Insurers’ investment strategies : pro- or countercyclical?
Date: 2020-05-29
Institutions: European Central Bank
Subjects: financial stability, insurance company, investment, pension fund, public debt

Linda Fache Rousová, Margherita Giuzio 

Working Paper Series 

Insurers’ investment strategies: 
pro- or countercyclical? 

No 2299 / July 2019 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

Abstract 

Traditionally, insurers are seen as stabilisers of financial markets that act countercyclically by 

buying  assets  whose  price  falls.  Recent  studies  challenge  this  view  by  providing  empirical 

evidence  of  procyclicality.  This  paper  sheds  new  light  on  the  underlying  reasons  for  these 

opposing  views.  Our  model  predicts  procyclicality  when  prices  fall  due  to  increasing  risk 

premia,  and  countercyclicality in response to rises in  the  risk-free rate.  Using granular  data 

on  insurers’  government  bond  holdings,  we  validate  these  predictions  empirically.  Our 

findings  contribute  to  the  current  policy  discussion  on  macroprudential  measures  beyond 

banking. 

JEL classification: G01, G11, G12, G22, G23 

Keywords: insurance companies, cyclicality, portfolio allocation, financial stability, sovereign 

debt crisis. 

ECB Working Paper Series No 2299 / July 20191 
 
 
 
 
 
Non-technical summary 

Insurance  companies  are  large  institutional  investors.  They  provide  the  bulk  of  long-term 

funding to the economy. The sector has been growing in recent years and has become more 

interconnected  with  banks  and  other  financial  intermediaries.  Therefore,  insurers’  response 

to changes in asset prices could have a significant direct impact on the availability of funding 

sources to the economy. Moreover, in crisis periods, large asset sales could amplify price falls 

and  negatively  affect  other  investors  holding  the  same  assets,  potentially  threatening  the 

stability  of  the  financial  system.  In  boom  periods,  on  the  other  hand,  if  insurers  buy  assets 

whose value is rising, they may contribute to the development of asset price bubbles.  

Traditionally,  insurers  have  been  considered  stabilisers  of  financial  markets.  As  long-term 

investors,  they  typically  hold  assets  until  maturity  and  are  indifferent  to  short-term  price 

movements.  Therefore,  they  are  expected  to  respond  countercyclically  to  changes  in  asset 

prices,  i.e.  to  buy  assets  whose  value  declines  (and  vice-versa).  Recent  studies,  however, 

challenge  this view  by  providing empirical evidence  of  the  procyclical investment behaviour 

of insurance firms, especially during crises.  

In  this  paper,  we  provide  a  theoretical  framework  that  helps  explain  some  of  the  mixed 

results in  the literature.  We  argue that  it is  the underlying driver  of  a price  change,  rather 

than  just  the  direction,  that  matters.  In  particular,  we  develop  a  theoretical  model  to 

distinguish  between  the  effects  of  changes  in  the  risk-free  interest  rate  and  risk  premia  on 

insurers’  equity  valuations  and  thus  on  their  investment  behaviour.  When  prices  fall  due  to 

increasing  risk  premia,  our  model  predicts  procyclical  investment  behaviour.  By  contrast,  it 

suggests that countercyclicality occurs in response to price drops driven by a rising risk-free 

interest rate.  

We test these predictions empirically by using security-by-security data on government bond 

holdings of euro area insurance companies from 2009 to 2016. In line with the predictions, we 

estimate that higher risk premia have a negative and significant effect on insurers’ holdings 

of  government  bonds  (procyclicality)  whereas  a  higher  risk-free  interest  rate  has  a  positive 

and  significant  effect  of  on  those  holdings  (countercyclicality).  Moreover,  we  show  that 

during  the  euro  area  sovereign  debt  crisis  the  procyclical  effect  of  risk  premia  tended  to 

ECB Working Paper Series No 2299 / July 20192 
 
 
 
outweigh the countercyclical effect of the risk-free rate. Interestingly, these results only hold 

for  insurers’  holdings  of  foreign government bonds,  while  domestic  bonds appear  to receive 

preferential treatment: insurers tend to respond countercyclically (rather than procyclically) 

to changes in the risk premia of their own sovereign.  

Our findings are not without policy implications. By providing evidence of insurers’ procyclical 

behaviour  during  the  euro  area  sovereign  debt  crisis,  our  results  contribute  to  the  current 

policy  discussion  on  macroprudential  measures  beyond  banking  by  underlining  the  need  for 

such  measures.  Moreover,  since  our  paper  sheds  light  on  the  underlying  mechanism  of  such 

behaviour, it can be instrumental for the development of an effective policy instrument that 

helps address such behaviour. However, one shortcoming of our analysis is that it is too early 

for  us  to  empirically  assess  insurers’  investment  behaviour  under  the  current  regulatory 

regime,  Solvency II,  as it  only  entered  into  force in  January 2016.  In particular, this regime 

already includes measures of a macroprudential nature, but their effectiveness during a crisis 

is yet to be tested in practice. 

ECB Working Paper Series No 2299 / July 201931. INTRODUCTION

Traditionally,  insurers  have  been  viewed  as  stabilisers  of  financial  markets  that  respond 

countercyclically  to  changes  in  asset  prices.  Owing  to  the  long  duration  of  their  liabilities, 

they  are  expected  to  hold  assets  until  maturity  and  buy  assets  whose  value  declines.  Such 

investment  behaviour  is  particularly  relevant  from  a  financial  stability  perspective  since 

insurers are strongly interconnected with other financial intermediaries and play a key role in 

the long-term financing of the economy. For instance, more than 40% of euro area investment 

in bonds with maturity over 10 years is provided by the insurance sector (Figure 1). 

Figure  1:  Euro  area  holdings  of  debt  securities,  broken  down  by  residual 

maturity and holder sector (end-2016) Q4 

(percentage of total securities holdings) 

Source: ECB Securities Holdings Statistics and authors’ calculations. 

Our  paper  challenges  this  traditional  view  by  unearthing  the  reasons  why  insurance 

investment  behaviour  can  turn  procyclical  in  distress  periods.  The  framework  we  develop 

hinges on the theoretical insight that it is the underlying driver of a price change (rather than 

just the direction) that matters for insurers’ investment behaviour. 

More specifically, we present a stylised model, in which we separate the effects of the risk-

free interest rate from those of the risk premia on insurers’ equity valuations under a market-

consistent  regulatory  regime.  A  rise  in  the  risk-free  rate  pushes  up  the  value  of  insurers’ 

equity,  whereas  an  increase  in  risk  premia  lowers  it.  Through  their  different  effects  on 

ECB Working Paper Series No 2299 / July 20194equity,  we  argue  that  the  two  factors  can  also  imply  different  investment  behaviours  in 

response to a price change. When prices fall due to increasing risk premia, our model predicts 

procyclical  investment  behaviour  as  insurers  attempt  to  restore  their  financial  position.  On 

the other hand, it predicts countercyclicality in response to price drops driven by a rising risk-

free rate. These theoretical insights help explain some arguments put forward in the existing 

literature such as that of Cochrane (2017), who suggests that financial institutions may decide 

to  sell  assets  for  two  reasons:  either  they  think  that  the  price  drop  is  related  to  a  real 

increase  in  the  long-term  default  probability  of  the  counterparty,  or  their  risk-bearing 

capacity is declining due to increasing risk aversion. 

We  validate  our  predictions  empirically  using  security-by-security  data  on  government  bond 

holdings of euro area insurers from 2009 to 2016. We estimate that higher risk premia have a 

negative  and  significant  effect  on  euro  area  insurers’  holdings  of  government  bonds 

(procyclical behaviour) and that a higher risk-free interest rate has a positive and significant 

effect  on  those  holdings  (countercyclical  behaviour).  Moreover,  we  show  that  during  the 

outbreak of the euro area sovereign debt crisis the procyclical effect of risk premia tended to 

outweigh the countercyclical effect of the risk-free rate. At the same time, these results hold 

only  for  insurers’  holdings  of  foreign  government  bonds,  while  domestic  bonds  appear  to 

receive  preferential  treatment.  In  particular,  we  find  that  insurers  tend  to  respond 

countercyclically  (rather  than  procyclically)  to  changes  in  the  risk  premia  of  their  own 

sovereign. 

We  confirm  that  our  empirical  results  are  robust  to  different  model  specifications  and 

estimation approaches.  Specifically,  we pay  particular  attention to  the  standard problem of 

empirical studies that estimate the impact of price changes on investment behaviour, which 

is  the  potential endogeneity  of  price  changes. One concern  is that our  estimates  are  biased 

due  to  the  possible  omission  of  variables  that  affect  both  the  two  explanatory  variables  of 

interest  and  insurers’  holdings.  To  address  this  concern,  we  include  a  number  of  control 

variables, such as measures of credit quality and economic fundamentals, the volumes of the 

ECB’s recent purchases of government bonds as well as very granular fixed effects. 

Another concern is that our estimates could be biased due to reverse causality, i.e. a causal 

effect of insurers’ holdings on bond prices. To tackle this problem,  we use the instrumental 

ECB Working Paper Series No 2299 / July 20195variable  (IV)  approach.  We  instrument  the  euro  risk-free  interest  rate  curve  with  the 

corresponding US curve, and the risk premia with inflation rates in the issuer country. These 

instruments  are  likely  to  be  exogenous  to  euro  area  insurers’  holdings  of  government  bonds 

but at the same time they affect the risk-free rate and risk premia. In addition to tackling the 

potential endogeneity problem, we conduct a wide range of further robustness checks. 

Our  paper  contributes  to  the  existing  literature  on  insurers’  investment  behaviour,  which  is 

scarce  and  often  limited  to  country-specific  studies.  Moreover,  the  evidence  on  buy/sell 

decisions of insurance companies in response to price changes is mixed and the papers often 

fail to recognise that the investment pattern may vary with market conditions and/or type of 

assets.  For  instance, Timmer  (2018)  suggests that  German insurance companies and  pension 

funds  (ICPFs)  act  countercyclically  in  the  sovereign  bond  market,  including  during  times  of 

crisis  such  as  the  European  sovereign  debt  crisis.  These  findings  are  supported  by  earlier 

studies such as Grinblatt and Keloharju (2000) on Finnish financial and insurance institutions, 

and  De  Haan  and  Kakes  (2011)  on  Dutch  institutional  investors.  By  contrast,  Bijlsma  and 

Vermeulen  (2016)  find  that  Dutch  insurers  sold  distressed  euro  area  sovereign  bonds  during 

the  European  sovereign  debt  crisis,  thus  acting  procyclically.  Their  results  are  supported  by 

earlier findings by Impavido and Tower (2009) on life insurance companies, Ellul, Jotikasthira 

and  Lundblad  (2011)  and  Merrill  et  al.  (2012)  on  insurers  subject  to  high  regulatory 

constraints,  a  Bank  of  England  (2014)  study  on  life  insurance  companies  and  pension  funds, 

and Duijm and Bisschop (2018) on Dutch ICPFs.  

Since  our  paper  focuses  on  insurers’  holdings  of  government  bonds  during  the  euro  area 

sovereign  debt  crisis,  it  is  closely  linked  to  the  work  of  Timmer  (2018)  and  Bijlsma  and 

Vermeulen (2016). Compared to these country-specific studies, our results can be considered 

more  robust  as  they  are  based  on  new  granular  data  from  the  ECB’s  Securities  Holdings 

Statistics  (SHS),  which  cover  all  19  euro  area  countries.  Moreover,  our  paper  not  only 

highlights  the  changing  nature  of  insurers’  investment  behaviour  in  relation  to  market 

conditions  and  type  of  assets  (domestic/foreign),  but  also  underlines  the  importance  of  the 

regulatory  regimes  in  place.  In  particular,  our  results  also  suggest  that  insurers’  investment 

behaviour  is  more  sensitive  to  price  changes  when  insurers  operate  under  a  more  market-

based regime, such as the one in the Netherlands prior to the introduction of Solvency II. This 

helps explain the mixed results found in the two studies.  

ECB Working Paper Series No 2299 / July 20196 
 
In  line  with  our  paper,  two  recent  studies  highlight  that  the  investment  behaviour  of 

insurance  companies  and  other  institutional  investors  may  turn  from  countercyclical  to 

procyclical  in  periods  of  market  distress.  Using  transaction-level  data  on  the  UK  corporate 

bond market, Czech and Roberts-Sklar (2017) find that insurance companies, hedge funds and 

asset  managers  are  typically  acting  countercyclically,  but  that  the  behaviour  of  asset 

managers turned procyclical during the 2013 ‘taper tantrum’. However, they fail to provide a 

theoretical  motivation  for  such  a  reversal.  This  is  different  in  Ellul  et  al.  (2018),  which 

provides  evidence  of  the  procyclical  investment  behaviour  of  US  life  insurers  with  a  large 

variable  annuity  business.  This  paper  argues  that  due  to  the  high  minimum  return  that 

insurers  offer  to  policyholders  and  the  related  hedging  activity,  these  insurance  companies 

may  stop  acting  as  stabilisers  in  periods  of  market  distress  and  may  themselves  become  a 

source of significant selling activity. Even if the mechanism is different from ours, as US and 

euro  area  insurers  have  different  business  models  and  operate  under  different  regulatory 

regimes, our paper is in the spirit of Ellul et al. (2018) since it provides both theoretical and 

empirical  insights  about  the  investment  behaviour  of  insurance  companies  and  its  changing 

nature. 

Finally, Domanski et al. (2017) show that insurers’ investment strategy can change over time 

in response to changes in asset-liability duration mismatch rather than acute market distress. 

