /ŶƚĞƌĞƐƚ ZĂƚĞƐ͕͟   Journal  of  Finance,  29,  449ʹ470.   Norden,   L   and   M.   Weber,   2004͕ ͞Informational   Efficiency   of   Credit   Default   Swap   and   Stock   Markets:   The   /ŵƉĂĐƚ ŽĨ ƌĞĚŝƚ ZĂƚŝŶŐ ŶŶŽƵŶĐĞŵĞŶƚƐ͕͟ Journal   of   Banking   and   Finance,   28,   2813ʹ2843.   Raunig,   B   and   M.   Scheicher,   2009,   ͞Are   Banks   Different?   Evidence   ĨƌŽŵ ƚŚĞ ^ DĂƌŬĞƚ͕͟   Working  Paper  152,  Oesterreichische  Nationalbank.   Sarig,   O.   and   A.   Warga,   1989,   ͞Some   Empirical   Estimates   of   the   Risk   Structure   of   Interest   ZĂƚĞƐ͕͟  Journal  of  Finance,  44,  1351ʹ1360.  

29    

Shaked,  I.,  1985͕͞Measuring  Prospective  Probabilities  of  Insolvency:  An  Application  to  the  Life   /ŶƐƵƌĂŶĐĞ/ŶĚƵƐƚƌLJ͕͟  Journal  of  Risk  and  Insurance,  52,  59ʹ80.   Trieschmann,   J.S.   and   G.E.   Pinches,   1973͕ ͞A   Multivariate   Model   for   Predicting   Financially   Distressed  PL  /ŶƐƵƌĞƌƐ͕͟Journal  of  Risk  and  Insurance,  40,  327ʹ338.   Vassalou,   M   and   Y.   Xing,   2004͕ ͞Default   ZŝƐŬ ŝŶ ƋƵŝƚLJ ZĞƚƵƌŶƐ͕͟   Journal   of   Finance,   59,   831ʹ 868.   Zhang,  B.Y.,  Zhou,  H.  and  H.  Zhu,  2009͕͞Explaining  Credit  Default  Swap  Spreads  with  the  Equity   Volatility  and  :ƵŵƉZŝƐŬƐŽĨ/ŶĚŝǀŝĚƵĂů&ŝƌŵƐ͕͟  Review  of  Financial  Studies,  22,  5099ʹ5131.   Zhu,  H.,  2006,  ͞An  Empirical  Comparison  of  Credit  Spreads  Between  the  Bond  Market  and  the   ƌĞĚŝƚĞĨĂƵůƚ^ǁĂƉDĂƌŬĞƚ͕͟  Journal  of  Financial  Services  Research,  29,  211ʹ235.    

 

30    

8  

1.4  

7  

1.2  

CDS  Spreads  (%)  

6  

1  

5   0.8  

4   0.6   3   0.4  

2  

CDS  Spreads  

Apr-­‐12  

Jun-­‐11  

Nov-­‐11  

Jan-­‐11  

Aug-­‐10  

Mar-­‐10  

Oct-­‐09  

May-­‐09  

Jul-­‐08  

Dec-­‐08  

Feb-­‐08  

Apr-­‐07  

Sep-­‐07  

Nov-­‐06  

Jan-­‐06  

Jun-­‐06  

Aug-­‐05  

Oct-­‐04  

Mar-­‐05  

May-­‐04  

Jul-­‐03  

0  

Dec-­‐03  

0  

Feb-­‐03  

0.2  

Sep-­‐02  

1  

Cash  to  Total  Assets  (%)  

Figure   1:   CDS   Spreads   and   Cash   Holdings   for   U.S.   Insurance   Companies.   The   figure   plots   the   cross-­‐sectional   average  CDS  spread  and  cash  to  total  assets  for  all  U.S.  insurance  companies.  The  sample  covers  the  period  July   2002  to  June  2012.    

