Insurers’ investment behaviour and the coronavirus (COVID-19) pandemic.
Date: 2023-09-21
Institutions: European Systemic Risk Board
Subjects: coronavirus disease, crisis management, economic consequence, financial risk, financial stability, insurance

Occasional Paper Series 

No 22 

Insurers’ investment behaviour and 
the coronavirus (COVID-19) 
pandemic 

by 
Constanze Fay 
Angelica Ghiselli 

 
 
 
 
  
 
Contents 

Abstract 

Executive summary 

1 

2 

3 

Introduction 

Data 

Descriptive statistics related to COVID-19 

3.1 

Aggregate descriptive statistics 

3.2  Country-level descriptive statistics 

4 

Regressions 

4.1 

Summary statistics 

4.2  Main results 

4.3  Robustness tests 

5 

Conclusion 

References 

Annex 48 

A1  Methodology 

A2 

Additional analyses 

Imprint and acknowledgements 

2 

3 

4 

9 

12 

12 

16 

19 

19 

25 

37 

42 

44 

48 

49 

59 

Occasional Paper Series No 22 
Contents 

1 

 
 
Abstract 

This research explores two aspects of European insurers’ investment behaviour related to crises. 

While they are often considered as financial market stabilisers and long-term investors, there is 

currently a lack of knowledge about insurers’ investment behaviour in crises under the regulatory 

Solvency II regime implemented in 2016. With assets of nearly €9 trillion and bond holdings of more 

than €3 trillion in Q2 2022, European insurers are important financial intermediaries and finance 

European economies. With an empirical study, we investigate their reaction to the asset price 

shock at the onset of the coronavirus (COVID-19) pandemic in the first quarter of 2020 and explore 

cyclical investment behaviour by replicating Timmer’s (2018) study with fixed effects panel 

regressions. We use a large cross-country dataset, with the novelty of exploiting cross-country 

heterogeneity for European countries with 458,758 security-level observations from 2017 to 2022. 

Overall, our findings are very relevant from a policy perspective as they suggest active and 

heterogeneous cyclical investment behaviour in the European insurance market with differences 

across issuer and holder countries of domicile. 

Keywords: Insurance Companies, Cyclicality, Portfolio Allocation, Financial Stability, Pandemic, 

Debt Capital Flows. 

JEL Codes: G01, G11, G15, G22, G28. 

Occasional Paper Series No 22 
Abstract 

2 

 
 
 
Executive summary 

In this paper, we explore two aspects of European insurers’ investment behaviour related to 

crises. With an empirical study, we investigate their reaction to the asset price shock at the onset 

of the coronavirus (COVID-19) pandemic in the first quarter of 2020 and explore cyclical investment 

behaviour by replicating Timmer’s (2018) study with fixed effects panel regressions. In line with 

other works (Fache Rousová and Giuzio, 2019b; Carvalho and Schmitz, 2021), we use a large, 

confidential cross-country dataset, with the novelty of exploiting cross-country heterogeneity for 

European countries with 458,758 security-level observations from the first quarter of 2017 to the 

second quarter of 2022. Insurance markets in Europe differ in terms of the type of savings products 
offered, insurers’ capitalisation ranges1 and investment strategies. For index-linked and unit-linked 

insurance, for example, policyholders bearing the investment risk account for between 6% and as 
much as 70% of total insurance assets.2 Regarding capitalisation, the long-term guarantee (LTG) 

support measures under Solvency II (SII) improved average country-level solvency ratios up to 47 

percentage points in 2019 (EIOPA, 2020b). Lastly, insurers in several countries have a home bias 

in their investments, for example when exposure to the domestic sovereign exceeds 80% of total 

holdings in government bonds (EIOPA, 2022). 