Specifically, they provide evidence that when long-term rates fell in the period between 2009 

and  2014,  German  insurance  companies  purchased  more  bonds  with  a  higher  duration.  This 

‘hunt  for  duration’  works  as  follows:  when  (long-term)  interest  rates  fall,  the  duration  gap 

between assets and liabilities becomes increasingly negative and insurers tend to extend the 

maturity  of  bond  portfolios  in  order  to  contain  it.  Our  paper  cannot  empirically  investigate 

the  relationship  between  insurers’  duration  gap  and  their  investment  behaviour,  due  to  the 

lack  of  time  series data on  the duration of  insurers’  liabilities.  However,  even if  not  tested 

empirically,  our  theoretical  framework  suggests  that  the  (negative)  duration  gap  and  the 

duration of assets increase insurers’ sensitivity to asset price changes. 

The  remainder  of  the  paper  is  organised  as  follows.  Section  2  discusses  the  main 

characteristics  of  the  euro  area  insurance  sector  and  develops  the  notion  of  a  stylised  euro 

area  insurer.  Section  3  introduces  the  theoretical  framework,  while  Section  4  outlines  the 

ECB Working Paper Series No 2299 / July 20197empirical  model  and  describes  the  data  we  use  to  estimate  it.  Section  5  presents  the 

empirical results. Finally, Section 6 concludes. 

2. The euro area insurance sector

The investment behaviour of an insurer can be influenced by various factors. These include – 

but are not limited to – the type of firm and its business model, the structure of the balance 

sheet, the investment preferences of its management and stakeholders, market developments 

and the regulatory framework under which an insurance firm operates. A recent study by the 

Bank  of  England  and  Procyclicality  Working  Group  (2014)  highlights,  for  instance,  the 

importance of liability characteristics, regulation, accounting and valuation methods as well 

as industry practices for insurers’ asset allocations.  

Starting  with  the  type  of  firm,  the  euro  area  insurance  sector  comprises  four  broad 
categories:  life  insurers,  non-life  insurers,  composite  insurers4  and  reinsurers.  In  terms  of 

total assets, life insurers represent the largest category (42%), closely followed by composites 

(37%),  whereas  the  size  of  non-life  insurers  (14%)  and  reinsurers  (8%)  is  relatively  limited 

(Figure 2). The importance of the life insurance business in the euro area is, however, much 

greater  than  these  figures  suggest.  One  reason  is  that  the  technical  reserves  of  composite 

insurers are dominated by the life insurance products. In addition, more than half of non-life 

insurance products are considered to be technically “similar to life”, thus those products are 

also  recorded  under  life  insurance  technical  reserves.  As  a  result,  around  91%  of  euro  area 

insurers’ technical reserves relate to life-type insurance businesses (Figure 3).  

4 Composite insurers offer both life and non-life insurance products. 

ECB Working Paper Series No 2299 / July 20198Figure  2:  Total  assets  of  euro  area 

Figure 3: Euro area insurance technical reserves 

insurers – by type of firm (end-2016) 

– by type of business (end-2016)

Source:  ECB’s  insurance  balance  sheet  data 

Source:  ECB’s  insurance  balance  sheet  data  and 

and authors’ calculations. 

authors’ calculations.  

Regarding investment preferences, the portfolios of euro area insurers are dominated by fixed 

income assets (Figure 4). Specifically, holdings of government and corporate bonds accounted 

for around 44% of euro area insurance financial assets at the end of 2016. Another important 

asset  class  is  investment  fund  shares  (25%),  which  also  serve  as  an  important  channel  for 
investment in fixed income instruments.5 The remaining third of financial assets held by euro 

area  insurers  consists  of  shares  and  other  equity  (9%),  currency  and  deposits  (8%),  lending 

(7%) and other financial assets (7%).  

5 According to the ECB’s data on insurance corporations, bond fund shares and mixed fund shares are the two 
largest categories of investment fund shares held by euro area insurance corporations. Each category accounted for 
around a third of the insurers’ holdings of investment fund shares at the end of 2016.   

reinsurance8%life insurance42%non-life insurance14%composite insurance37% - 0.50 1.00 1.50 2.00 2.50 3.00life insurancecorporationscompositesnon-life insurancecorporationsReinsurancecorporationsLife insurance technical reservesNon-life insurance technical reservesECB Working Paper Series No 2299 / July 20199Figure 4: The composition of financial assets 

Figure 5: Duration of assets and liabilities of 

of euro area insurers (end-2016) 

European insurance companies (end-2013) 

Source:  ECB  (euro  area  accounts)  and  authors’ 

Source: EIOPA Insurance stress test (2014), Figure 

calculations. 

78.  Durations  are  based  on  Macaulay  duration 

formula 

Turning  to  other  characteristics  of  the  balance  sheet,  a  typical  life  insurer  operates  with  a 

negative duration gap, i.e. the duration of its liabilities exceeds that of its assets. According 

to EIOPA (2016a), the average modified duration of fixed income assets in the portfolio of a 

European  life  insurer  is  around  7.85  years.  On  the  other  hand,  the  average  duration  of 

liabilities  is  13.97  years  as  measured  by  Macaulay  duration,  or  8.23  years  as  measured  by 
approximate  effective  duration.6  According  to  EIOPA  (2014),  the  negative  duration  gap  is  a 
common characteristic of most insurers in EU/EEA counties (Figure 5).7  

With  respect  to  the  regulatory  framework,  euro  area  insurers  currently  operate  under  the 

Solvency  II  regime,  which  entered  into  force  in  2016.  Solvency  II  introduced  a  single 

prudential  rulebook  for  all  insurance  firms  in  the  EU  and  thus  limits  prudential  differences 

across  individual  EU  countries.  Prior  to  Solvency  II,  EU  insurance  firms  operated  under  the 

Solvency I framework, which consisted of a set of minimum regulatory requirements at the EU 

6 Macaulay duration reflects the average maturity of liabilities, while effective duration aims at estimating the 
sensitivity of liabilities to interest rate changes and also takes into account optionalities of insurance contracts (e.g. 
options for lower guarantees). For more details, see EIOPA (2016a). 
7 Insurers can hedge some of the interest rate risk on their balance sheet arising from the negative duration gap 
through the use of interest rate derivatives, which is not reflected in these numbers. Fache Rousová and Letizia 
(2018), however, show that derivative usage by euro area insurers is relatively limited on aggregate, even if interest 
rate derivatives are the main derivative class used by the insurance sector.  

debt securities44%shares and other equity9%lending7%investment fund shares25%currency and deposits8%other financial assets7%ATBEBGCYCZDEDKEEESFIFRGBGRHRHUIEITLTLUMTNLPLPTROSESISK0510152005101520LiabilitiesAssetsECB Working Paper Series No 2299 / July 201910 
 
 
 
 
 
                                                 
level, and additional regulatory requirements at an individual country level. Hence, prior to 

2016, insurers across the EU operated under a plethora of different regulatory requirements. 

However, the switch between the two regimes on 1 January 2016 cannot be considered as a 

one-off, short-term shock, as the insurance sector had been anticipating the new regime and 
gradually  adjusting  to  it  since  at  least  2009,  when  the  Solvency  II  directive  was  published.8 

Moreover,  given  the  long-term  nature of  the  (life)  insurance business,  Solvency II allows for 

the use of transitional measures for up to 16 years after its introduction in  2016 in order to 

smooth the transition to the new rules (see robustness checks in Section 5 for more details). 

Compared  to  Solvency  I,  Solvency  II  is  a  more  fair  value-based  and  more  risk-sensitive 

supervisory  regime  as  it  captures  a  number  of  key  risks,  including  market,  credit  and 

operational risks. Specifically, this regime requires insurers to value both assets and liabilities 

mark-to-market  in  order  to  provide  a  market-consistent  view  on  insurers’  solvency.  The 

market-based valuation implies that an appropriate, market-consistent discount rate is to be 

used to discount the expected cash flows, and that insurers have to hold an amount of capital 

proportional  to  the  risk  on  their  balance  sheet.  In  addition,  Solvency  II  requires  insurers  to 

invest  in  accordance  with  the  ‘prudent  person  principle’,  which  means  that  insurance 

companies have flexibility in their asset allocation decisions as long as they satisfy some high-

level general principles related to proper risk identification and management. This regime is 

significantly different from the more simplistic and non-risk-sensitive Solvency I, under which 

insurers  had  to  comply  with  certain  quantitative  limits  and  eligibility  criteria  on  their  asset 

holdings.  Moreover,  Solvency  I  did  not  provide  any  explicit  capital  requirement  related  to 

market risk, unless a more risk-sensitive requirement was embedded in the national law.  

To  sum  up,  although  the  euro  area  insurance  sector  comprises  different  types  of  firms,  we 

focus  in  this paper  on a stylised  insurance firm.  The  firm  in  question is  a  life insurer  whose 

balance  sheet  has  a  negative  duration  gap  and  whose  portfolio  is  heavily  invested  in  fixed 

income  assets.  It  operates  under  Solvency II  and  has  been  transitioning  to  this  regime  since 

2009. 

8 See Solvency II Directive. 

ECB Working Paper Series No 2299 / July 201911 
 
 
 
 
                                                 
3. Theoretical framework 

3.1 Equity valuation of a stylised euro area insurer 

The bulk of liabilities of a stylised euro area insurer are technical provisions, i.e. obligations 

to policyholders (see ECB, 2016). Under a market-consistent regulatory regime, the value of 

the technical provisions is to be discounted by a risk-free rate of return. In other words, the 

firm is required to value its liabilities as if its own default risk were zero and all obligations to 

policyholders  were  to  be  paid  out.  To  reflect  this  requirement  in  a  simple  modelling 

framework, let us assume that the market value of liabilities (denoted as L) of a typical euro 

area insurer can be modelled as a zero coupon bond with face value BL and maturity DL: 

𝐿 =   

𝐵𝐿
(1+𝑟)𝐷𝐿

 , 

(1) 

where  r  is  the  risk-free  rate  used  to  discount  technical  provisions.  We  assume  𝑟 > −1  (thus 

1
1+𝑟

 > 0).9  

When  valuing  assets,  the  appropriate  discount  rate  consists  of  both  the  risk-free  rate  of 

return and the risk premia. This is because a market-consistent regime would need to account 

for the riskiness of assets, as creditors may not deliver their payments (e.g. due to a default 

or  liquidity  squeeze).  Since  the  portfolio  of  our  stylised  insurer  is  heavily  invested  in  fixed 

income assets, let us assume that the market value of its assets (A) can be modelled as a zero 

coupon bond with face value BA and maturity DA: 

𝐴 =  

𝐵𝐴
(1+𝑟+𝑝)𝐷𝐴

 , 

(2) 

where  𝑝 > 0 denotes the bond’s risk premium.  

Since the value of the firm’s equity 𝐸 can be expressed as a difference between the values of 

its assets and liabilities, it follows that: 

9 This assumption is also realistic for the current market environment, in which some interest rates have moved into 
a negative territory. The only situation in which the assumption would not hold is if the negative interest rates 
exceeded 100%.     

ECB Working Paper Series No 2299 / July 201912 
 
 
 
 
 
 
 
 
 
                                                 
𝐸 = 𝐴 − 𝐿 =  

𝐵𝐴
(1+𝑟+𝑝)𝐷𝐴

−  

𝐵𝐿
(1+𝑟)𝐷𝐿

 . 

(3) 

The simplifying assumption of zero coupon bonds to model insurers’ assets and liabilities has 

the advantage that maturities DA and DL can be directly interpreted as the duration of assets 

and  liabilities,  respectively.  Our  model  is,  however,  only  a  special case  of  the  duration  gap 

model and, together with the resulting predictions, it can be generalised to include all assets 

and liabilities on the balance sheet with their respective durations, as shown in Saunders and 

Cornett  (1999).  In  addition  to  different  durations,  this  framework  also  allows  for  a  more 

general representation of insurer’s asset and liability portfolios, composed by a mix of assets 

and liabilities (e.g. a mix of bonds with different levels of risk premia).  

For simplicity, our model aims to capture only the basic mechanism of equity valuation under 

a  market-consistent  regime.  However,  the  regulatory  regimes  in  place  are  typically  much 

more complex. In particular, Solvency II includes long-term guarantee (LTG) measures such as 

volatility  and  matching  adjustments  that  are  not  considered  in  our  model.  These  measures 

were  designed  to  mitigate  the  impact  of  widening  credit  spreads  and,  more  generally,  of 

short-term  price  movements  on  insurers’  assets,  especially  if  those  are  unrelated  to  the 

default.  This  notwithstanding,  the  measures  do  not  fully  offset  all  short-term  price 

movements and are applied only by some euro area insurers (e.g. insurers located in countries 

where  the  regulator  allows  for  the  use  of  LTG  measures).  Therefore,  we  believe  that  our 

framework,  despite  being  simple,  can  provide  realistic 
dependencies between insurers’ equity and the risk-free rate and risk premia.10 

insights  about  the  current 

3.1.1 Insurers’ sensitivity to risk-free rate changes 

We compute the sensitivity of the market value of equity to a change in the risk-free interest 

rate  by  taking  the  first  derivative  of  Equation  (3)  with  respect  to  𝑟.  Re-arranging  the 

equation, we obtain  

𝜕𝐸
𝜕𝑟

=

1
1+𝑟

(𝐿𝐷𝐿 − 𝐴𝐷𝐴

1+𝑟
1+𝑟+𝑝

)  

(4) 

10 The results of the two scenarios considered in 2016 EIOPA stress test are in line with our framework (see EIOPA, 
2016a).  