Cash  

      Figure  2:  CDS  Spreads  and  Cash  Holdings  for  European  Insurance  Companies.  The  figure  plots  the  cross-­‐sectional   average  CDS  spread  and  cash  to  total  assets  for  all  European  insurance  companies.  The  sample  covers  the  period   July  2002  to  June  2012.     5  

6  

4.5  

CDS  Spreads  (%)  

3.5  

4  

3   2.5  

3  

2   2  

1.5   1  

Cash  to  Total  Assets  (%)  

5  

4  

1  

CDS  Spreads  

Apr-­‐12  

Nov-­‐11  

Jun-­‐11  

Jan-­‐11  

Aug-­‐10  

Mar-­‐10  

Oct-­‐09  

May-­‐09  

Dec-­‐08  

Jul-­‐08  

0  

Cash  

 

   

31    

Feb-­‐08  

Sep-­‐07  

Apr-­‐07  

Jun-­‐06  

Nov-­‐06  

Jan-­‐06  

Aug-­‐05  

Mar-­‐05  

Oct-­‐04  

Dec-­‐03  

May-­‐04  

Jul-­‐03  

Feb-­‐03  

0  

Sep-­‐02  

0.5  

Table   I:   Balance   Sheet   Composition   by   Company   and   Region.   The   table   reports   balance   sheet   information   on   selected   insurance   companies   in   U.S.   and   Europe.  The  sample  covers  the  period  July  2002  to  June  2012.  All  figures  are  time-­‐series  averages  denominated  in  millions  of  USD.     Region  

Company  

Type  

U.S.  

Ace  Ltd.  

MULTI-­‐LINE  

Total   Assets   68,437  

Cash  

Investments  

631  

38,657  

Insurance   Liabilities   42,316  

   

Allstate  Corp.  

MULTI-­‐LINE  

   

American  International  Group  Inc.  

MULTI-­‐LINE  

140,270  

497  

105,625  

798,324  

2,628  

462,741  

   

Chubb  Corp.  

PROPERTY/CASUALTY  

47,063  

47  

   

Hartford  Financial  Services  Group  Inc.    

   

Lincoln  National  Corp.  

MULTI-­‐LINE  

283,827  

LIFE/HEALTH  

156,589  

   

Loews  Corp.  

MULTI-­‐LINE  

74,814  

164  

44,308  

   

Metlife  Inc.  

MULTI-­‐LINE  

514,353  

9,279  

305,836  

   

Prudential  Financial  Inc.  

LIFE/HEALTH  

453,417  

10,465  

213,696  

   

Travelers  Companies  Inc.  

PROPERTY/CASUALTY  

99,555  

318  

   

Torchmark  Corp.  

LIFE/HEALTH  

14,876  

67  

   

Unum  Group  

LIFE/HEALTH  

52,542  

   

Average  

   

Europe  

Axa  S.A.  

MULTI-­‐LINE  

   

Allianz  SE  

MULTI-­‐LINE  

   

Swiss  Re  

REINSURANCE  

   

Zurich  Insurance  Group  

       

Debt  

Equity  

2,915  

15,194  

106,755  

5,421  

19,295  

462,831  

68,320  

81,124  

36,175  

27,725  

3,261  

12,737  

1,592  

108,507  

255,415  

5,201  

16,104  

2,532  

60,972  

135,698  

4,053  

9,606  

41,307  

7,188  

16,782  

405,385  

14,465  

33,132  

369,235  

21,134  

24,796  

62,898  

64,788  

5,648  

21,766  

9,505  

9,244  

848  

3,323  

97  

37,249  

39,996  

2,801  

7,536  

225,339  

2,360  

123,848  

163,646  

11,771  

21,783  

831,656  

29,734  

652,270  

745,941  

13,408  

78,704  

1,134,270  

26,300  

417,103  

945,912  

136,856  

51,501  

250,668  

14,130  

169,718  

193,219  

17,597  

26,326  

MULTI-­‐LINE  

353,237  

13,213  

274,859  

414,061  

10,809  

26,351  

Swiss  Life  Holding  

LIFE/HEALTH  

172,771  

8,413  

103,565  

149,868  

4,839  

7,574  

Legal  General  Group  

LIFE/HEALTH  

231,886  

1,204  

330,611  

372,049  

12,708  

7,324  

   

Muenchener  

REINSURANCE  

289,438  

3,770  

236,678  

229,242  

43,058  

28,647  

   

Scor  

REINSURANCE  

28,944  

2,073  

17,232  

22,664  

3,497  

3,762  

   

Average  

   

411,609  

12,355  

275,255  

384,120  

30,347  

28,774  

 

32    

Table   II:   Statistics   on   CDS   Spreads   for   U.S.   and   European   Insurance   Companies.   The   table   reports   descriptive   statistics  on  CDS  spreads  on  selected  U.S.  and  European   insurance  companies.   All  figures  are  expressed  in  basis   points.  The  sample  covers  the  period  July  2002  to  June  2012.    