With assets of nearly €9 trillion and bond holdings of more than €3 trillion, European 
insurers are important financial intermediaries and finance European economies.3 While they 

are often considered financial market stabilisers and long-term investors, there is currently a lack of 

knowledge about insurers’ investment behaviour in crises under the regulatory SII regime 

implemented in 2016. To the best of our knowledge, EIOPA (2021, 2022) and Brinkhoff and Solé 

(2022), who investigate European life insurers from 2005 to 2021, are the only authors to have 

studied a multi-period, cross-country sample since the introduction of SII. Other related studies are 

Bijlsma and Vermeulen (2016), Domanski, Shin and Sushko (2017), Timmer (2018), Carvalho and 

Schmitz (2021) and Apicella, Gallo and Guazzarotti (2022), who analyse shorter, pre-SII or single-

country samples. 

Notwithstanding the fact that our research refers to a period of low interest rates, our 

findings have policy implications beyond market cycles. First, we demonstrate that insurers 

are active and not buy-and-hold investors, given that they rebalanced their portfolios during the 

shock in the first quarter of 2020. Second, and over a longer period from the first quarter of 2017 to 

the second quarter of 2022, we find confirmation of active investment strategies in certain countries 

and situations. For example, our regression results point to countercyclical investments in Belgium 

and the Netherlands and – for government bonds – in Italy during the COVID-19 shock. The use of 

derivatives, product features, interconnectedness and national investment regulations may explain 

country-level differences. To better understand the drivers of this behaviour, company-level data 

are needed. Investigating such drivers (e.g., liquidity needs, solvency ratios or duration mismatch) 

are topics for future research. 

1   See Figure 2.15 in EIOPA (2022), p.40. 

2   See EIOPA Insurance Statistics Solvency II reporting template S.06.02. solo quarterly, second quarter of 2022. 

3   Kaufmann, Leyva and Storz (2022) show that euro area insurers held more than a quarter of total euro area corporate debt 

in the second quarter of 2021; see also Kubitza (2022). 

Occasional Paper Series No 22 
Executive summary 

3 

 
 
1 

Introduction 

While the financial health of European insurers is fairly stable, it is unclear whether and how 

they react to asset price shocks. Since the implementation of the Solvency II (SII) regulation, 

insurers are expected to be more sensitive to such shocks as SII is a market-based regime. 

Accordingly, insurers must value their assets and liabilities based on market values, and negative 

shocks might hamper their balance sheets and solvency ratios. This is apparent when interest rates 

are low and the present value of insurers’ liabilities is high. Against such a backdrop, some 
researchers have observed that insurers invest in riskier assets in search for yield.4 At the same 

time, insurers must fulfil the solvency capital requirement to withstand shocks with a 99.5% 

probability over the next year. Solvency ratios describe the relationship between own funds and 

solvency capital requirements and must exceed 100%. Declining asset prices due to increases in 

risk spreads (ceteris paribus) lead to lower solvency ratios. To protect their solvency ratios against 

greater losses if asset prices were to fall further, insurers could potentially conduct large-scale 

asset sales in downturns, triggering fire sales and posing risks to financial stability. 

There is currently little knowledge about the drivers of European insurers’ investment 

behaviour, including solvency ratios and investment gains. The relationship between 

investments and solvency ratios is as follows: life and composite insurers predominantly invest in 

fixed income instruments to match the characteristics of their assets and liabilities. As the 

characteristics of assets and liabilities typically do not perfectly match, volatility in fixed income 

prices contributes to fluctuations in own funds and solvency ratios. An increase in credit spreads 

decreases own funds and may increase solvency capital requirements, leading to lower overall 

solvency ratios. Hence, insurers with solvency ratios close to the regulatory requirement have 

greater incentives to sell risky assets in crises. On the other hand, potential contributors to 

countercyclical investment behaviour are the capital support LTG measures, which aim to reduce 
the volatility of solvency ratios.5 Another reason for selling assets are unrealised investment gains 
under national accounting laws.6 These unrealised gains present buffers to support profits shared 

with life insurance policyholders. Other possible drivers for insurers’ investment behaviour are 

liquidity needs, compliance with the defined investment strategy (e.g., triggering sales of 

downgraded bonds), business models and premium flows (Kubitza, 2022) as well as deliberate 

countercyclical investments.  