ECB Working Paper Series No 2299 / July 201913 
 
 
 
 
 
 
 
                                                 
Since  1
1+𝑟

 > 0, the derivative is positive if and only if  

(𝐿𝐷𝐿 − 𝐴𝐷𝐴

1+𝑟
1+𝑟+𝑝

) > 0   <=>  

𝐷𝐿
𝐷𝐴

>

𝐴
𝐿

1+𝑟
1+𝑟+𝑝

(5) 

We further assume that this inequality is satisfied for our stylised firm because a typical euro 

area  insurer  has  a  negative  duration  gap  (i.e.  𝐷𝐿 > 𝐷𝐴)  and  operates  with  positive  equity. 
Moreover,  based  on  available  evidence,  it  is  plausible  to  assume  that  the  negative  duration 

gap (in its absolute value) is large enough compared to the (discounted) excess of assets over 

liabilities. Specifically, according to EIOPA’s stress test in 2014 (EIOPA, 2014), for an average 

European insurer, 𝐷𝐿/𝐷𝐴 equals to 177% (as measured by Macaulay’s duration) and A/L equals 
to  110%.  Therefore,  it  also  holds  that  𝐷𝐿
𝐷𝐴
Consequently,  it  is plausible to assume  that  𝜕𝐸
𝜕𝑟

  > 0  for  most euro area  insurers.  This in turn 

  (considering  that 

1+𝑟
1+𝑟+𝑝

1+𝑟
1+𝑟+𝑝

≤ 1  ). 

𝐴
𝐿

𝐴
𝐿

>

>

means that the value of equity of the stylised insurer increases with an increase in the risk-

free rate.  

Prediction  1a:    The  value  of  insurers’  equity  increases  with  an  increase  in  the 

risk-free rate (and vice-versa).  

It is worth highlighting  that this prediction holds in the presence of  a  negative duration gap 

and  thus  is  characteristic  only  for  certain  financial  institutions,  notably  (life)  insurance 
firms.11  In  particular,  it would  not  be  verified  for  other  financial  institutions,  such  as  banks 

and investment funds, which operate instead with a positive duration gap.             

Moreover, since our model does not consider any changes to the structure and composition of 

insurers’ liabilities in response to changes in the risk-free rate, the prediction only reflects an 

immediate (short-term) effect on insurers’ equity valuation. In particular, in case of a large 

and  persistent  increase  in  the  risk-free  rate,  an  insurance  firm  could  be  faced  with  a 

significant  risk  of  policy  lapses,  as  policyholders  could  shift  away  from  policies  with  low 

11 Kablau and Weiss (2014) point out that low interest rates are particularly important for life insurance companies, 
especially if the risk-free yield falls below the maximum technical interest rate, which is the maximum rate that they 
can typically use to calculate the premium reserves and the guaranteed return of new contracts. 

ECB Working Paper Series No 2299 / July 201914 
 
 
 
 
 
 
 
 
 
 
                                                 
guaranteed  rates,  underwritten  by  an  insurer  in  the  low-yield  regime,  to  other  types  of 

financial  products  in  view  of  higher  returns.  Such  a  scenario  could  then  have  a  negative 

overall  effect  on  the  financial  position  of  the  firm.  However,  the  size  and  nature  of  the 

overall  effect  is  difficult  to  predict  ex  ante  since  insurers  aim  to  mitigate  policy  lapses 

through  increased  profit  sharing  (i.e.  redistribution  of  a  part  of  the  investment  income  to 

policyholders).    

3.1.2 Insurers’ sensitivity to risk premia changes 

As with the risk-free rate, we compute the first derivative with respect to 𝑝 in order to obtain 

the sensitivity of the market value of equity to a change in the risk premium as 

𝜕𝐸
𝜕𝑝

= −

𝐴𝐷𝐴
1+𝑟+𝑝

.  

(6) 

Since  1 + 𝑟 + 𝑝 ≥ 0,  the  change  in  equity  corresponding  to  an  increase  in  risk  premium  is 

always negative, i.e. the value of equity decreases with an increase in risk premia. 

Prediction  2a:    The  value  of  insurers’  equity  decreases  with  an  increase  in  risk 

premia (and vice-versa).  

3.2 Insurers’ response to changes in equity valuation 

The predictions derived so far refer to the impact of interest rate changes on insurers’ equity 

valuation,  while  we  are  interested  in  the  effect  of  these  changes  on  insurers’  investment 

behaviour.  Therefore,  we  turn  in  this  section  to  a  discussion  of  how  shocks  in  equity 

propagate to the asset portfolio held by an insurance firm.  

In case of a negative shock to equity, insurers  have several ways of restoring their financial 

positions. In principle, they can act on all three parts of the balance sheet: equity, liabilities 

and assets. In the case of equity, firms can raise fresh capital in the market. However, this 

would dilute existing shareholdings  and  could  be  particularly difficult in periods of financial 

distress (Myers, 1977). They can also generate capital through retained earnings, yet such a 

process would improve capital levels only gradually (Cohen and Scatigna, 2016). With respect 

ECB Working Paper Series No 2299 / July 201915 
 
 
 
 
 
 
 
 
 
to liabilities, insurers can lower these by decreasing profit sharing, underwriting less business 

or  shifting  away  from  products  with  guaranteed  rates  of  return  towards  capital-light,  unit-

linked  policies.  However,  since  most  insurers’  liabilities  are  of  a  long  duration,  and  new 

policies  and  profit  sharing  represent  only  a  small  fraction  of  all  outstanding  liabilities,  a 

significant reduction in liabilities is also not a viable option in the short-term.   

Therefore, we can assume that an insurance firm would respond to a shock in equity by acting 

on  the  asset  side.  This  assumption  is  also  supported  by  the  existing  literature.  Das  (2017) 

shows  empirically  that  non-deposit  taking  institutions,  such  as  insurance  firms  with  higher 

capital-to-asset  ratio,  are  likely  to  purchase  assets.  The  fewer  the  constraints  in  raising 
funding, the higher the likelihood that they buy assets.12 Moreover, the theoretical model of 

Van Binsbergen and Brandt (2016) developed for asset-liability management investors such as 

insurance firms,  predicts that  they  decrease the riskiness of their portfolio in response to a 

shock that reduces their assets-to-liabilities ratio (i.e. they shift away from riskier assets to 
safer assets).13 The two studies thus suggest that the demand for risky assets can be modelled 

as a function of capital, whereby firms with higher capital (capital surplus) would have more 

cash to purchase assets and would be able to borrow more and on better terms. On the other 

hand, firms that experience a negative shock to equity (capital shortage) hold a higher level 

of risk on their balance sheet for a given level of capital, and would therefore need to de-risk 
their asset holdings.14 Against this backdrop, we assume that insurers sell/buy (risky) assets 

when  they  experience  a  negative/positive  shock  to  their  equity  in  order  to  restore  their 

financial position.  

Our  considerations  are  motivated  by  –  but  differ  from  –  the  deleveraging  model  for  banks. 

According to Adrian and Shin (2010), banks target a specific leverage ratio (defined as a ratio 

of  assets  over  equity)  and  thus  sell  off  assets  after  a  negative  shock  to  equity  in  order  to 
restore  their  target  leverage.15  The  deleveraging  mechanism  assumes  that  banks  use  the 

12 Das (2017) focuses on purchases of risky assets such as real estate and loan portfolios, bank branches, equity 
investment portfolios and asset-backed securities. 
13 Douglas et al. (2017) has recently pointed out that the de-risking behaviour of UK insurers may be incentivised by 
the use of risk margins under Solvency II, which reduce the solvency position of the firm following a decrease in the 
risk-free rate and encourage the selling of risky assets to reduce the probability of insolvency. 
14 An alternative way for insurers to act on the asset side after a negative shock to their equity is to purchase 
(additional) reinsurance, but this is often a more costly solution.  
15 Adrian and Shin (2010, 2011) show that broker-dealers and commercial banks engage in leverage targeting, 
adjusting their balance sheets according to a fixed leverage ratio. Greenwood et al. (2015) and Eisenbach et al. 

ECB Working Paper Series No 2299 / July 201916 
 
                                                 
proceeds  from  asset  sales  to  repay  debt,  thereby  lowering  their  liabilities.  This  type  of 

adjustment would, however, be rather limited for insurance firms because they are much less 
leveraged than banks, having only limited amounts of debt on their liability side.16 Therefore, 

our  predictions  relate  to  balance  sheet  “de-risking”  rather  than  “deleveraging”.  Recent 

evidence  from  the  US  shows  that  life  insurers  that  were  particularly  affected  by  the  global 

financial crisis de-risked their  portfolio (Kirti,  2017). Rather  than gambling for  resurrection, 

US insurers characterised by lower equity-to-book ratios and dividend growth reduced credit 

and interest rate risks by buying bonds with low yield to maturity and long duration, and by 

entering fixed-interest rate swap positions. 

Since  we  assume  that  assets  of  a  typical  insurance  firm  can  be  modelled  as  a  zero  coupon 

bond (or a mix of bonds in the generalised duration gap model), we use the inverse relation 

between bond prices and interest rates to rephrase our predictions in terms of price changes. 

Furthermore,  considering  the  link  between  equity  shock  and  investment  behaviour,  we 

formulate the predictions as follows:  

Prediction 1b: Insurers buy (risky) bonds when their prices fall due to an increase in the 

risk-free rate of return (and vice-versa). 

Prediction 2b: Insurers sell (risky) bonds when their prices fall due to an increase in risk 

premia (and vice-versa). 

These two predictions imply that insurers’ investment behaviour can be both procyclical and 

countercyclical,  depending  on  the  underlying  driver  of  a  price  change.  If  the  price  change 

stems from  a  change  in  risk premia,  then  insurers are  expected to behave  procyclically.  On 

the other hand, when the price change is due to a change in the risk-free rate, then insurers 

are expected to behave countercyclically. We believe that this new theoretical insight sheds 

light  on  the  mixed  results  in  the  existing  literature,  which  did  not  consider  the  distinction 

between the two types of price shocks. We test these predictions empirically in the following 

sections. 

(2015) have recently used this evidence as the basis for constructing a systemic risk measure of fire sale in the 
banking system. 
16 According to the ECB’s euro area accounts (EAA) data, loans represented around 5% of euro area insurers’ 
liabilities at the end of 2016. 

ECB Working Paper Series No 2299 / July 201917 
 
 
 
                                                                                                                                                             
While  we  focus  only  on  these  two  predictions  in  the  rest  of  the  paper,  our  theoretical 

framework allows us to make two further predictions. In particular, it follows from Equations 

(4)  and  (6)  that  the  sensitivity  of  insurers’  equity  to  changes  in  the  risk-free  rate  increases 

with  the  size  of  the  negative  duration  gap  (Equation  4),  while  that  to  changes  in  the  risk 

premia  rises  with  the  duration  of  assets  (Equation  6).  Hence,  we  could  test  empirically 

whether  insurers’  countercyclical  investment  behaviour  diminishes  with  a  shrinking  duration 

gap and their procyclical behaviour attenuates with decreasing asset duration. Unfortunately, 

the  lack  of  time-series  data  on  insurers’  asset  and  liability  duration  (and  ultimately  on  the 
duration  gap)  does  not  allow  us  to  do  so.17  If  available,  such  data  would  enable  us  to 

significantly expand our research in the spirit of Domanski et al (2017). 

4. Empirical model and data  

4.1 Model set-up 

We  consider  the  following  model  specification  to  explain  the  amount  of  a  security  held  by 

insurance sectors in the individual euro area countries: 

log(ℎ𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑖,𝑗,𝑡) = 𝛼𝑟𝑖,𝑡−1 + 𝛽𝑝𝑖,𝑡−1 + 𝛾𝑖 + 𝑦𝑗,𝑡 + 𝛿𝑍𝑖,𝑗,𝑡 + 𝜖𝑖,𝑗,𝑡  (7) 

where  log(ℎ𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑖,𝑗,𝑡)  denotes  the  natural  logarithm  of  the  nominal  amount  of  security  i 

held by the insurance sector in country j at quarter t and 𝜖𝑖,𝑗,𝑡 is the error term (see Section 

4.2 for more details about the dependent variable).  

Our two explanatory variables of interest are 𝑟𝑖,𝑡−1 and 𝑝𝑖,𝑡−1. They denote, respectively, the 

risk-free  interest  rate  at  maturity  equal  to  security  i’s  residual  maturity  and  the 

corresponding  risk  premium  of  security  i  (see  Section  4.3  for  more  details  about  these  two 

explanatory  variables  of  interest).  In  line  with  our  theoretical  predictions,  we  expect  the 

estimate  of  𝛼  to  be  positive,  indicating  insurers’  countercyclical  behaviour  in  response  to 

17 To our knowledge, the only available data on insurers’ duration gap for all euro area countries can be found in 
EIOPA (2014) and these refer to a single point in time, the end of 2013, while our empirical analysis covers almost 
10 years (see Figure 5).     

ECB Working Paper Series No 2299 / July 201918 
 
 
 
 
 
 
 
                                                 
rises in the risk-free rate of interest. On the other hand, we expect a negative estimate of 𝛽 

in  order  to  confirm  the  prediction  of  procyclical  behaviour  in  response  to  changes  in  risk 

premia. In all model specifications, we lag the two explanatory variables of interest by one-

quarter to account for potential endogeneity due to reverse causality. A problem of reverse 

causality may in fact arise from the fact that insurers are important institutional investors in 

the  (sovereign)  bond  market  and  thus  a  shift  in  their  holdings  of  security  i  at  time  t  may 

affect  its  price/yield  in  the  same  quarter  (see  Section  5  for  a  more  in-depth  discussion 

related to reverse causality).  