   

Region  

Company  

Avg.  

St.  Dev.  

Min.  

Max.  

U.S.      

Ace  Ltd.  

71.110  

35.598  

17.192  

161.250  

Allstate  Corp.  

67.734  

62.369  

11.011  

321.809  

   

American  International  Group  Inc.  

271.940  

455.123  

9.761  

2165.095  

   

Chubb  Corp.  

51.462  

30.551  

11.091  

122.500  

       

Hartford  Financial  Services  Group  Inc.    

165.487  

208.168  

11.721  

1054.284  

Lincoln  National  Corp.  

229.715  

457.789  

13.044  

2655.676  

   

Loews  Corp.  

56.056  

31.370  

12.498  

135.045  

   

Metlife  Inc.  

152.305  

180.025  

11.943  

840.591  

   

Prudential  Financial  Inc.  

159.307  

211.097  

11.835  

1016.348  

   

Travelers  Companies  Inc.  

58.434  

31.593  

16.984  

113.755  

   

Torchmark  Corp.  

133.075  

112.610  

23.845  

310.000  

   

Unum  Group  

189.352  

85.414  

50.300  

387.719  

   

All  

135.247  

228.083  

9.761  

2655.676  

Europe  

Axa  S.A.  

96.962  

92.472  

10.250  

340.619  

   

Allianz  SE  

58.173  

40.289  

6.571  

134.528  

   

Swiss  Re  

104.865  

141.569  

8.100  

647.306  

   

Zurich  Insurance  Group  

72.369  

49.234  

8.917  

163.635  

   

Swiss  Life  Holding  

247.860  

286.930  

32.000  

1108.750  

   

Legal  General  Group  

117.348  

182.632  

9.375  

981.412  

   

Muenchener  

43.939  

25.082  

6.500  

90.064  

   

Scor  

88.613  

60.164  

10.500  

212.566  

   

All  

94.519  

131.011  

6.500  

1108.750  

 

33    

Table   III:   Statistics   on   Explanatory   Variables.   The   table   reports   statistics   on   explanatory   variables   for   selected   insurance   companies   in   U.S.   and   Europe.   The   sample   covers   the   period   July   2002   to   June   2012.   For   company   specific  variables  in  Panels  A  and  B,  all  figures  are  percentages  with  respect  to  the  value  of  total  assets.       Panel  A:    Investment  Portfolio  Variables   Region  

Variable  

Avg.  

St.  Dev.  

Min.  

Max.  

U.S.  

Cash  

0.768  

0.841  

0.000  

4.800  

   

Short-­‐Term  Inv  

3.141  

3.492  

0.000  

23.200  

   

Fixed  Income  

49.031  

13.341  

21.700  

73.700  

   

Equity  Inv  

1.956  

2.520  

0.000  

11.800  

   

Loans  

2.195  

2.789  

0.000  

10.800  

Europe  

Cash  

3.629  

3.007  

0.000  

16.500  

   

Short-­‐Term  Inv  

0.624  

1.539  

0.000  

10.400  

   

Fixed  Income  

37.904  

10.652  

0.000  

60.100  

   

Equity  Inv  

9.581  

13.186  

0.000  

64.000  

   

Loans  

5.075  

6.994  

0.000  

34.500  

Panel  B:  Other  Firm-­‐Specific  Variables   Region  

Variable  

Avg.  

St.  Dev.  

Min.  

Max.  

U.S.  

Debt  

5.104  

3.419  

0.600  

22.800  

       

Insurance  Liab.  

70.779  

12.307  

0.000  

95.318  

Volatility  

36.400  

37.000  

9.400  

283.600  

   

Distance  to  Default  

4.037  

2.747  

-­‐0.877  

13.779  

Europe  

Debt  

6.937  

5.765  

0.000  

19.230  

   

Insurance  Liab.  

68.920  

23.540  

0.000  

95.440  

   

Volatility  

37.000  

23.100  

13.200  

134.400  

   

Distance  to  Default  

3.195  

4.114  

-­‐0.341  

22.460  

Region  

Variable  

Avg.  

St.  Dev.  

Min.  

Max.  

U.S.  