This work is intended to contribute to the empirical research on European insurers’ 

investment behaviour from a macroprudential perspective. First, we complement the emerging 

literature on the role of insurers as financial intermediaries by investigating their behaviour in 

4   See Fache Rousovà and Giuzio (2019a), FMA (2019), Deutsche Bundesbank (2021) and Becker and Ivashina (2015). 

Brinkhoff and Solé (2022) estimate that the search for yield accounted for about one-third of the total deterioration in credit 
quality of European life insurers’ portfolios from 2005 to 2021. 

5   Due to the mismatch of assets and liabilities, the volatility adjustment LTG measure can also lead to higher and more 

volatile solvency ratios, as observed in AT, BE and NL in the first quarter of 2020. See ESRB (2021), p. 111 and ESRB 
(2018), pp. 20-30. 

6  

It should be noted that, under French accounting rules, the result realised by insurers on bond sales does not directly affect 
financial results. Indeed, gains realised on such sales are booked in a reserve account (called  a  “capitalisation reserve”), 
while losses are offset by a decrease in this reserve. As a consequence, this mechanism significantly reduces fluctuations in 
the financial results of insurers, even in the event of significant variations in interest rates. 

Occasional Paper Series No 22 
Introduction 

4 

 
 
shocks. Insurers’ investment decisions in crises can be triggered by many reasons, including 

margin calls (Fache Rousová, Ghio, Kördel and Salakhova, 2020), interconnectedness within the 

financial system (Billio et al., 2011; Baluch, Mutenga and Parsons, 2011; Jourde, 2022), intra-

sector developments such as duration gaps (Domanski, Shin and Sushko, 2015; Timmer, 2018), 

company-specific characteristics such as low excess of assets-to-liabilities or solvency ratios (Van 

Binsbergen and Brandt, 2016; Banca d’Italia, 2021) or a combination of external and internal 

factors, including risk limits (Bijlsma and Vermeulen, 2016). In this respect we complement the 

works developing around the coronavirus (COVID-19). Carvalho and Schmitz (2021) focus on the 

portfolio shifts of euro area investors in the first and second quarters of 2020, also considering 

investments within funds. They find a strong preference for domestic sovereign debt. In the United 

Kingdom, Czech, Gual-Ricart, Lillis and Worlidge (2021) find insurers sell this asset class, leading 

to a sharp increase in trading volumes. In Italy, Apicella, Gallo and Guazzarotti (2022) show that 

only well capitalised insurers were able to act countercyclically in the first quarter of 2020. Our 

contribution to this literature is to take a country-level perspective by investigating a multi-period 

European sample of insurers in Austria, Belgium, France, Germany, Ireland, Italy, Luxembourg, the 

Netherlands, Portugal, and Spain from the first quarter of 2017 to the second quarter of 2022. Our 

sample is also new, as insurers under SII are no longer subject to regulatory quantitative 
investment rules but must abide by the prudent person principle in their investments.7 With detailed 

descriptive statistics along several asset characteristic dimensions, we show changes in the 

composition of insurers’ portfolios during the capital market shock in the first quarter of 2020, 

implying that they actively adjust them in response to short-term asset price movements. In the first 

quarter of 2020 they purchased and sold securities worth €680 billion, mainly focusing on 

government debt. 