Furthermore,  we  include  several  control  variables  in  our  model  to  address  potential 

endogeneity arising from omitted variable bias. These controls include security fixed effects 

(𝛾𝑖),  holder  country-quarter  fixed  effects  (𝑦𝑗,𝑡)  and  further  time-varying  factors  (presented 

formally  by  vector  Zi,j,t)  that  potentially  affect  both  insurers’  holdings  and  the  price  of  a 

security (see Annex B). More specifically, we include an indicator for a rating downgrade, the 

debt-to-GDP  ratio  of  the  issuer  country  and  the  volumes  of  the  ECB’s  Public  Purchase 

Programme (PPP). All control variables and their estimated effects are discussed in detail in 

Section  5.  Finally,  to  correct  for  the  possibility  that  error  terms  are  correlated  across 

individual securities, we cluster standard errors at the individual security level. 

4.2 Dependent variable 

We obtain our dependent variable from the new Securities Holdings Statistics (SHS) collected 

by the Eurosystem. To extend the time period and cover the euro area sovereign debt crisis, 
we  combine  both  data  from  SHS  collected  under  an  ECB  regulation  since  201418  and  those 
collected prior to 2014 under Securities Holdings Experimental Statistics (SHES).19 As a result, 

our data span from the first quarter of 2009 to the last quarter of 2016.  

This data source provides us with granular security-by-security information on holdings of the 

insurance  sector  in  19  euro  area  countries.  These  holdings  are  not  available  at  the  level  of 

18 Regulation (EU) No 1011/2012 of the ECB of 17 October 2012 concerning statistics on holdings of securities 
ECB/2012/24. 
19 These data  were collected on a voluntary and best-efforts basis from 2009 to 2013, i.e. in the period before the 
SHS collection on the basis of an ECB regulation began, and are thus subject to some quality limitations, such as 
lower coverage in some countries. We pay due attention to these limitations including via careful data cleaning and 
robustness checks (see Section 5).  

ECB Working Paper Series No 2299 / July 201919 
 
 
 
                                                 
individual  insurance  firms,  but  rather  as  an  aggregate  for  the  whole  insurance  sector  in  a 

given  euro  area  country.  We  use  nominal  rather  than  market  values  of  holdings  to  exclude 

valuation effects that do not reflect a buy/sell decision.   

In  the  empirical  analysis,  we  focus  on  holdings  of  government  bonds  in  order  to  overcome 

certain  data  limitations.  In  particular,  the  information  on  the  characteristics  of  individual 

securities  such  as  prices  and  credit  ratings,  which  we  take  from  the  Centralised  Securities 

Database  (CSDB),  is  more  complete  for  government  bonds  than  for  other  types  of  securities 

such as corporate bonds and equity. In the same vein, SHS holdings of government bonds tend 

to  be  of  a  relatively  good  quality  and  coverage,  including  for  SHES  data  collected  prior  to 
2014.20 

Even  if  our  empirical  analysis  is  limited  to  government  bonds,  it  is  representative  for  a 

significant  part  of  insurers’  portfolios,  since  government  bonds  account  for  around  30%  of 

insurers’ securities portfolios (Figure 7). The holding amounts are also significant in absolute 

terms:  at  the  end  of  2016,  the  euro  area  insurance  sector  held  around  EUR  1.5  trillion  in 

government bonds (according to the ECB’s EAA). In addition, insurers hold roughly one-third 

of  their  securities  portfolios  in  shares  of  investment  funds,  two-thirds  of  which  are  either 

bond or mixed funds that further increase the exposure of insurers to government bonds. At 

the  same  time,  the  euro  area  insurance  sector  is  also  one  of  the  most  important  investor 

sectors in government bonds, as it directly holds around 15% of debt securities issued by euro 

area  sovereigns  (Figure  6)  (i.e.  excluding  indirect  holdings  through  funds).  This  figure 
increases to more than 40% for government bonds with a maturity of over 10 years.21  

20 In general, we carefully check the granular data used and clean them from outliers as well as making some further 
adjustments to address data quality limitations. For instance, the SHES data prior to 2014 includes an unclassified 
(residual) category within the ICPF sector, which we – after careful time-series and coverage checks – consider as 
belonging to the insurance sector. Moreover, we run robustness checks related to the use of SHES data in Section 5.      
21 There are various reasons why insurers hold such large amounts of government bonds in their portfolios. First of 
all, insurers use long-term government bonds, which ensure a fixed nominal return, to match the duration of their 
long-term nominal liabilities. Furthermore, government bonds can be used as collateral for hedging contracts, such 
as interest rate swaps. As in the case of banking regulation, government bonds also benefit from preferential 
treatment under Solvency II. Specifically, government bonds of countries in the European Economic Area (EEA) 
are exempted from the calculation of solvency capital requirements and from the large exposure regime in the 
standard formula. However, insurance firms using internal models should – at least in principle – account for the 
riskiness of their exposure to government bonds (ESRB, 2015). 

ECB Working Paper Series No 2299 / July 201920 
 
                                                 
Figure 6: Euro area holdings of euro area debt 

Figure  7:  Euro  area  insurers’  securities 

securities, broken down by holder sector 

holdings, broken down by main asset class  

(EUR trillion, end-2016)  

(end-2016) 

Source:  ECB  Euro  Area  Accounts  and  authors’ 

Source: ECB (SHS data). 

calculations. 

4.2.1 Shifts in insurers’ government bond holdings since 2009 

Government bonds also seem to be a good candidate for investigating the nature of changes 

in asset holdings and testing our predictions, when considering the period from 2009 to 2016, 

as we are doing. This is because, despite the preferential regulatory treatment of government 

bond  holdings,  the  euro  area  sovereign  debt  crisis  undermined  the  notion  of  government 

bonds  as  ‘safe’  assets  and  pointed  out  that  exposure  to  sovereigns  may  represent  an 

important element in determining market risk in insurers’ and bank portfolios. Furthermore, 

Bijlsma  and  Vermeulen  (2016),  who  investigate  Dutch  insurers’  portfolio  shifts  within 

different asset classes, detect the largest changes in asset allocation to take place within  a 

portfolio of government bonds.  

Starting  with  descriptive  evidence,  Figure  8  reveals  two  significant  changes  in  euro  area 

insurers’  asset  allocations.  First  of  all,  euro  area  insurers  significantly  increased  holdings  of 

domestic  government  debt  between  2010  and  mid-2012,  while  they  decreased  cross-border 

exposure  to  government  debt issued by  ‘vulnerable’  euro area countries.  At the same time, 

insurers’  cross-border  exposure  to  government  debt  issued  by  ‘non-vulnerable’  euro  area 

countries remained broadly unchanged. This suggests – in line with the findings in Bijlsma and 

Vermeulen  (2016)  –  that  euro  area  insurers’  asset  allocations  were  strongly  affected  by  the 

012345678910Government bondsBank bondsCorporate bonds ofnon-financialsCorporate bonds ofnon-bank financialsMillionsInsurance corporationsPension fundsBanksInvestment fundsOther euro area investorsNon-euro area investorsinvestment funds shares33%government bonds30%corporate bonds33%listed shares4%ECB Working Paper Series No 2299 / July 201921 
 
 
 
 
 
European sovereign debt crisis, during which the credit quality of bonds issued by ‘vulnerable’ 

euro area countries (as measured by credit ratings) significantly deteriorated. Hence, in line 

with our predictions, credit risk (being a significant part of risk premia) is likely to be one of 

the main drivers of insurers’ procyclical investment behaviour.  

Even  if  euro  area  insurers  reshuffled  their  portfolios  of  government  bonds,  this  was  not 

accompanied  by  substantial  portfolio  shifts  between  different  types  of  assets.  In  particular, 

the  share  of  government  bonds  in  insurers’  portfolios  remained  relatively  stable  throughout 

the full time span (Figure 9). Overall, Figures 8 and 9 indicate that, during the sovereign debt 

crisis,  euro  area  insurers  sold  government  bonds  of  (other)  ‘vulnerable’  euro  area  countries 

and bought domestic government bonds instead (even if this might have been intermediated 
by bank dealers).22  

Figure  8:  Euro  area  insurers’  holdings  of 

Figure  9:  Euro  area 

insurers’  securities 

government  bonds,  broken  down  by  type  of 

holdings, broken down by type of security. 

issuer. 

Note:  ‘Domestic  exposure’  refers  to  all  holdings 

Source: ECB (SHS data) and authors’ calculations. 

where  the  issuer  and  holder  are  from  the  same 

country.  Vulnerable  countries  include  CY,  ES,  GR, 

IT, PT, SI and non-vulnerable countries include the 

remaining euro area countries. 

Source: ECB (SHS data) and authors’ calculations. 

22 Our data are not at transaction level and thus do not allow us to directly link the buyer and seller of a government 
bond. 

0%10%20%30%40%50%60%70%80%90%100%20092010201120122013201420152016Domestic exposureCross-border exposure to ‘non-vulnerable’ countriesCross-border exposure to ‘vulnerable’ countries0%10%20%30%40%50%60%70%80%90%100%20092010201120122013201420152016Investment funds sharesGovernment bondsCorporate bondsListed sharesECB Working Paper Series No 2299 / July 201922 
 
 
 
 
 
 
                                                 
4.3. Explanatory variables of interest 

As a proxy for the risk-free rate 𝑟, we use the historical risk-free interest rate term structure 

for euro currency, published every month by EIOPA. Under Solvency II, this yield curve is used 

for the calculation of the value of technical provisions for insurance obligations in euro, which 
represent the bulk of technical provisions of euro area insurers.23 For each month, the term 

structure  includes  the  risk-free  interest  rate  at  different  maturities,  from  one  to  60  years, 

with  time  intervals  of  one  year.  We  estimate  a  more  granular  term  structure  by  using  the 

Nelson-Siegel-Svensson  method  (see  Nelson-Siegel,  1985  and  Svensson,  1994)  in  such  a  way 

that the maturity brackets shrink from one year to 3.65 days. Given that the securities in our 

sample have different maturities, we assign to each security  the value of the risk-free yield 

curve with the closest maturity. As a result, all securities with the same residual maturity m 

in  our  sample  are  assigned  the  same  risk-free  rate,  i.e.  𝑟𝑖,𝑡 =  𝑟𝑚,𝑡  for  all  securities  i  with 

maturity m.   

Regarding the calculation of risk premia p, we first obtain the yield-to-maturity 𝑌𝑇𝑀𝑖,𝑚,𝑡 from 

the  ECB’s  Centralised  Securities  Database  (CSDB).  The  yield-to-maturity  is  assigned  to  each 

security  i  with  residual  maturity  m  at  time  t.  We  then  construct  risk  premia  p  as  the 

difference between the yield-to-maturity of such a security at time t and the risk-free rate r 

with the same maturity m:  

 𝑝𝑖,𝑡 = 𝑌𝑇𝑀𝑖,𝑚,𝑡 − 𝑟𝑚,𝑡 

(8) 

Figure 10 shows the risk-free rate curve at the end of 2016 and a hypothetical yield curve for 

sovereign bonds. For a bond with maturity m equal to 3 years, for example, the risk premium 

is  calculated  as  the  difference  between  the  yield  of  the  bond  and  the  risk-free  rate  at  3 

years. Therefore, the risk premia of sovereign bonds issued by the same country may differ in 

a given quarter, depending on the residual maturity of the securities. Since the risk-free rate 

varies only with maturity but not across countries, our risk premia captures various types of 

23 EIOPA risk-free yield curves are based on liquid swap and governments bond rates, and then adjusted to include 
the counterparty default risk. Further information is available at https://eiopa.europa.eu/regulation-
supervision/insurance/solvency-ii-technical-information/risk-free-interest-rate-term-structures. To test the 
robustness of our results to the choice of the risk-free rate proxy, we also run robustness checks with alternative 
measures (see Section 5). 

ECB Working Paper Series No 2299 / July 201923  
 
 
 
 
                                                 
risks faced by the investor. Some risks such as credit and liquidity risks are specific to each 

individual  security/issuer,  while  other  types  of  risks  such  as  those  stemming  from 

(expectations  about)  inflation  and  economic  growth  differentials  reflect  differences  in 

macroeconomic fundamentals across countries.   

Figure  10:  EIOPA  risk-free  rate  term  structure  (end 

of  2016)  and  a  hypothetical  sovereign  bond  yield 

curve  to  illustrate  the  construction  of  the  risk 

premia variable 

Source: EIOPA and authors’ calculations. 

4.3.1 Evolution of the risk-free rate and risk premia since 2009 

Figure  11  shows  the  evolution  of  the  average  risk-free  rate  and  risk  premia  over  the  time 

period we cover  (weighted  by  insurers’  government  bond  holdings in  our sample).  Following 

the  financial  crisis  in  2007-2008,  the  risk-free  yield  has  declined  and,  recently,  reached 

historically low levels. The evolution of the sovereign risk premia was very different. Prior to 

the financial crisis, the levels were very low, as investors considered many sovereign bonds as 

risk-free investments. In November 2009, the Greek government announced a revised budget 

deficit,  which  significantly  exceeded  market  expectations.  This event led to a reassessment 

of  the  sovereign  risk  by  investors  and  triggered  the  start  of  the  European  sovereign  debt 

crisis.  During  the  crisis,  yield spreads across countries were mostly related to differences in 

their  fiscal  fundamentals,  competitiveness  and  the  need  for  foreign  financing  (De  Santis, 

2014). Specifically, the risk premia of euro area countries with weak fundamentals rose until 

-1012345036912151821242730riskpremium at10Yriskpremium at30YActual yield curveEIOPA risk-free rate term structure risk-free rateat10Yrisk-free rateat30YECB Working Paper Series No 2299 / July 201924 
 
 
 
 
mid-2012,  when  the  ECB  announced  the  Outright  Monetary  Transactions  programme  (OMT), 

which  generated  a  decline  in  all  euro  area  sovereign  risk  premia.  Supported  by  further 

monetary  policy  accommodation  and  better  macroeconomic  prospects,  the  average  risk 

premia in our sample decreased to pre-crisis levels at the end of 2016.  