Risk-­‐Free  

3.794  

0.523  

2.589  

3.780  

   

Yield  Slope  

2.275  

1.010  

0.299  

2.739  

   

Swap  Spread  

0.424  

0.239  

0.164  

1.459  

   

VIX  

   

SKEW  

Europe  

Panel  C:  Macro  Variables  

19.150  

7.189  

11.570  

16.295  

115.228  

4.199  

108.270  

115.940  

Risk-­‐Free  

3.248  

0.909  

1.306  

3.208  

   

Yield  Slope  

1.196  

0.792  

-­‐0.330  

1.322  

   

Swap  Spread  

   

VIX  

   

SKEW  

0.445  

0.315  

0.118  

1.210  

25.993  

11.735  

12.377  

22.608  

115.228  

4.199  

108.270  

115.940  

     

 

34    

Table   IV:   Regression   of   Credit   Default   Swap   Spreads   for   All   Firms.   The   table   reports   the   estimates   from   the   following  specification:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܸܰܫ‬௜ǡ௧ ൅ ߛ ᇱ ܺ௜ǡ௧ ൅ ߜ ᇱ ܼ௧ ൅ ߝ௜ǡ௧   where  ‫ܵܦܥ‬௜ǡ௧  represents  the  CDS  quote  for  entity  i  at  the  end  of  period  t;  ‫ܸܰܫ‬௜ǡ௧  is  is  a  set  of  investment  portfolio   variables   available   for   entity   i   at   the   end   of   period   t;  ܺ௜ǡ௧  is   a   set   of   firm-­‐specific   variables;   and  ܼ௧  is   a   vector   of   macroeconomic   or   time   fixed-­‐effects   variables.   P-­‐values   computed   using   robust   standard   errors   are   reported   in   brackets.  The  sample  covers  the  period  July  2002  to  June  2012.        

Model  

Regressor  

(1)  

(2)  

Cash  

0.2782  

    Short-­‐Term  Inv  

(4)  

(5)  

(6)  

0.2913   0.1584  

0.0038  

0.0348  

0.0392  

[0.131]  

[0.067]   [0.152]  

[0.969]  

[0.696]  

[0.662]  

0.1666  

0.1341   0.0919  

0.0204  

0.0228  

0.0013  

   

[0.141]  

[0.154]   [0.132]  

[0.552]  

[0.571]  

[0.977]  

Fixed  Income  

0.0209  

-­‐0.0031   -­‐0.0166  

0.0225  

0.0110  

-­‐0.0017  

   

[0.102]  

[0.758]   [0.067]  

[0.025]  

[0.221]  

[0.873]  

Equity  Inv  

-­‐0.0128  

0.0033   0.0235  

-­‐0.0032  

0.0023  

0.0058  

   

[0.498]  

[0.714]   [0.005]  

[0.689]  

[0.749]  

[0.398]  

Loans  

0.0500  

0.0298   -­‐0.0114  

0.0126  

0.0072  

-­‐0.0121  

   

[0.172]  

[0.272]   [0.423]  

[0.158]  

[0.350]  

[0.326]  

Debt  

   

    Insurance  Liab.   Volatility  

   

       

       

   

    Dist.  To  Default  

       

   

(3)  

   

   

0.0600   [0.137]  

0.0161  

0.0110  

0.0113  

   

[0.010]  

[0.061]  

[0.020]  

3.9760  

3.9370  

4.7618  

   

[0.000]  

[0.000]  

[0.000]  

-­‐0.0632  

-­‐0.0498  

-­‐0.0164  

       

   

   

   

   

   

   

[0.006]  

[0.025]  

[0.555]  

Risk-­‐Free  Rate  

   

-­‐0.2430  

   

   

-­‐0.2998  

   

   

   

[0.058]  

   

   

[0.002]  

   

Yield  Slope  

   

-­‐0.4336  

   

   

-­‐0.2488  

   

   

   

[0.001]  

   

   

[0.000]  

   

Implied  Volatility  

   

0.0112  

   

   

-­‐0.0069  

   

   

   

[0.401]  

   

   

[0.547]  

   

Swap  Spread  

   

1.0618  

   

   

0.0355  

   

   

   

[0.021]  

   

   

[0.866]  

   

Skew  

   

0.0583  

   

   

0.0281  

   

   

   

[0.002]  

   

   

[0.022]  

   

YES  

YES  

YES  

YES  

YES  

YES  

NO  

NO  

YES  

NO  

NO  

YES  

0.005  

0.034  

0.336  

0.542  

0.539  

0.661  

725  

725  

725  

725  

725  

725  

Firm  Fixed   Effects   Time  Fixed   Effects   ܴଶ     Number  Obs.  