We further add to the literature by investigating whether and how European insurers behave 

procyclically in their investments. Following Timmer (2018), we define procyclicality as the 

selling or purchasing of securities in the period following a price decline or increase. Though 

traditionally considered countercyclical investors who purchase bonds in periods of falling prices, 

there is empirical evidence that insurers can amplify trends in bond prices (Duijm and Steins 

Bisschop, 2018; Domanski, Shin and Sushko, 2017). Fache Rousová and Giuzio (2019b) explain 

their behaviour with changes in interest rates and risk premia and find that the latter drive 

procyclical investment behaviour with the exception of domestic sovereign debt. Over a longer 

period from 2005 to 2021 and to the contrary, however, Brinkhoff and Solé (2022) find that 

European life insurers largely retained their buy-and-hold strategies. To the best of our knowledge, 

we are the first to replicate Timmer’s (2018) study to assess the cyclical investment behaviour of 

European SII insurers in several countries. Timmer (2018) finds countercyclical behaviour for 

German insurers in his cross-sector study from the fourth quarter of 2004 to the fourth quarter of 

2014. Following the author, we regress the percentage change in the nominal holding of a debt 

security held by insurers in a country on the security’s lagged holding period return. In line with the 

literature (Bijlsma and Vermeulen, 2016; Fache Rousová and Giuzio, 2019b; Czech, Gual-Ricart, 

Lillis and Worlidge, 2021), we find procyclical investment behaviour in certain situations. These 

situations refer to market shocks, to the domicile of the insurer or bond issuer and to specific bond 

market segments. 

7   See Art. 132 of the Solvency II Directive 2009/138/EC of the European Parliament and of the Council of 25 November 

2009 on the taking-up and pursuit of the business of Insurance and Reinsurance (Solvency II), 30.06.2021. 

Occasional Paper Series No 22 
Introduction 

5 

 
 
Our novel method complements aggregated descriptive statistics with regression analysis 

of 458,758 security-level observations. In a first step, we focus on the crisis in the first quarter of 

2020 and compute aggregate and country-level descriptive statistics on transaction volumes, shifts 

between asset classes, ratings, maturities, and geographies. To better understand the 

counterparties of each trade, we compare insurers’ investments with those of banks and investment 

funds. These two sectors showed higher turnover at the onset of the pandemic, with investment 

funds typically being net sellers and banks typically being net buyers. Furthermore, we consider 

matured bonds and the return of principal as a source of liquidity for insurers. To account for 

seasonality in premiums, we extend our analysis to the preceding and following quarters. In the 

second-step regressions, we aim to better understand whether and in which European insurance 

markets systemic risks from investments are more likely. Given their importance in insurers’ asset 

allocations, and for this exercise, we focus on debt securities. 

As illustrated in Chart 1a, bonds accounted for around 50% of European insurers’ asset 

allocations before the COVID-19 pandemic, most of which were government bonds. In some 

Member States, the majority of these government bonds are domestic (Chart 1b). To gain new 
insights on the insurance-sovereign nexus8 and to understand whether there is a “preferred habitat” 

(Giese, Joyce, Meaning and Worlidge, 2021) in insurers’ transactions, we focus on government 

bonds in the regressions. To test whether insurers change their behaviour in shocks more formally, 

we introduce a COVID-19 dummy variable. For robustness, we conduct additional tests for samples 

of all bonds, and of financial and non-financial sector bonds separately. Our analysis concludes 

with an explorative study relating the solvency ratios of the 10% least capitalised insurers to 

investment behaviour. 

8   See also Düll, König and Ohls (2017). 

Occasional Paper Series No 22 
Introduction 

6 

 
 
Chart 1 

Asset allocation of European insurers 

a) Asset allocation over time of European insurers 

(percentages and EUR billion, fourth quarter of 2017 to second quarter of 2022) 

b) Government bond holdings by domicile of the issuer 

(percentages and EUR billion, fourth quarter of 2019) 

Sources: Public EIOPA insurance statistics, Solvency II reporting template S.06.02. solo quarterly (panel a); and EIOPA (2020a; 
panel b). 
Note: Panel a) refers to insurers in Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, 
Portugal, and Spain. 