We  plot  the  first  differences  of  the  average  risk-free  rate  and  risk  premia  in  Figure  12  to 

check whether (and if so, in which periods) the two display a strong correlation. The variables 

show a slightly negative correlation, mostly due to the sovereign debt crisis period, in which 

the  risk-free  rate  kept  declining,  while  the  risk  premia  were  sharply  increasing.  Overall, 

however, our sample covers all four combinations of changes: increase and decrease in both 

the risk premia and risk-free rate as well as their movements in opposite directions.   

Figure  11:  Average  risk-free  rate  and  risk 

Figure  12:  Scatter  plot  of 

the 

first 

premia  over  time  (weighted  by  holdings  in 

differences of average risk-free rate and risk 

our sample) 

premia (weighted by insurers’ holdings) 

Source: ECB SHS data and authors’ calculations. 

Source: ECB SHS data and authors’ calculations. 

5. EMPIRICAL RESULTS 

5.1 Baseline model  

To confirm our theoretical predictions empirically, we start estimating the effects of a drop 

in price due to a change in the risk-free rate and due to a change in risk premia on insurers’ 

holdings. In line with our predictions, we find opposite effects for the two factors (see Table 

1,  column 1).  On the one hand,  the positive coefficient  for  the risk-free rate  indicates  that 

0.511.52risk premia01234risk-free rate2009q12011q12013q12015q12017q1mydaterisk-free raterisk premia2009q22009q32009q42010q12010q22010q32010q42011q12011q22011q32011q42012q12012q22012q32012q42013q12013q22013q32013q42014q12014q22014q32014q42015q12015q22015q32015q42016q12016q22016q32016q4-.50.51first difference of risk premia-1-.50.5first difference of risk-free rateECB Working Paper Series No 2299 / July 201925 
 
 
 
 
 
 
 
insurers  buy  (sell)  securities  whose  prices  have  fallen  (risen)  due  to  an  increase  in  the  risk-

free  rate,  showing  a  countercyclical  behaviour  (Prediction  2a).  On  the  other  hand,  the 

negative  coefficient  for  risk  premia  suggests  that  insurers  sell  (buy)  securities  whose  prices 

have  fallen  (risen)  due  to  an  increase  in  risk  premia,  indicating  a  procyclical  behaviour 

(Prediction 2b). 

Our  initial  specification  includes  security  fixed  effects  that  control  for  any  time-invariant 

characteristics  of  a  security  such  as  its  type,  original  maturity,  coupon  payments  frequency 

and  face  value.  These  fixed  effects  also  capture  any  time-invariant  issuer  characteristics, 

notably  the  country  of  the  issuer.  Furthermore,  the  specification  includes  holder  country-

quarter  fixed  effects  to  account  for  any  time-varying  factors  in  individual  holder  countries 

and  global  trends.  These  fixed  effects  inter  alia  capture  both  cyclical  and  long-term 

structural changes in insurers’ holdings such as a changing appetite to hold government bonds 

in view of (the price of) other products on the market and the increasing size of the insurance 

sector  in  a  given  holder  country.  They  also  control  for  changes  in  the  macroeconomic 

environment and financial conditions of the holder country. Moreover, they capture any time-

varying global factors such as market volatility and the overall level of yields (including any 

parallel shifts in the risk-free yield curve).  

While  the  results  in  column  1  provide  the  first  indicative  confirmation  of  our  predictions,  a 

standard  problem  of  empirical  studies  that  estimate  the  impact  of  price  changes  on 

investment  behaviour  is  the  potential  endogeneity  of  price  changes,  which  may  bias  the 

estimates. The endogeneity problem can have two sources: omitted variable bias and reverse 

causality, which we discuss in turn below.  

5.1.1  Addressing endogeneity due to omitted variable bias  

The  omission  of  variables  that  could  simultaneously  determine  investment  behaviour  and 

price movements can be a potential source of endogeneity and thus could bias our estimated 

coefficients  of  risk-free  rates  and  risk  premia.  The  use  of  two  types  of  fixed  effects  in  our 

model  already  partially  addresses  this  problem  as  they  capture  any  unobserved  security-

specific and holder country-quarter specific factors. However, these fixed effects do not fully 

capture all time-varying co-determinants of insurers’ investment behaviour and price/interest 

ECB Working Paper Series No 2299 / July 201926 
 
 
 
rate  movements.  Therefore,  we  turn  to  the  inclusion  of  a  number  of  security-specific  or 

issuer-country  specific  time-varying  variables.  All  these  explanatory  variables  are  lagged  by 

one-quarter to specifically address the possibility that they are not only the determinants of 

insurers’  asset  holdings  but  also  determinants  of  the  lagged  explanatory  variables  of 
interest.24  

In column 2, we focus on the role of time-varying creditworthiness of an issuer as we predict 

that a deteriorating creditworthiness could trigger both asset sales and a price drop. We use 

two  different  measures.  First,  we  include  a  dummy  that  indicates  a  (significant)  rating 

downgrade.  Given  that  sovereigns  are  rated  by  several  credit  rating  agencies,  we  first  map 

the  available  ratings  into  the  four  credit  quality  steps  defined  in  the  Eurosystem  credit 
assessment  framework  (ECAI  steps).25  The  dummy  ECAI  downgrade  then  equals  one  if  the 

bond’s  credit  quality  changed  from  a  lower  to  a  higher  ECAI  step  between  two  consecutive 

quarters. The negative and significant coefficient of 0.17 suggests that insurers decrease their 

bond  holdings  by  around  17%  after  a  (significant)  rating  downgrade.  While  a  deteriorating 

credit  quality  per  se  can  be  a  reason  for  a  bond’s  sell  off,  insurance  firms  may  also  face 

regulatory  limits  linked  to  ratings  and/or  use  ratings  to  define  their  internal  investment 
strategies,  which  may  contribute  to  the  relatively  large  estimate  of  this  effect.26  As  a 

complementary measure of creditworthiness, we use the issuer country’s debt-to-GDP ratio, 

which  is  found  to  be  insignificant  in  this  specification.  In  line  with  our  expectations,  the 

inclusion  of  these  two  measures  of  creditworthiness  lowers  somewhat  the  size  of  the 

estimated coefficient of risk premia (from 0.30 to 0.20) but the estimate still remains highly 

significant.      

In column 3, we account for the ECB’s non-standard monetary policy measures by controlling 

for the volumes of Public Sector Programme Purchases (PSPP). Under PSPP, the ECB purchases 

bonds issued by euro area sovereigns with the objective of maintaining price stability in the 

euro  area.  Although  PSPP  only  started  in  March  2015  and  thus  influences  only  the  last  two 

24 Factors that are determinants of the dependent variable (holdings) but that do not affect the explanatory variables 
of interest (risk-free rate and risk premia) could have an explanatory power in our model. Their omission would, 
however, not lead to biased estimates of the coefficients for our explanatory variables of interest.  
25 For more details regarding the construction of the four ECAI steps, see Annex B. 
26 Recently, Becker and Ivashina (2015) observed a search for yield behaviour in the corporate bond holdings of 
insurance companies. As risky assets are assigned a capital requirement according to their rating, insurance firms 
prefer to hold higher rated bonds. However, within each rating class, they tend to buy higher yield assets to achieve 
higher returns. Therefore, insurance companies can be vulnerable to rating migrations.   

ECB Working Paper Series No 2299 / July 201927 
 
                                                 
years of our data, it is a potentially important confounding factor in our model. On the one 

hand,  PSPP  can  influence  the  level  of  insurers’  holdings,  as  some  of  the  securities  may  be 
directly purchased from them.27 On the other hand, the aim of PSPP is to ease monetary and 

financial conditions by lowering the level of interest rates along the yield curve. We use the 

publicly available aggregates of PSPP volumes on the level of individual issuer countries of the 

euro area to estimate this effect. Since PSPP is limited to the universe of securities eligible 

for Eurosystem operations, we associate (the log of) PSPP volumes only with insurers’ holdings 

of  securities  from  this  universe,  while  we  assign  zero  PSPP  volumes  to  insurers’  holdings  of 

remaining securities. By the same token, we assign zero PSPP volumes to all insurers’ holdings 
prior  to  the  start  of  the  purchase  programme.28  The  estimated  coefficient  is  found  to  be 
insignificant and the estimates of our explanatory variables of interest barely change.29 

While the size of the estimated coefficient of risk premia decreases from 0.30 to 0.18 by the 

inclusion  of  these  control  variables,  the  estimate  remains  highly  significant.  Moreover,  the 

estimated  coefficient  of  the  risk-free  rate  remains  very  stable  and  significant  throughout 

these alternative specifications. Hence, the results provide a further empirical support of our 

model.     

27 As we are primarily interested in including factors that could lead to endogeneity, we lag the volumes of PSPP 
purchases by one quarter to explicitly control for the possibility that they influence our lagged explanatory variables 
of interest. However, the PSPP volumes are highly auto-correlated as the programme targets a fixed amount of 
monthly purchases (EUR 60 billion in the first year and EUR 80 billion in the second year of the programme) and, 
therefore, they also affect the level of insurers’ holdings in the subsequent quarter. As an alternative, we also 
experimented with the inclusion of contemporary PSPP volumes in the model but our estimates remained broadly 
unchanged.  
28 PSPP is not the first ECB purchasing programme of securities issued by euro area sovereigns. From May 2010 to 
September 2012, the Eurosystem also enacted the Securities Market Purchase (SMP) programme. However, the 
volumes of the purchases were much smaller than those under PSPP and thus their effect is expected to be rather 
limited. Specifically, the SMP holdings amount to EUR 98 443 million and the last operation was allotted on 10 
June 2014. The PSPP holdings amounted to EUR 1 568 013 million in June 2017 and include nominal and inflation-
linked central government bonds, and bonds issued by recognised agencies, regional and local governments, 
international organizations and multilateral development banks located in the euro area. Moreover, we do not have 
quarterly information on the amount of the SMP purchases by country to properly control for the potential 
confounding effect.   
29 An alternative approach to controlling for PSPP purchases is to shorten the sample by excluding the quarters since 
the start of the purchase programme. Also in this case, the estimated coefficients of our explanatory variables of 
interest remain by and large unchanged (see column 6 in Table 6). 

ECB Working Paper Series No 2299 / July 201928 
 
 
 
 
                                                 
5.1.2  Do insurers treat domestic government bonds differently?  

As  outlined  in  Section  4,  one  of  the  main  developments  observed  during  the  euro  area 

sovereign  debt  crisis  was  an  increase  in  insurers’  holdings  of  domestic  securities.  In  fact, 

insurers’  holdings  of  domestic  government  bonds  are  substantial,  representing  around  two-

thirds of the holding amounts at the end of 2016 (i.e. EUR 1 trillion out of EUR 1.5 trillion). 

Therefore, in columns 4 and 5 of Table 1, we study the effects of the risk-free rate and risk 

premia on non-domestic and domestic holdings separately and compare them to our previous 

estimates.  

The results for non-domestic exposures (column 4) further confirm our theoretical predictions 

of insurers’ procyclicality with respect to risk premia, and countercyclicality with respect to 

the  risk-free  rate.  Moreover,  the  coefficient  of  debt-to-GDP  ratio  becomes  negative  and 

significant.  As  higher  debt  levels  may  signal  the  unsustainability  of  public  finances  and  a 
fundamental credit risk, this is a plausible finding.30   

The  results  for  domestic  holdings,  however,  show  the  opposite  picture.  We  estimate  that  a 

rise  in  the  risk-free  rate  is  associated  with  a  decrease  in  insurers’  holdings  of  its  own 

sovereign bonds (procyclical behaviour) and that insurers tend to respond countercyclically to 

changes  in  the  risk  premia  of  their  own  sovereign.  In  particular,  if  the  risk  premia  of  a 

domestic  sovereign  rises  (e.g.  due  to  the  deterioration  in  credit  quality),  we  estimate  that 

the insurance sector in that country will buy its bonds, which is the opposite result compared 

with  non-domestic  holdings.  By  further  splitting  the  sample  into  ‘vulnerable’  and  ‘non-

vulnerable’  countries,  we  find  that  this  type  of  behaviour  is  mainly  driven  by  insurers’ 
behaviour in ‘vulnerable’ euro area countries (CY, ES, GR, IE, IT, PT, SI).31  

These  empirical  findings  indicate  that  there  are  different  incentives  for  insurers  to  hold 

domestic  bonds.  In  the  financial  literature,  it  is  well-known  that  the  so-called  home  bias  is 

present  across  different  investment  classes  (not  only  in  the  sovereign  bond  market);  it  is 

widespread  in  both  developed  and  emerging  markets  (French  and  Poterba,  1991)  and  is  not 

necessarily  an  inefficiency  to  correct.  There  are  manifold  reasons  for  an  investor  to  prefer 

30 The coefficient of PSPP volumes also becomes significant. The positive estimates suggest that PSPP purchases 
tend to be associated with larger volumes of insurers’ government bond holdings. 
31 Results are available upon request. 