 

35    

0.0801   [0.079]  

       

   

0.0954   [0.030]  

   

Table   V:   Regression   of   Credit   Default   Swap   Spreads   for   All   Firms.   The   table   reports   the   estimates   from   the   following  specification:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܸܰܫ‬௜ǡ௧ିଵ ൅ ߛ ᇱ ܺ௜ǡ௧ିଵ ൅ ߜ ᇱ ܼ௧ିଵ ൅ ߝ௜ǡ௧   where  ‫ܵܦܥ‬௜ǡ௧  represents  the  CDS  quote  for  entity  i  at  the  end  of  period  t;  ‫ܸܰܫ‬௜ǡ௧ିଵ  is  is  a  set  of  lagged  investment   portfolio  variables  available  for  entity  i  at  the  end  of  period  t-­‐1;  ܺ௜ǡ௧ିଵ  is  a  set  of  firm-­‐specific  variables  with  lags;   and  ܼ௧ିଵ  is   a   vector   of   past   values   of   macroeconomic   or   time   fixed-­‐effects   variables.   P-­‐values   computed   using   robust  standard  errors  are  reported  in  brackets.  The  sample  covers  the  period  July  2002  to  June  2012.        

Model  

Regressor  

(1)  

Casht-­‐1  

(3)  

(5)  

(6)  

0.1101  

0.121146  

-­‐0.0482  

0.0155  

0.00872  

   

[0.200]  

[0.215]  

[0.199]  

[0.603]  

[0.870]  

[0.925]  

 Short-­‐Term  Inv  t-­‐1  

0.1649  

0.0971  

0.0959  

0.0306  

0.0300  

0.0300  

   

[0.123]  

[0.086]  

[0.099]  

[0.309]  

[0.429]  

[0.475]  

Fixed  Income  t-­‐1  

0.01447  

-­‐0.0136  

-­‐0.0156  

0.0152  

0.0059  

-­‐0.0011  

   

[0.276]   -­‐ 0.00687   [0.690]  

[0.205]  

[0.098]  

[0.146]  

[0.575]  

[0.925]  

0.0186  

0.0209  

0.0025  

0.0042  

0.0067  

[0.009]  

[0.013]  

[0.771]  

[0.516]  

[0.220]  

Loans  t-­‐1  

0.0384  

-­‐0.0048  

-­‐0.0062  

0.0007  

-­‐0.0031  

-­‐0.0075  

   

[0.275]  

[0.745]  

[0.695]  

[0.948]  

[0.781]  

[0.657]  

   

   

   

0.1194  

0.0959  

0.0855  

[0.012]  

[0.051]  

[0.066]  

0.01887  

0.00802  

0.01039  

[0.008]  

[0.193]  

[0.108]  

   

Debt  t-­‐1      

   

Insurance  Liab  t-­‐1  

   

   

   

   

       

       

   

Volatility  t-­‐1  

   

   

   

3.434  

3.054  

3.448  

   

   

   

   

[0.000]  

[0.000]  

[0.000]  

Dist.  To  Default  t-­‐1  

   

   

   

-­‐0.0408  

-­‐0.06245  

-­‐0.0172  

   

   

   

   

[0.134]  

[0.020]  

[0.555]  

Risk-­‐Free  Rate  t-­‐1  

   

-­‐0.16553      

   

-­‐0.10909      

   

   

[0.304]      

   

[0.240]      

Yield  Slope  t-­‐1  

   

-­‐0.1074      

   

-­‐0.2595      

   

   

[0.091]      

   

[0.004]      

Implied  Volatility  t-­‐1    

-­‐0.02646      

   

-­‐0.0109      

[0.392]      

   

[0.389]      

   

   

Swap  Spread  t-­‐1  

   

0.0133      

   

[1.323]      

   

   

[0.983]      

   

[0.001]      

Skew  t-­‐1  

   

   

   

Firm  Fixed  Effects   ܴଶ     Number  Obs.  