Occasional Paper Series No 22 
Introduction 

7 

6,5007,0007,5008,0008,5000%20%40%60%80%100%Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022Government bondsCorporate bondsEquityCollective investment undertakingsStructured notesCollateralised securitiesCash and depositsMortgages and loansPropertyOther investmentsNot reportedTotal assets (rhs)01002003004005006007008000%20%40%60%80%100%ESITFRBEPTDENLATFIIELUDomesticOther EU/EEA countriesEU institutionsUnited StatesSwitzerlandCanadaJapanSupranational issuersEmerging markets and other advanced economiesUnited KingdomTotal assets (rhs) 
 
 
 
 
Fixed effects panel data regressions best capture the stable characteristics of European 

insurance markets. These country-level characteristics comprise, for example, the share of index-

linked and unit-linked insurance, insurers’ investments via funds or national investment 
regulations,9 which can affect insurers’ investment behaviour and do not or only slightly change 

over time. These characteristics are captured by country-level fixed effects (FE) in our model. As 

explained in the data section (Section 2), our unit of observation are individual securities held by 
insurers in a country.10 Our panel is unbalanced, as not all security-holder country combinations are 

held over the entire sample period. In addition, as there is no information on the specific insurance 

company holding the security, we cannot control for variables which vary at the insurer level, such 

as solvency ratios or index-linked and unit-linked business. However, since our aim is to strictly 

follow Timmer (2018), we do not need this information as we focus on security-level information 

and use a parsimonious regression approach. Furthermore, since our research relates to cyclical 

investment behaviour, we concentrate on a model that gains the regression estimates from the 

variation in variables over time. Thereby, in addition to country-level FE, we also control for 

security-level FE such as issuer country, duration, and credit quality, which may influence 

investment behaviour. The controls are implemented by a process of “demeaning” in the fixed 

effects regression, as deviations from the means of the individual securities at the holder country 

level are considered. Moreover, significant estimators in fixed effects models explain differences in 

insurers’ investment behaviour for individual security-holder country pairs from one quarter to the 

next (“within estimator”). 

Our results provide evidence for policy conclusions on insurers’ long-term investments and 

liquidity risk. Insurers are broadly considered as long-term buy-and-hold investors. We show that 

some SII insurers invest procyclically in government and in financial sector bonds, which can affect 

the liquidity of markets and “liquid” assets such as money market funds (MMFs). We also find 

indications of a preference for domestic government bonds and for asymmetric behaviour in market 

downturns as compared with market upturns (see also Duijm and Steins Bisschop, 2018). An 

important ancillary finding of our analysis is that European insurers invest substantially in bonds 

which are neither listed nor actively traded. This points to liquidity needs as another driver of 

insurers’ investment behaviour observed in the first quarter of 2020. 

This paper is organised as follows: Section 2 describes the data, Section 3 shows the descriptive 

results, Section 4 the regressions and Section 5 concludes. Additional analyses and further 

explanatory notes on the methodology to retrieve the dataset are set out in the annex. 

9   See, for example, Insurance Undertaking Investment Regulation (Versicherungsunternehmen 

Kapitalanlageverordnung - VU-KAV) BGBL II Nr. 423/2015, which provides requirements for insurers’ investments in 
accordance with the prudent person principle (see also Art. 132 of the SII directive). 

10  

Insurers in several countries also often hold the same securities over different periods.  

Occasional Paper Series No 22 
Introduction 

8 

 
 
2 

Data 

Our sample comprises data for all euro area insurance markets with assets larger than €50 

billion in Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg, the 

Netherlands, Portugal, and Spain. The security-level data of the ECB’s Securities Holdings 

Statistics by Sector (SHSS) provide confidential quarterly information on the securities holdings of 
euro area investors at the holder country and sector level.11 The analysis of SHSS data allows for a 

comparison of the investment behaviour of insurers with that of other financial intermediaries and 

has the advantage of providing granular, security-level data focusing typically on more liquid assets 

in insurers’ portfolios. The data do not include information about individual insurers or insurance 
business lines, and our sample is therefore a composite of life and non-life insurance.12 For data 

quality reasons, we analyse SHSS data starting from the first quarter of 2017 and retrieve 

information on market and notional values of securities holdings until the second quarter of 2022. 