ECB Working Paper Series No 2299 / July 201929 
 
 
 
                                                 
domestic  securities,  particularly  during  a  financial  crisis  (Gennaioli  et  al.  2014).  Among 

others,  the  reasons  include  political  pressure  and  moral  suasion  (Erce  2015,  Acharya  and 

Steffen  2015),  redenomination  and  exchange  rate  risks  (Fabozzi  et  al.  2015),  transaction 

costs,  information  advantage and geographical  hedging  of  assets and liabilities (Coeurdacier 

and Rey 2013, Choi et al., 2017).  

The  results  for  the  risk  premia  based  on  domestic  exposures  seemingly  contradict  our 

theoretical predictions. However, this is not necessarily the case, provided that insurers (and 

regulators) view – for some of the reasons listed above – domestic sovereign bonds as if they 

were safe assets. For safe assets, the theoretical predictions of our model are the opposite to 

those for risky assets. 

Going  forward,  we  focus  on  the  sample  of  non-domestic  exposures  and  choose  the  (richest) 

specification in column 4 as our baseline regression.    

5.1.3  Addressing endogeneity due to reverse causality  

Reverse  causality  may  bias  our  estimates  if  the  risk-free  rate  and  risk  premia  (explanatory 

variables  of  interest)  depended  on  insurers’  holdings  (dependent  variable).  In  particular, 

insurers’  purchases  of  bonds  could  increase  the  overall  demand  for  sovereign  bonds  in  the 

market, which could suppress yields. For instance, if a large insurers’ sell-off of a particular 

bond increased its risk premia, the coefficients obtained by OLS estimations in Table 1 would 

be  biased  downwards (taking into  account  their  negative  sign).  To avoid such contemporary 

feedback,  we  lag  our  explanatory  variables  of  interest  by  one-quarter  in  all  model 

specifications.  Nevertheless,  as  both  insurers’  holdings  and  yields  are  auto-correlated,  this 

may  not  fully  address  our  concern.  Therefore,  we  aim  to  tackle  this  problem  using 

instrumental  variables  and  report  the  results  in  Table  2.  For  a  better  comparability  of  the 

coefficients,  we  include  in  column  1  of  Table  2  the  results  of  our  baseline  regression 

presented in column 4 of Table 1.   

In  column  2  of  Table  2,  we  address  the  reverse  causality  of  the  (lagged)  risk-free  rate  of 

return.  We  instrument  the  risk-free  interest  rate  (at  all  maturities)  with  the  US  risk-free 

interest rate (at the corresponding maturity). The results of the first stage regression confirm 

ECB Working Paper Series No 2299 / July 201930 
 
 
 
 
that  this  variable  is  highly  correlated  with  the  risk-free  interest  rate,  while  being  (likely) 

exogenous to euro area insurers’ holdings of government bonds, because it is a result of the 

policy decisions of a foreign country, combined with the underlying economic conditions and 

expectations  in  that  country.  While  the  policy  decision  takes  into  account  various  factors, 

holdings of euro area insurers would only be a fraction of the overall information feeding into 

such a decision-making process. Therefore, the reverse causality problem is minimised by the 

use of this instrument. The fact that the coefficient of the risk-free rate remains significant 

(and even increases) confirms the robustness of our results. 

In column 3, we turn to the potential reverse causality of the (lagged) risk premia.  We first 

recall  that  we  construct  risk  premia  as  a  difference  between  the  yield-to-maturity  and  the 

risk-free  rate  at  the  same  maturity.  While  the  long-term  risk-free  interest  rate  should  in 

principle reflect inflation and growth expectations (Taylor, 1993), the fact that we use only a 

single risk-free rate for all bonds in our sample means that the (expectations about) country 

inflation and growth differentials are  captured by the risk premia (in addition to credit and 

liquidity premia). Therefore, we instrument the risk premia by inflation rates in the country 

of  issuer.  The  first  stage  results  confirm  that  inflation  is  a  significant  determinant  of  risk 

premia. On the other hand, there is no particular reason why insurers’ holdings of government 

bonds  issued  by  a  (different)  country  would  significantly  influence  inflation  in  that  country. 

While overall capital flows may do so, especially for small open economies, foreign insurers’ 

holdings of government bonds are only a small part of the overall capital flows and other type 

of flows such as foreign direct investment may play a much larger role. This notwithstanding, 

to further limit the possibility of such a feedback loop, we lag the instrument by two-quarters 
(as  compared  to  the  dependent  variable).32  The  estimated  coefficient  for  the  risk  premia 

remains negative and significant, which further confirms our baseline results. 

Finally,  we  use  the  two  instruments  jointly  in  the  last  column  of  Table  2  to  instrument  for 

both the risk premia and the risk-free rate. The results also remain robust in this case.  

Overall,  the  results  of  the  IV  estimations  provide  further  support  for  the  significant  and 

positive (negative) effect of the risk-free rate (risk premia) on insurers’ holdings. The results 

are also robust to the selection of the IV method used. In particular, the estimates obtained 

32 The results are broadly unchanged, when the first (instead of a second lag) is used. 

ECB Working Paper Series No 2299 / July 201931 
 
 
                                                 
by the two-stage least square (2SLS), which are reported in Table 2, are very similar to those 

obtained by the General Method of Moments (GMM) estimator.  

5.1.4  Model implications for insurers’ investment behaviour  

When considering the estimates obtained in our baseline specification (column 4 of Table 1), 

the estimated effects of the risk-free rate and risk premia are not only statistically but also 

economically  important.  For  instance,  if  the  risk-free  rate  increased  by  100bp  (i.e.  1 

percentage point), we estimate that insurers would increase their nominal holdings by 11% in 

the  following  quarter  ceteris  paribus.  Given  that  the  non-domestic  holdings  of  government 

bonds by euro area insurers at the end of 2016 totalled around EUR 0.5 trillion, an increase of 

11% would translate into purchases of around EUR 55 billion. On the other hand, we estimate 

that if the risk premia increased by the same amount, insurers would decrease their nominal 

holdings by 2.2% ceteris paribus, i.e. by around EUR 11 billion.  

Although the size of the estimated effect is smaller for risk premia than for the risk-free rate 

of return, the overall effect of an interest rate change (or price change) on insurers’ holdings 

depends on the relative size of the change in the risk-free rate as compared with the change 

in risk  premia.  Particularly  in  crisis periods,  the changes in risk  premia  can exceed those in 

the  risk-free rate.  If  the excess is  sufficiently large,  our model implies  that the risk  premia 

become the main driver of the overall interest rate effect on holdings. As a result, insurers’ 

investment  behaviour  in  our  model  turns  procyclical  under  such  a  scenario.  On  the  other 

hand,  in  calm  periods,  when  the  changes  in  risk  premia  are  relatively  muted,  our  model 

predicts countercyclical behaviour because the risk-free rate effect dominates.  

Figures  13  and  14  show  those  periods  in  which  our  model  predicts  pro-  and  countercyclical 

behaviour when using changes in the average risk-free rate and risk premia in our sample as 
examples.33  The  results  suggests  that 

investment  behaviour  tends  to  be 

insurers’ 

countercyclical during calm periods, while turning procyclical in crisis periods with high risk-

premia volatility such as during the euro area sovereign debt crisis. The results in Figures 13 

and 14, however, only serve an illustrative purpose to the extent that individual bonds have 

33 In more detail, when using the estimated coefficients in column 5 of Table 1, insurers’ investment behaviour is 
estimated to be countercyclical, if sign(-0.022*change in risk premia + 0.11*change in risk-free rate) = sign(change 
in risk premia + change in risk-free rate). 

ECB Working Paper Series No 2299 / July 201932    
 
 
 
                                                 
individual risk premia. For instance, in the middle of the euro area sovereign debt crises, the 

risk  premia of  a  ten-year  Italian government  bond increased  by  around 145bps between the 

third and fourth quarters of 2011, while that of a ten-year German government bond declined 

by  around  9bps.  Hence,  although  the  insurers’  investment  behaviour  implied  by  our  model 

was  procyclical  with  respect  to  Italian  government  bonds  in  that  period,  it  remained 

countercyclical with respect to German government bonds.    

Figure  13:  Insurers’  pro-/countercyclicality  over 
time  (as  estimated  by  our  model  for  the  average 
risk-free rate and risk premia) 

Figure  14:  Insurers’  pro-/countercyclicality  in  a 
scatter  plot  (as  estimated  by  our  model  for  the 
average risk-free rate and risk premia) 

Source: ECB SHS data and authors’ calculations. 
Notes:  The  averages  refer  to  averages  of  the  risk-free  rate  and  risk  premia  in  a  given  period  weighted  by  the 
holdings in our sample. 

5.2 Robustness checks  

One drawback of our empirical results is that they are largely based on the period prior to the 

start of Solvency II, when euro area insurers did not necessarily operate under a market-based 

regulatory regime. The regulatory regime, however, plays an important role in our theoretical 

considerations,  which  are  derived  assuming  a  market-consistent  valuation  of  assets  and 

liabilities.  The  following  two  robustness  checks  aim  at  addressing  this  shortcoming.  In 

addition,  we  also  test  the  robustness  of  the  results  with  respect  to  different  empirical 

specifications and (sub-)samples. 

0.511.52risk premia012342009q12011q12013q12015q12017q1mydatePro-cyclical - sellingPro-cyclical - buyingrisk-free raterisk premia2010q22011q42012q22012q42013q32015q42009q22009q32009q42010q12010q32010q42011q12011q22011q32012q12012q32013q12013q22013q42014q12014q22014q32014q42015q12015q22015q32016q12016q22016q32016q4-.50.51first difference of risk premia-1-.50.5first difference of risk-free ratePro-cyclical - sellingPro-cyclical - buyingCounter-cyclicalECB Working Paper Series No 2299 / July 201933 
 
 
 
 
 
 
 
 
A. Do our empirical results hinge on the regulatory regime in place? 

The Solvency II regime entered into force in January 2016. The basic principle of this regime 

requires  all  euro  area  insurers  to  provide  a  market-consistent  view  on  their  balance  sheet, 

which is the starting point for our theoretical considerations. Therefore, in columns 2 and 3 of 

Table  3  we  test  whether  our  empirical  results  substantially  change  when  we  split  the  time 

span into  periods (i)  prior  to  2016 (column 2)  and  (ii)  during 2016 (column 3).  Interestingly, 
the  results  hold  for  the  period  prior  to  the  start  of  the  Solvency  II  regime.  34  The  second 

sample  period  is,  however,  very  short  (effectively  only  three  quarters  as  we  use  lagged 

explanatory  variables)  and  the  estimated  coefficient  of  risk  premia  becomes  insignificant. 

Overall, we believe that it is too early for our study to empirically assess insurers’ investment 

behaviour under the Solvency II regime given the short time period since its introduction. 

Focusing  on  the  period  prior  to  the  Solvency  II  introduction,  our  theoretical  model  suggests 

that  insurers’  investment  behaviour  was  sensitive  to  price  changes  mainly  in  cases  where 

insurers operated under a market-based regime, as also suggested by Lepore et al. (2018). To 

test  this,  we  aim  at  singling  out  these  countries,  for  which  the  introduction  of  Solvency  II 

represented a notable change compared to the non-risk-sensitive requirements in Solvency I. 

Although  our  approach  is  far  from  perfect,  it  is  based  on  the  use  of  transitional  measures 

under  Solvency  II  as  no  detailed  and  comprehensive  information  on  the  nature  of  the 

regulatory regimes in place in individual euro area countries prior to 2016 is available to us. 

Since the transitional measures were designed to smooth the transition between the Solvency 
I  and  Solvency  II  regimes,35  we  assume  that  insurers  in  countries  that  had  a  more  market-

based  regulatory  regime  prior  to  Solvency  II  do  not  (need  to)  use  transitional  measures.  By 

contrast,  we  assume  that  insurers  in  countries  with  less  market-based  regimes  prior  to 

Solvency II are more likely to apply these measures.  

34 It is not necessarily the case that the regimes in place prior to 2016 were fully non-risk sensitive. First of all, euro 
area insurers were anticipating the introduction of Solvency II for several years before 2016 (see Section 2). 
Moreover, the national laws in some countries included more risk-sensitive requirements. 
35 The transitional measures under Solvency II are twofold and allow firms to use Solvency I valuation for some of 
their technical provisions (transitional measure on technical provisions) and apply a transitional adjustment to the 
risk-free rate (transitional measure on the risk-free rate) for up to 16 years after Solvency II entered into force 
(EIOPA 2016b). 

ECB Working Paper Series No 2299 / July 201934  
 
 
                                                 
In  line  with  our  expectations,  we  find  that  insurers’  investment  behaviour  is  sensitive  to 

changes  in  the  risk-free  rate  and  risk  premia  when  insurers  operate  under  a  more  market-

based  regime  since  the  corresponding  coefficients  are  estimated  to  be  highly  significant 

(Column 5 of Table 3). By contrast, the size of both coefficients of interest decrease and the 

coefficient  of  the  risk-free  rate  becomes  insignificant  for  countries  with  less  market-based 

regimes (column 4 of Table 3).  

B. What is the role of different risk-free measures? 

The  choice  of  our  proxy  for  the  risk-free  rate  in  the  baseline  model  is  motivated  by  the 

current  Solvency  II  framework,  which  requires  all  euro  area  insurers  to  discount  their 

technical provisions in euro using a single yield  curve for euro currency published by EIOPA. 

However,  this  was  not  necessarily  the  case  under  Solvency  I.  Therefore,  we  test  the 

robustness  of  our  results  to  the  use  of  different  measures  of  the  risk-free  rate.  The  risk 

premia also change with the measures of the risk-free rates, as the former is calculated from 

the latter.  