-­‐0.0108  

   

[0.901]       YES  

Time  Fixed  Effects  

YES  

       

YES  

0.0187  

   

[0.098]       YES  

YES  

YES  

NO  

NO  

YES  

NO  

NO  

YES  

0.002  

0.348  

0.340  

0.422  

0.433  

0.556  

717  

717  

717  

700  

700  

700  

 

36    

(4)  

0.20236  

Equity  Inv  t-­‐1  

   

(2)  

Table  VI:  Regression  of  Credit  Default  Swap  Spreads  for  US  Firms.  The  table  reports  the  estimates  from  the   following  specification:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܸܰܫ‬௜ǡ௧ ൅ ߛ ᇱ ܺ௜ǡ௧ ൅ ߜ ᇱ ܼ௧ ൅ ߝ௜ǡ௧   where  ‫ܵܦܥ‬௜ǡ௧  represents  the  CDS  quote  for  entity  i  at  the  end  of  period  t;  ‫ܸܰܫ‬௜ǡ௧  is  is  a  set  of  investment  portfolio   variables   available   for   entity   i   at   the   end   of   period   t;  ܺ௜ǡ௧  is   a   set   of   firm-­‐specific   variables;   and  ܼ௧  is   a   vector   of   macroeconomic   or   time   fixed-­‐effects   variables.   P-­‐values   computed   using   robust   standard   errors   are   reported   in   brackets.  The  sample  covers  the  period  July  2002  to  June  2012.        

Model   (1)  

(3)  

(5)  

(6)  

1.9980  

1.4809  

0.9059  

0.8447  

0.8140  

   

[0.013]  

[0.030]  

[0.039]  

[0.027]  

[0.037]  

[0.022]  

Short-­‐Term  Inv  

0.1445  

0.1372  

0.1021  

0.0410  

0.0405  

0.0160  

   

[0.151]  

[0.094]  

[0.060]  

[0.277]  

[0.329]  

[0.634]  

Fixed  Income  

0.0537  

0.0080  

0.0277  

0.0605  

0.0379  

0.0394  

   

[0.056]  

[0.831]  

[0.501]  

[0.001]  

[0.100]  

[0.068]  

Equity  Inv  

0.0136  

-­‐0.0478  

0.0946  

0.0624  

0.0703  

0.0128  

   

[0.659]  

[0.475]  

[0.270]  

[0.096]  

[0.237]  

[0.838]  

Loans  

0.3318  

0.2013  

0.1303  

0.0614  

0.0012  

0.0163  

   

[0.174]  

[0.354]  

[0.430]  

[0.128]  

[0.980]  

[0.771]  

0.1622  

0.1525  

0.1131  

   

[0.000]  

[0.001]  

[0.028]  

0.0275  

0.0361  

0.0303  

   

[0.313]  

[0.211]  

[0.232]  

3.2788  

3.4853  

5.0020  

   

[0.000]  

[0.000]  

[0.001]  

-­‐0.0549  

-­‐0.0053  

-­‐0.0557  

[0.850]  

[0.082]  

   

   

       

Insurance  Liab.  

   

   

   

   

   

       

       

Dist.  To  Default  

       

   

Volatility  

   

       

   

   

   

   

   

   

[0.120]  

Risk-­‐Free  Rate  

   

-­‐0.1405  

   

   

0.1872  

   

   

   

[0.470]  

   

   

[0.179]  

   

Yield  Slope  

   

-­‐0.4304  

   

   

-­‐0.1213  

   

   

   

[0.004]  

   

   

[0.212]  

   

Implied  Volatility  

   

-­‐0.0193  

   

   

0.0040  

   

   

   

[0.377]  

   

   

[0.767]  

   

Swap  Spread  

   

1.3543  

   

   

-­‐0.5409  

   

   

   

[0.035]  

   

   

[0.099]  

   

Skew  

   

0.0230  

   

   

-­‐0.0020  

   

   

   

[0.076]  

   

   

[0.812]  

   

Firm  Fixed-­‐Effects  

YES  

YES  

YES  

YES  

YES  

YES  

Time  Fixed-­‐Effects  

NO  

NO  

YES  

NO  

NO  

YES  

0.101  

0.162  

0.372  

0.587  

0.633  

0.732  

442  

442  

442  

442  

442  

442  

ܴଶ     Number  Obs.  