Notional values refer to the nominal outstanding amount of debt securities and are unaffected by 

changes in market prices. Market prices and information on credit ratings in SHSS are derived from 

the Centralised Securities Database (CSDB). In our research we consider market and notional 

values of insurers’ securities holdings. 

For the descriptive statistics related to COVID-19 (Section 3), we aggregate security-level 

data at the country level and analyse transaction data. Transaction data in SHSS are either 

reported by national central banks or calculated otherwise. Transactions are calculated as the 
change of positions in nominal value, taking into account “other changes” in volume,13 multiplied by 

the average price and divided by the average exchange rate. The average price is the arithmetic 

mean of the price value of a security at time t and t-1 (latest available price for the period), similar to 

the average exchange rate. This only applies to cases in which no transaction is reported. If the 

national central bank reports a transaction, the reported transaction is used. In our analysis, 

positive transaction values represent purchases and negative transaction values represent sales. 

Debt securities expiring in a specific quarter are negative transactions in SHSS, as they are no 

longer held at the end of the quarter. Transaction data are available for the main asset classes as 

defined by ESA (2010). In addition, we retrieve data on the bond’s issuer country and credit rating.  

For the regressions (Section 4), we follow Timmer (2018) and investigate changes in the 

nominal values of individual bond securities holdings in response to securities’ returns. 

Furthermore, we compute lagged securities’ returns as the changes in price from the start to the 

end of the previous quarter, plus quarterly coupon income, and divide the sum by the previous 

quarter’s starting price. Bonds comprise long-term and short-term debt securities (hereinafter 

“bonds” for simplicity) which pay no or fixed annual or semi-annual coupons. For data quality 

reasons, we select fixed or zero-coupon bonds only. In addition, given our research question and 

11   All aggregate data points for which more than 85% belong to two or fewer insurers in our study have been cleared for 

confidentiality by national central banks. 

12   Differences in the investment behaviour of life and non-life insurers in shocks may relate to the interest rate environment 

(see also Duijm and Steins Bisschop (2018), Kirti and Shin (2020), and Apicella, Gallo and Guazzarotti (2021)). 

13   Other changes refer to changes in data attributes between current and previous quarter such as the issuer sector, the 

issuer country, instrument class, and maturity date. 

Occasional Paper Series No 22 
Data 

9 

 
 
following Timmer (2018), our sample comprises observations with changes in holdings only.14 
Considering the few observations, the large share of pension assets15 and the variation in the data 

observed in the descriptive statistics, we exclude Finnish insurers from the regressions. Our sample 

comprises 458,758 security-level observations from the first quarter of 2017 to the second quarter 

of 2022. 

𝑅𝑒𝑡𝑢𝑟𝑛𝑠,𝑡−1 =

𝑃𝑟𝑖𝑐𝑒𝑠,𝑡−1 − 𝑃𝑟𝑖𝑐𝑒𝑠,𝑡−2 + 𝐶𝑜𝑢𝑝𝑜𝑛𝑠
𝑃𝑟𝑖𝑐𝑒𝑠,𝑡−2

(1) 

Several aspects must be considered when analysing SHSS data, such as their incomplete 

coverage of holdings focusing on securities that are listed and can be easily traded only. 