We  report  the  results  in  Table  4.  As  a  starting  point,  we  use  the  baseline  estimates  from 

column 4 of Table 1. In Columns 2 and 3 of Table 4, we then proxy the risk-free rate with the 

yield  curve  of  10-year  government  bonds  issued  by  Germany  and  the  overnight  index  swap 

(OIS)  yield  curve.  In  both  specifications,  our  baseline  estimates  are  highly  robust  to  the 

different choices of risk-free rate proxy.  

C. Are our results driven by the empirical specification?  

To  assess  the  robustness  of  our  results  to  changes  in  the  empirical  specification,  we  first 

include  different  types  of  fixed  effects.  Column  1  of  Table  5  presents  the  baseline  results 

obtained  with  security-specific  fixed  effects  and  with  holder  country-quarter  fixed  effects. 

Instead  of  the  latter  type  of  fixed  effects,  column  2  includes  holder  country  fixed  effects 

(which  are  less  granular  than  holder  country-quarter  fixed  effects)  but  also  quarterly  fixed 

effects. Our predictions are also verified in this empirical specification. 

ECB Working Paper Series No 2299 / July 201935 
  
 
 
 
 
Furthermore, we estimate the overall effect of an interest rate shock on insurers’ holdings by 

means of the yield-to-maturity variable. By doing this, we follow the spirit of previous studies 

(e.g.  Timmer,  2018;  Bijlsma  and  Vermeulen,  2016),  which  do  not  distinguish  between  the 

different drivers of an interest rate/price change. This overall effect is found to be negative 

and  significant  (see  Table  5,  column  3),  which  suggests  that  euro  area  insurers  sell  bonds, 

whose  yield-to-maturity  rises,  i.e.  whose  price  falls  (and  vice-versa).  Hence,  if  we  did  not 

distinguish between the effect of the risk-free rate and risk premia, we could conclude that 

the euro area insurance sector (always) reacts procyclically to a bond price change.  

It  could  also  be  that  our  results  are  only  driven  by  positive  or  negative  changes  in  the 

explanatory variables of interest.  To  test  whether  this is the case,  we include in our model 

explanatory variables, which separately capture positive and negative changes in the risk-free 

rate  and  risk  premia.  The  estimated  coefficients  suggest  that  our  results  hold  for  both 

positive and negative changes in the variables of interest (see Table 5, column 4), which seem 

to have a symmetric effect on insurers’ holdings.   

We  also  test  our  results  with  different  specifications  of  the  dependent  variable.  First,  we 

difference the  dependent variable  and  present the results with  both  holder  country-quarter 

and  holder  country-year  effects  (columns  5  and  6).  Second,  we  also  difference  all  the 

explanatory variables and run regressions in changes, again including holder country-quarter 
and  holder  country-year  effects  (columns  7  and  8).36  The  coefficient  of  the  risk-free  rate  is 

found  to  be  insignificant  in  both  specifications  with  holder  country-quarter  fixed  effects 

(columns 5 and 7) but becomes significant with holder country-year fixed effects (columns 6 

and  7).  The  insignificant  result  for  the  risk-free  rate  coefficient  obtained  with  the  very 

granular  holder  country-quarter  fixed effects is not  surprising given  that  these fixed effects 

fully account for any parallel shifts in the risk-free rate curve. At the same time, the effect of 

risk premia remains significant in all these regressions, which further supports the robustness 

of our finding of insurers’ procyclical behaviour.  

Finally, in the last column of Table 5, we construct a buy/sell indicator equal to 1 if the first 

difference of holdings is greater than 0 (insurers buy a security), to -1 if the first difference is 

36 The security fixed effects are dropped in these two specifications as they do not change over time and are thus 
differenced out. 

ECB Working Paper Series No 2299 / July 201936 
 
 
                                                 
negative (insurers sell a security), and 0 otherwise. We run ordered logit regressions with this 

dependent variable and the results further support our theoretical predictions. 

D. Are our results driven by sample bias? 

Our sample combines the holdings of euro area insurance companies from SHS collected since 

2014  and  those  collected  prior  to  2014  under  Securities  Holdings  Experimental  Statistics 

(SHES).  In  Columns  2  and  3  of  Table  6,  we  split  our  sample  in  these  two  periods  to  see 

whether  SHES  data,  which  were collected  on a  voluntary and best-efforts basis, could drive 

the  results  in  the  full  sample.  The  results  indicate  that  this  is  not  the  case  because  the 

coefficients of our variables of interest remain highly significant and the estimated effect of 

risk  premia  rises,  when  estimated  on  the  sample  that  excludes  SHES  data  (column  3). 

Moreover, the results based on SHES sample are also in line with our theoretical predictions, 

even if the significance of the risk-free rate coefficient is found to be only borderline (column 
2).37 In fact, the quality and coverage of SHES data on non-domestic holdings (i.e. data used 

in this regression) are fairly good since security-by-security data have been historically used 

in many euro area countries for the compilation of balance of payments statistics (see Fache 

Rousová and Rodríguez Caloca, 2018).   

Our  time  series  includes  the  European  sovereign  debt  crisis  and  the  following  Outright 

Monetary  Transactions  programme  of  the  ECB.  Under  this  programme,  the  ECB  is  ready  to 

purchase sovereign bonds of euro area Member States in the secondary market, under certain 

conditions. For this reason, we test the behaviour of insurance companies and the robustness 

of our results in the two sub-samples, before and after the OMT announcement in July 2012 

(see  columns  4  and  5  of Table  6).  The estimates of  our  variables of interest show a slightly 

lower sensitivity to changes in risk premia before the OMT announcement and a slightly lower 

sensitivity  to  changes  in  the  risk-free  rate  after  the  OMT  announcement.  However,  signs  do 

not vary and all the coefficients of interest remain significant. Similarly, in Column 6 of Table 

6, we test whether our results are driven by the Public Sector Purchase Programme of the ECB 

and we find no major changes in the estimates of our variables of interest. 

37 The p-value of 0.14 indicates significance at 15% confidence level. 

ECB Working Paper Series No 2299 / July 201937 
 
 
 
                                                 
During the European sovereign debt crisis, some countries (i.e. Cyprus, Spain, Greece, Ireland 

and  Portugal)  entered  the  bailout  programs  provided  jointly  by  the  International  Monetary 

Fund, the European Commission and the European Central Bank. In Column 7 of Table 6, we 

exclude  the  holdings  of  securities  issued  by  such  sovereigns  with  fundamental  risk  and  test 

whether they drive our estimates. We find that the sensitivity of insurers’ holdings to changes 

in risk premia increases, but signs and significance do not vary. 

Finally, our sample includes some bonds, for which the risk premium is negative, given that 

we construct it as a difference between the yield-to-maturity and EIOPA risk-free yield curve. 

We exclude these  bonds in  Column  8  of Table  6.  Our results  also  remain robust  in this sub-

sample. 

To  sum  up,  the  different  robustness  checks  provide  a  strong  empirical  support  to  our 

theoretical  predictions.  The  procyclical  effect  of  risk  premia  for  non-domestic  holdings  is 

found to be particularly robust as the coefficient remains negative and highly significant in all 

types of robustness checks, except for one regression, in which we use only three quarters of 

the  data  (Table  3,  column  3).  The  countercyclical  effect  of  the  risk-free  rate  is  also  fairly 

robust  to  the  different  empirical  specifications,  even  if  we  estimate  an  insignificant 

coefficient in the regressions in changes (Table 5, columns 5 and 7). The likely reason for the 

insignificant coefficient is, however, the high collinearity of the risk-free rate with the very 

granular holder country-quarter fixed effects, since the coefficient becomes significant with 

fewer granular time-fixed effects.  

6. CONCLUSIONS  

In this paper, we develop a stylised model of insurers’ investment behaviour, which predicts 

procyclicality  when  prices  fall  due  to  increasing  risk  premia,  and  countercyclicality  when 

prices  drop  due  to  rises  in  the  risk-free  interest  rate.  Using  security-by-security  data  on 

government bond holdings of euro area insurance companies from 2009 to 2016, we validate 

these predictions empirically. In line with the theoretical framework, we estimate a positive 

and  significant  effect  of  the  rise  in  the  risk-free  interest  rate  on  insurers’  holdings 

(countercyclicality),  while  the  effect  of  risk  premia  is  found  to  be  negative  and  significant 

(procyclicality).  These  results,  however,  only  hold  for  insurers’  holdings  of  foreign 

ECB Working Paper Series No 2299 / July 201938 
 
 
 
government  bonds,  while  domestic  bonds  appear  to  receive  preferential  treatment.  In 

particular, we find that insurers tend to respond countercyclically (rather than procyclically) 

to changes in the risk premia of their own sovereign. 

The  estimated  effects  for  non-domestic  government  exposures  are  not  only  statistically  but 

also economically important. While the size of the estimated effect is smaller for risk premia 

than for the risk-free rate, the overall effect of an interest rate change (or price change) on 

insurers’  holdings  also  depends  on  the  relative  size  of  the  changes  in  the  risk-free  rate  and 

risk  premia.  Therefore,  insurers’  investment  behaviour  in  our  model  turns  procyclical  in  a 

crisis scenario,  when changes in risk  premia  significantly  exceed  those  in the risk-free rate, 

and we show that this was the case during the euro area sovereign debt crisis. Our paper thus 

helps  explain  why  insurers’  investment  behaviour  tends  to  turn  procyclical  in  crisis  periods, 

even if previous empirical literature already suggested that this may be the case.  

Our  theoretical  framework  and  empirical  results  also  suggest  that  insurers’  investment 

behaviour  is  more  sensitive  to  price  changes,  when  insurers  operate  under  a  more  market-

consistent regulatory regime. This finding may help shed light on some of the mixed results in 

the existing literature such as the procyclical behaviour evidenced by Biljsma and Vermeulen 

(2017)  for  Dutch  insurers  (under  a  market-based  regime)  and  the  countercyclical  behaviour 

found by  Timmer  (2018)  for  German  insurers (under  a non-market based regime)  during the 

sovereign debt crisis.  

One shortcoming of our  analysis is,  however,  that  it  is too early for  us to empirically assess 

insurers’ investment behaviour under the current regulatory regime, Solvency II, as it entered 

into force only in January 2016. Another limitation of our paper is that our empirical results 

are estimated on a sample of government bonds only and thus cannot be generalised to other 

asset classes. By the same token, the findings are characteristic for a time period, in which 

the euro area sovereign debt crisis played a prominent role and the risk-free rate tended to 

gradually  decline.  Even  if  we  do  have  observations  of  increases  in  the  risk-free  rate  in  our 

sample  and  specifically  check  for  the  robustness  of  our  results  to  positive  and  negative 

changes in this rate, it is not clear to what extent our results would hold going forward if the 

risk-free  rate  were  to  rise  over  a  long-term  horizon  and  the  negative  duration  mismatch  of 

insurers were to fall.  

ECB Working Paper Series No 2299 / July 201939 
 
 
Our  findings  are  not  without  policy  implications.  By  providing  robust  evidence  of  insurers’ 

procyclicality during the euro area sovereign debt crisis, our paper contributes to the current 

policy  discussion  on  macroprudential  measures  beyond  banking.  Since  it  highlights  that 

insurers’  procyclicality  is  linked  to  changes  in  risk  premia,  it  can  be  instrumental  for  the 

development  of  an  effective  countercyclical  policy  instrument  that  targets  risk-premia 

volatility.  Some  insights  put  forward  by  this  paper  can  also  be  useful  in  a  broader  context 

such  as  in  the  development  of  system-wide  stress  tests  that  aim  to  model  the  investment 

behaviour of all sectors in the economy. One particular question in these models is who buys 

the  assets  whose  prices  have  declined.  Our  finding  that  insurers  bought  domestic  sovereign 

bonds  during  the  sovereign  debt  crisis,  even  if  their  risk  premia  and  credit  quality 

deteriorated,  can  provide  some  indication  as  to  where  to  look  for  the  potential  buyers  in  a 

crisis scenario.  

ECB Working Paper Series No 2299 / July 201940 
 
 
Sample  

Dependent Variable 

Risk premia (lag) 

Risk-free rate (lag) 

ECAI downgrade (lag) 

Debt/GDP (lag) 

Log PSPP volume (lag) 

Table 1: Baseline model and omitted variable bias 

(1) 

Full 

(2) 

Full 

(3) 

Full 

(4) 
Baseline 
Non-Domestic  

(5) 

Domestic  

-0.030*** 
(0.00) 
0.074*** 
(0.01) 

-0.020*** 
(0.00) 
0.079*** 
(0.00) 
-0.17*** 
(0.00) 
0.00065 
(0.71) 

Log Holdings 

-0.018*** 
(0.00) 
0.081*** 
(0.00) 
-0.17*** 
(0.00) 
0.00053 
(0.76) 
0.010** 
(0.02) 

168,643 
0.587 

-0.022*** 
(0.00) 
0.11*** 
(0.00) 
-0.18*** 
(0.00) 
-0.0035** 
(0.03) 
0.016*** 
(0.00) 

Y 
Y 
149,130 
0.603 

0.014* 
(0.09) 
-0.084** 
(0.03) 
-0.051 
(0.21) 
-0.020 
(0.62) 
-0.0044 
(0.73) 

Y 
Y 
19,513 
0.981 

Security FE 
Holder country-quarter FE 
Observations 
R-squared 

Y 
Y 
190,469 
0.603 

Y 
Y 
168,643 
0.587 

The  dependent  variable  is  the  logarithm  of  a  nominal  amount  of  single  government  bonds  held  by  insurance  companies  in 
different euro area countries. The sample includes exposures on zero- and fixed-coupon bonds with residual maturity between 
three  months  and  30  years.  All  independent  variables  are  lagged  by  one-quarter.  The  risk-free  rate  is  obtained  from  EIOPA’s 
risk-free interest rate term structures (using the same maturity as that of a given security). The risk premia are calculated as being 
spread between yield-to-maturity and the risk-free rate. The ECAI downgrade is a dummy equal to one if the credit quality of a 
security significantly deteriorates from one-quarter to another. Debt/GDP ratio is the amount of an issuer  country’s total gross 
government debt as a percentage of its GDP. Log PSPP volume is the log of the cumulative quarterly net purchases under the 
Public Sector Purchase Programme (aggregated by issuer country). In columns 4 and 5, the sample is limited to exposures of euro 
area insurers to non-domestic and domestic government bonds, respectively. ‘Security FE’ denotes security-specific fixed effects, 
‘Holder  country-quarter  FE’  denotes  fixed  effects  that  are  specific  for  quarterly  developments  in  a  holder  country.  Standard 
errors are clustered at holder country and individual security level and the corresponding p-values are reported in parenthesis. *** 
denotes significance at 1% level, ** significance at 5% level and * significance at 10% level. 