 

37    

(4)  

2.2325  

Debt  

   

(2)  

  Cash  

Table  VII:  Regression  of  Credit  Default  Swap  Spreads  for  European  Firms.  The  table  reports  the  estimates  from   the  following  specification:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܸܰܫ‬௜ǡ௧ ൅ ߛ ᇱ ܺ௜ǡ௧ ൅ ߜ ᇱ ܼ௧ ൅ ߝ௜ǡ௧   where  ‫ܵܦܥ‬௜ǡ௧  represents  the  CDS  quote  for  entity  i  at  the  end  of  period  t;  ‫ܸܰܫ‬௜ǡ௧  is  is  a  set  of  investment  portfolio   variables   available   for   entity   i   at   the   end   of   period   t;  ܺ௜ǡ௧  is   a   set   of   firm-­‐specific   variables;   and  ܼ௧  is   a   vector   of   macroeconomic   or   time   fixed-­‐effects   variables.   P-­‐values   computed   using   robust   standard   errors   are   reported   in   brackets.  The  sample  covers  the  period  July  2002  to  June  2012.        

Model   (1)  

  Cash  

0.1217  

(3)  

0.1597  

0.1249  

(5)  

-­‐0.0125  

0.0320  

(6)   0.0330  

[0.362]  

[0.043]  

[0.039]  

[0.877]  

[0.657]  

[0.723]  

Short-­‐Term  Inv  

-­‐0.0466  

-­‐0.2424  

-­‐0.1795  

-­‐0.0402  

-­‐0.1511  

-­‐0.1302  

   

[0.608]  

[0.022]  

[0.046]  

[0.521]  

[0.057]  

[0.207]  

Fixed  Income  

0.0203  

-­‐0.0022  

-­‐0.0082  

0.0051  

-­‐0.0067  

-­‐0.0099  

   

[0.089]  

[0.799]  

[0.359]  

[0.665]  

[0.542]  

[0.296]  

Equity  Inv  

-­‐0.0122  

0.0152  

0.0174  

0.0027  

0.0159  

0.0135  

   

[0.507]  

[0.079]  

[0.009]  

[0.809]  

[0.026]  

[0.139]  

Loans  

0.0049  

-­‐0.0064  

-­‐0.2580  

0.0038  

0.0002  

-­‐0.0092  

   

[0.484]  

[0.658]  

[0.127]  

[0.668]  

[0.985]  

[0.538]  

0.0255  

0.0244  

0.0254  

   

[0.297]  

[0.279]  

[0.209]  

0.0133  

0.0052  

0.0017  

   

[0.033]  

[0.266]  

[0.939]  

3.8876  

3.9050  

4.0820  

   

[0.008]  

[0.015]  

[0.048]  

-­‐0.0416  

-­‐0.0489  

-­‐0.0574  

   

   

       

Insurance  Liab.  

   

   

   

   

   

       

       

Dist.  To  Default  

       

   

Volatility  

   

       

   

   

   

   

   

   

[0.005]  

[0.033]  

[0.047]  

Risk-­‐Free  Rate  

   

-­‐0.1848  

   

   

-­‐0.3053  

   

   

   

[0.072]  

   

   

[0.012]  

   

Yield  Slope  

   

0.2170  

   

   

0.0301  

   

   

   

[0.081]  

   

   

[0.732]  

   

Implied  Volatility  

   

0.0339  

   

   

-­‐0.0211  

   

   

   

[0.030]  

   

   

[0.129]  

   

Swap  Spread  

   

1.0750  

   

   

1.0150  

   

   

   

[0.000]  

   

   

[0.000]  

   

Skew  

   

0.0364  

   

   

0.0225  

   

   

   

[0.039]  

   

   

[0.129]  

   

Firm  Fixed-­‐Effects  

YES  

YES  

YES  

YES  

YES  

YES  

Time  Fixed-­‐Effects  

NO  

NO  

YES  

NO  

NO  

YES  

0.026  

0.348  

0.522  

0.401  

0.469  

0.545  

280  

280  

280  

280  

280  

280  

ܴଶ     Number  Obs.  

 

38    

(4)  

   

Debt  

   

(2)  