Like many other published studies, our sample includes securities with an international securities 
identification number (ISIN).16 Securities with an ISIN are typically listed and can be easily traded.17 

When comparing SHSS with SII data that include all bonds, we find that insurers’ holdings of bonds 

without ISINs vary substantially across countries. For example, €445 billion or 50% of the total bond 

volume held by German insurers and €15 billion or 26% of the total bonds held by Luxembourgish 

insurers as of the fourth quarter of 2019 have no ISIN. In Germany, these bonds without ISINs 

consist of private sector as well as government bonds (the latter being issued mainly by states, 

municipalities, or publicly owned enterprises). Notwithstanding these coverage differences, in the 

regressions we analyse only those directly held securities with changes in holdings from one 

quarter to the next. Chart 2 compares the total asset allocation based on SII data with the relative 

amount of securities with changes in holdings from SHSS data as of the end of the fourth quarter of 

2019. The dots show that this amount is very small in Germany, indicating very stable overall direct 

holdings of bonds with ISIN codes by German insurers. 

14   For robustness, we also conduct regressions including securities holdings without changes and find broadly the same 

results.  

15   See the presentation by Teija Korpiaho, FIN-FSA (ESRB Insurance Expert Group, 02.05.2022), and the Finnish Pension 

Alliance’s (TELA) website. 

16   Less than 1% of securities holdings of government bonds in our sample have a different identifier type (e.g., CUSIP, 

internal code, SEDOL).  

17   The relatively low frequency of quarterly data appears less relevant, as bonds often do not trade on a daily basis. MiFIR 
transaction reporting is daily, but insurers are exempt from it; see Art. 26 of the MiFIR regulation (EU) 600/2014. 

Occasional Paper Series No 22 
Data 

10 

 
 
 
Chart 2 

Asset allocation and listed bonds of European insurers 

(percentages, fourth quarter of 2019) 

Sources: Public EIOPA insurance statistics, Solvency II reporting template S.06.02. solo quarterly; ECB SHSS; and authors’ 
calculations. 
Note: Regression bonds in SHSS refer to insurers’ holdings of bonds with ISINs and changes as compared with the previous 
quarter. 

Another difference between SHSS and SII data is the security classification and the 

consideration of index-linked and unit-linked insurance. Whereas SHSS refer to the ESA 
(2010) asset classification, SII uses a regulatory taxonomy18 that categorises bonds guaranteed by 

governments as government bonds and does not distinguish between financial and non-financial 

sector bonds. Government bonds in SHSS are general, central, state, and local government bonds 

and social security funds. EIOPA (2021, 2022) conducts complementary analysis to ours, and the 

different samples explain the two, in both cases very small deviating descriptive statistics for the 
first quarter of 2020.19 Another shortcoming is that SHSS do not account for investment fund shares 
held for index-linked and unit-linked insurance that reflect policyholder choices.20 To deal with this 

caveat, we focus on direct bond holdings in our regression analysis. 

18   See Annex II of the Commission Implementing Regulation (EU) 2015/2450 of 2 December 2015. 

19   Whereas EIOPA’s public analysis for the first quarter of 2020 focuses on corporate bonds based on aggregated data, we 
focus on government bonds and on country-level data. See EIOPA (2021a), pp. 67-69 and EIOPA (2022), pp. 74-80. 

20   See also the study on the interlinkages between alternative investment funds and insurers in ESRB (2022), pp. 32-36. 

Occasional Paper Series No 22 
Data 

11 

0%10%20%30%40%50%60%70%80%90%100%ATBEDEESFRIEITLUNLPTGovernment bondsCorporate bondsCollective investment undertakingsEquityStructured notesCollateralised securitiesCash and depositsMortgages and loansPropertyOther investmentsNot reportedRegression bonds in SHSS 
 
 
3 

Descriptive statistics related to COVID-19 

In this section we provide an overview of insurers’ investment transactions in the first quarter of 

2020, when the major capital market shock occurred. The first part of the section focuses more on 

aggregate flows, and the second part more on country-level heterogeneity, which we analyse in the 

regressions in Section 4. 