ECB Working Paper Series No 2299 / July 201941 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Second stage 
Dependent Variable 

Risk premia (lag) 

Risk-free rate (lag) 

First stage 

Instrument for risk-free rate 
US risk-free interest rate (lag) 

Instrument for risk premia 
Issuer country inflation rate (lag 2) 

Security FE 
Holder country-quarter FE 
Other controls 
Observations 
R-squared 

Table 2: Reverse Causality 

(1) 
Baseline 

(2) 

(3) 

(4) 

Log Holdings 

-0.022*** 
(0.00) 
0.11*** 
(0.00) 

-0.022*** 
(0.00) 
0.16*** 
(0.00) 

-0.16*** 
(0.00) 
0.087*** 
(0.01) 

-0.16*** 
(0.00) 
0.10* 
(0.07) 

0.458*** 
(0.00) 

Y 
Y 
Y 
148,703 
0.370 

0.458*** 
(0.00) 

0.255*** 
(0.00) 

Y 
Y 
Y 
142,341 
0.373 

0.255*** 
(0.00) 

Y 
Y 
Y 
142,341 
0.373 

Y 
Y 
Y 
149,130 
0.603 

Estimates are obtained using two-stage least-squares (2sls). The first panel shows the results of the second stage. The dependent 
variable is the logarithm of  a nominal amount of single government bonds held by insurance companies in different euro area 
countries. The sample includes exposures on zero- and fixed-coupon bonds with residual maturity between three months and 30 
years.  All  independent  variables  are  lagged  by  one-quarter.  The  risk-free  rate  is  obtained  from  EIOPA’s  risk-free  interest  rate 
term  structures  (using  the  same  maturity  as  that  of  a  given  security).  The  risk  premia  are  calculated  as  being  spread  between 
yield-to-maturity and the risk-free rate. The second panel shows the results of the first stage. US risk-free interest rate is the yield 
curve obtained from the US risk-free interest rate term structures. Inflation rate is the inflation rate of a country, measured by the 
Consumer  Price  Index.  ‘Security  FE’  denotes  fixed  effects  that  are  specific  for  each  security.  ‘Holder  country-quarter  FE’ 
denotes  fixed  effects  that  are  specific  for  quarterly  developments  in  a  holder  country.  ‘Other  controls’  include  all  the  control 
variables  listed  in  the  baseline  specification  (see  column  4  of  Table  1).  The  Generalised  Method of  Moments  (GMM)  returns 
very  similar  estimates.  Standard  errors  are  clustered  at  holder  country  and  individual  security  level  and  the  corresponding  p-
values are reported in parenthesis. *** denotes significance at 1% level, ** significance at 5% level and * significance at 10% 
level.  

ECB Working Paper Series No 2299 / July 201942 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3: Robustness checks – less and more market-based regimes prior to Solvency II 

Sample 

(1) 
Baseline 
Full 

(2) 

(3) 

(4) 

(5) 

w/o 2016 
(09q1-15q4) 

only 2016 
(16q1-16q4) 

Countries with 
less 
market-based 
regime 
prior to SII 

Countries with 
more 
market-based 
regime 
prior to SII 

(w/o 2016) 

(w/o 2016) 

Dependent variable 

Log Holdings 

Risk premia (lag) 

Risk-free rate (lag) 

ECAI downgrade (lag) 

Debt/GDP (lag) 

Log PSPP volume (lag) 

Security FE 
Holder country-quarter FE 
Observations 
R-squared 

-0.022*** 
(0.00) 
0.11*** 
(0.00) 
-0.18*** 
(0.00) 
-0.0035** 
(0.03) 
0.016*** 
(0.00) 

Y 
Y 
149,130 
0.603 

-0.019*** 
(0.00) 
0.087*** 
(0.00) 
-0.17*** 
(0.00) 
-0.0045*** 
(0.01) 
0.015*** 
(0.01) 

Y 
Y 
121,507 
0.599 

-0.0030 
(0.90) 
0.13** 
(0.01) 
-0.067 
(0.58) 
0.0043 
(0.31) 
-0.030 
(0.22) 

Y 
Y 
27,623 
0.659 

-0.016* 
(0.07) 
0.0032 
(0.95) 
-0.16*** 
(0.00) 
-0.0022 
(0.40) 
0.039*** 
(0.00) 

Y 
Y 
34,985 
0.620 

-0.022*** 
(0.00) 
0.10*** 
(0.00) 
-0.19*** 
(0.00) 
-0.0059*** 
(0.00) 
0.014*** 
(0.01) 

Y 
Y 
86,522 
0.656 

The  dependent  variable  is  the  logarithm  of  a  nominal  amount  of  single  non-domestic  government  bonds  held  by  insurance 
companies  in  different  euro  area  countries.  The  sample  includes  exposures  on  zero-  and  fixed-coupon  bonds  with  residual 
maturity between three months and 30 years. All independent variables are lagged by one-quarter. The risk-free rate is obtained 
from EIOPA’s risk-free interest  rate term  structures (using the same  maturity  as that of a  given security).  The risk premia are 
calculated  as  being  spread  between  yield-to-maturity  and  risk-free  rate.  The  ECAI  downgrade  is  a  dummy  equal  to  one  if  the 
credit quality of a security significantly deteriorates from one-quarter to another. The debt/GDP ratio is the amount of an issuer 
country's total gross government debt as a percentage of its GDP. Log PSPP volume is the log of the cumulative quarterly net 
purchases under the Public Sector Purchase Programme. In 2016 q1, the quality of the insurance data in SHS improved due to the 
new requirement of direct reporting by insurance corporations (and at the same time the Solvency II regulatory regime entered 
into force). ‘Countries with less market-based regime’ are the countries that use large transitional measures under the Solvency II 
regulatory regime: GR, ES, PT, DE and FI. ‘Countries with more market-based regime’ are countries using less/no transitional 
measures and are the remaining euro area countries. The transitional dummy is equal to 1 if a holder country belongs to those 
having large transitional measures. ‘Security FE’ denotes fixed effects that are specific for each security. ‘Holder country-quarter 
FE’ denotes fixed effects that are specific for quarterly developments in a holder country. Standard errors are clustered at holder 
country and individual security level and the corresponding p-values are reported in parenthesis. *** denotes significance at 1% 
level, ** significance at 5% level and * significance at 10% level.  

ECB Working Paper Series No 2299 / July 201943 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4: Robustness checks – measurement of risk-free rate 

Dependent Variable 

Risk premia (lag) 

Risk-free rate (lag) 

Risk premia OIS (lag) 

Risk-free rate OIS (lag) 

Risk premia DE (lag) 

Risk-free rate DE (lag) 

Security FE 
Holder country-quarter FE 

Other controls 
Observations 
R-squared 

(1) 
Baseline 

-0.022*** 
(0.00) 
0.11*** 
(0.00) 

Y 
Y 

Y 
149,130 
0.603 

(2) 

(3) 

Log Holdings 

-0.020*** 
(0.00) 
0.094** 
(0.01) 

Y 
Y 

Y 
149,130 
0.603 

-0.022*** 
(0.00) 
0.12*** 
(0.00) 

Y 
Y 

Y 
149,130 
0.603 

The  dependent  variable  is  the  logarithm  of  a  nominal  amount  of  single  non-domestic  government  bonds  held  by  insurance 
companies  in  different  euro  area  countries.  The  sample  includes  exposures  on  zero-  and  fixed-coupon  bonds  with  residual 
maturity between three months and 30 years. All independent variables are lagged by one-quarter. The risk-free rate is obtained 
from EIOPA’s risk-free interest  rate term  structures (using the same  maturity  as that of a  given security).  The risk premia  are 
calculated as being spread between yield-to-maturity and risk-free rate. ‘Risk-free rate DE’ represents the yield curve of 10-year 
government  bonds  issued  by  Germany,  which  we  use  as  a  second  proxy  for  risk-free  rate,  and  ‘risk  premia  DE’  the  spread 
between the yield-to-maturity of a security and the German yield at the same maturity. Similarly, ‘risk-free rate OIS’ denotes the 
overnight  index  swap  yield  curve,  and  ‘risk  premia  OIS’  is  the  spread  between  the  yield-to-maturity  of  a  security  and  the 
overnight index swap yield at the same maturity. ‘Security FE’ denotes fixed effects that are specific for each security. ‘Holder 
country-quarter  FE’  denotes  fixed  effects  that  are  specific  for  quarterly  developments  in  a  holder  country.  ‘Other  controls’ 
include  all  the  control  variables listed  in  the  baseline  specification  (see  column  4 of  Table  1).  Standard  errors  are  clustered  at 
holder country and individual security level and the corresponding p-values are reported in parenthesis. *** denotes significance 
at 1% level, ** significance at 5% level and * significance at 10% level.  

ECB Working Paper Series No 2299 / July 201944 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ECB Working Paper Series No 2299 / July 201951 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex B: Variables description 

Variable 

Log 
Holdings 

Yield-to-
maturity 

Risk-free 
rate EIOPA 

Risk-free 
rate OIS 
Risk-free 
rate DE 

Risk 
premium 

ECAI 
downgrade 

Description 

Natural logarithm of the nominal amounts of government bond holdings of euro area insurers. 
Includes holdings of zero- and fixed-coupon bonds with residual maturity of between three months 
and 30 years (ECB’s SHS Sector data) 

Interest rate of single government bonds in a given quarter (ECB’s CSDB) 

Risk-free interest rate term structures, published monthly by EIOPA. The risk-free yield curves are 
based on liquid swap and government bond rates, and then adjusted to include the counterparty 
default risk (https://eiopa.europa.eu/regulation-supervision/insurance/solvency-ii-technical-
information/risk-free-interest-rate-term-structures) 

Overnight index swap yield curve (Bloomberg) 

Yield curve of 10-year government bonds issued by Germany (ECB – Euro area government bond 
yield curves) 

Difference between the yield-to-maturity of a security and the risk-free-rate at the same maturity 

A dummy that is equal to one if the bond’s credit quality changed from a lower to a higher ECAI 
credit quality step between two consecutive quarters. ECAI credit quality steps are defined in 
accordance with the Eurosystem credit assessment framework (ECAF), which provides a harmonised 
rating scale classifying ratings into three credit quality steps. The first category includes securities 
rated from AAA to AA-, the second from A+ to A- and the third from BBB+ to BBB-. A fourth 
category is added which includes all rated securities with a rating below credit quality step three. 

Debt/GDP 

Amount of a country's total gross government debt as a percentage of its GDP (OECD 
https://data.oecd.org/gga/general-government-debt.htm#indicator-chart) 

Log PSPP 
volume 

Natural logarithm of the cumulative quarterly net purchases under the Public Sector Purchase 
Programme (ECB https://www.ecb.europa.eu/mopo/implement/omt/html/index.en.html) 

Inflation 
rate 

Quarterly inflation rate of a country, measured by the Consumer Price Index (OECD 
https://data.oecd.org/price/inflation-cpi.htm) 

ECB Working Paper Series No 2299 / July 201952 
 
 
  
 
 
 
Acknowledgements 

The authors would like to thank Maciej Anacki, Roel Beetsma, Nicola Branzoli, Lorenzo Cappiello, Casper Christophersen, Giovanni di 
Iasio, Dieter Hendrickx, Petr Jakubik, Sujit Kapadia, Spyros Palligkinis, Daniel Perez, Alexander Popov, Joao Santos, Jialu Shen, Nadav 
Steinberg, Christiaan Wanningen and Dimitris Zafeiris for their helpful comments and suggestions. The paper also benefited from useful 
feedback from the participants of the 2nd Workshop on Systemic Stress, Investor Behaviour and Market Liquidity of the Financial 
Stability Board (FSB) in Amsterdam, the 2018 RiskLab/BoF/ESRB Workshop on Systemic Risk Analytics, the 2018 EFMA conference in 
Milan, the 2018 CEPR/BoI conference on Systemic Risk and Macroprudential Policy in Tel Aviv, as well as from the participants of 
internal seminars at the ECB and the members of the ESRB Insurance Expert Group. We remain responsible for any errors or 
omissions. 

Linda Fache Rousová 
European Central Bank, Frankfurt am Main, Germany; email: linda.fache_rousova@ecb.europa.eu 

Margherita Giuzio 
European Central Bank, Frankfurt am Main, Germany; email: margherita.giuzio@ecb.europa.eu 

© European Central Bank, 2019 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

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www.ecb.europa.eu 

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

PDF 

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ISSN 1725-2806 

doi:10.2866/181005 

QB-AR-19-080-EN-N