Table  VIII:  Regression  of  CDS  Spreads  with  Exogenous  Variations  in  Cash  for  US  Firms.  The  table  reports  the  estimates  from  the  following  specification:   ‫ܵܦܥ‬௜ǡ௧ ൌ ߙ௜ ൅ ߚଵ ‫ܪܵܣܥ‬௜ǡ௧ ൅ ߚଶ ‫ ܫ‬൅ ߚଷ ‫ܪܵܣܥ כ ܫ‬௜ǡ௧ ൅ ߛ ᇱ ܺ௜ǡ௧ ൅ ߜ ᇱ ܼ௧ ൅ ߤ௜ǡ௧   where  ‫ܵܦܥ‬௜ǡ௧  represents  the  CDS  quote  for  entity  i  at  the  end  of  period  t;  ‫ܪܵܣܥ‬௜ǡ௧  is  the  cash  holdings  to  total  assets  ratio  for  entity  i  at  the  end  of  period  t;  ‫ ܫ‬ is   an   indicator   function   equal   to   one   if   either  ȟ‫ܪܵܣܥ‬௜ǡ௧ ൐ Ͳ,  ȟ‫ܤܣܫܮ‬௜ǡ௧ ൏ Ͳ,   or   both;  ܺ௜ǡ௧  is   a   set   of   firm-­‐specific   variables   that   includes   all   other   investment   variables;  and  ܼ௧  is  a  vector  of  macroeconomic  or  time  fixed-­‐effects  variables.  P-­‐values  computed  using  robust  standard  errors  are  reported  in  brackets.  The   sample  covers  the  period  July  2002  to  June  2012.         Regressor  

Model   (1)  

(2)  

(3)  

(4)  

(5)  

(7)  

(8)  

(9)  

(10)  

(11)  

(12)  

‫ ݄ݏܽܥ‬ 

2.9267  

2.0140  

2.2591  

1.6121  

2.3627  

1.7026  

1.3281  

1.0350  

0.9608  

0.9070  

0.9920  

0.9135  

   

[0.008]  

[0.023]  

[0.017]  

[0.035]  

[0.017]  

[0.031]  

[0.048]  

[0.059]  

[0.046]  

[0.026]  

[0.044]  

[0.021]  

ͳሼ୼஼௔௦௛வ଴ሽ  

0.6618  

0.3720      

0.3631  

0.1587      

   

[0.099]  

[0.131]  

[0.215]  

[0.446]  

‫ ݄ݏܽܥ‬ൈ ͳሼ୼஼௔௦௛வ଴ሽ  

-­‐1.1455  

-­‐0.6444      

-­‐0.6278  

-­‐0.2858      

   

[0.042]  

[0.123]  

   

   

   

   

[0.178]  

[0.457]  

   

ͳሼ୼௅௜௔௕ழ଴ሽ  

   

   

0.1217  

-­‐0.1437  

   

   

   

   

0.1832  

0.2120      

   

   

   

[0.569]  

[0.531]  

   

   

   

   

[0.301]  

[0.116]  

‫ ݄ݏܽܥ‬ൈ ͳሼ୼௅௜௔௕ழ଴ሽ  

   

   

-­‐0.3959  

-­‐0.2164  

   

   

   

   

-­‐0.3569  

-­‐0.2908      

   

   

   

[0.103]  

[0.333]  

   

   

   

   

[0.179]  

[0.115]  

   

   

ͳሼ୼஼௔௦௛வ଴Ƭ୼௅௜௔௕ழ଴ሽ  

   

   

   

   

0.4380  

0.3120      

   

   

   

0.3612  

0.2974  

   

   

   

   

   

[0.077]  

[0.126]  

   

   

   

[0.122]  

[0.083]  

‫ ݄ݏܽܥ‬ൈ ͳሼ୼஼௔௦௛வ଴Ƭ୼௅௜௔௕ழ଴ሽ  

   

   

   

   

-­‐0.7339  

-­‐0.4929      

   

   

   

-­‐0.4743  

-­‐0.3490  

   

   

   

   

   

[0.005]  

[0.012]  

   

   

   

   

[0.089]  

[0.045]  

Firm  Fixed-­‐Effects  

YES  

YES  

YES  

YES  

YES  

YES  

YES  

YES  

YES  

YES  

YES  

YES  

Firm  Specific  Controls  

NO  

NO  

NO  

NO  

NO  

NO  

YES  

YES  

YES  

YES  

YES  

YES  

Macro  Controls  

YES  

NO  

YES  

NO  

YES  

NO  

YES  

NO  

YES  

NO  

YES  

NO  

Time  Fixed-­‐Effects   ܴଶ     Number  Obs.  

       

   

   

       

       

       

   

       

       

   

       

       

       

   

       

   

       

NO  

YES  

NO  

YES  

NO  

YES  

NO  

YES  

NO  

YES  

NO  

YES  

0.172  

0.357  

0.175  

0.378  

0.168  

0.366  

0.628  

0.724  

0.634  

0.731  

0.631  

0.727  

442  

442  

442  

442  

442  

442  

442  

442  

442  

442  

442  

442  

   

39    

(6)