3.1 

Aggregate descriptive statistics 

Compared with banks and investment funds, insurers’ portfolios were more stable during 

the shock in the first quarter of 2020. Chart 3 compares relative portfolio adjustments and 

absolute holdings across sectors and countries. Whereas banks increased their holdings by as 

much as 23% in Finland (around €8 billion) and by 14% in France (€101 billion), investment funds 

sold more than €190 billion bonds in response to outflows. A possible explanation for the relatively 
higher portfolio turnover21 of insurers in Finland is a rumour about a taxation regulation change, 

causing policyholders to exit and re-enter insurance contracts.  

Chart 3 

Bond transactions across financial sectors 

(percentages and EUR trillion, first quarter of 2020) 

Sources: ECB SHSS and authors’ calculations. 
Note: Net transactions, purchases and sales including matured bonds are expressed as a percentage of previous period 
holdings at market value.  

21   Turnover is defined as the sum of purchases and sales divided by the market value at the end of the previous period. More 
than 50% of Finnish insurers’ assets relate to statutory pension insurance; see the presentation by Teija Korpiaho, FIN-FSA 
(ESRB Insurance Expert Group, 02.05.2022). 

Occasional Paper Series No 22 
Descriptive statistics related to COVID-19 

12 

0.00.51.01.52.02.53.03.54.04.55.0-70-60-50-40-30-20-10010203040506070ATBEDEESFIFRIEITLUNLPTa) Banks0.00.51.01.52.02.53.03.54.04.55.0-70-60-50-40-30-20-10010203040506070ATBEDEESFIFRIEITLUNLPTc) Investmentfunds0.00.51.01.52.02.53.03.54.04.55.0-70-60-50-40-30-20-10010203040506070ATBEDEESFIFRIEITLUNLPTb) InsurersNet transactions (%)Purchases (%)Sales (%)Holdings -Q4 2019 (right-hand side)Holdings -Q1 2020 (right-hand side)  
 
 
European insurers purchased and sold securities worth more than €680 billion in the first 
quarter of 2020. These purchases and sales (gross transactions) in bonds22, listed equities and 

investment fund and MMF shares of €681 billion were 18% higher than the €579 billion in the first 

quarter of 2019 and 12% higher than the €610 billion in the first quarter of 2021. At the same time, 

purchases minus sales (net transactions) were only €55 billion, indicating portfolio rebalancing 
rather than strategic asset allocation changes.23  

To understand the portfolio adjustments, we investigate insurers’ bond transactions by 

bond market segment, issuer region and credit quality step. Government bonds accounted for 

45% of gross bond transactions, followed by financial sector bonds (38%) and corporate bonds 

(17%). Chart 4 shows that insurers predominantly traded EU government bonds, which are liquid 

and often have long durations. Regarding the issuer region (Chart 4a), insurers traded mostly 

domestic bonds (€137 billion) and other EU bonds (€162 billion), with an almost even split between 

government and financial sector bonds. Regarding the bonds’ credit quality (Chart 4b), insurers 

traded non-rated financial (€108 billion) and government bonds with the lowest investment grade 

rating CQS3 (€51 billion) the most.  

Chart 4 

Gross bond transactions of European insurers by segment 

(EUR billion, first quarter of 2020) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Gross bond transactions refer to purchases and sales including matured bonds. Gvmt, FC and NFC represent bonds 
issued by governments (S.13), financial sector corporations (S.12) and non-financial corporations (S.11) following the ESA 
(2010) definition. CQS refers to credit quality step and ranges from 0 (highest, i.e. AAA) to 6 (lowest, i.e. CCC/D); “NR” 
represents non-rated bonds. The sample refers to insurers in Austria, Belgium, Finland, France, Germany, Ireland, Italy, 
Luxembourg, the Netherlands, Portugal, and Spain. 

22   Here, the term “bonds” includes debt securities with all maturities and bonds with a call option. 

23   The same holds for the bond allocations with gross bond transactions of €388 billion but net sales of only €38 billion. 

Occasional Paper Series No 22 
Descriptive statistics related to COVID-19 

13 

domestic79other EU69UK, US, JP18RoW9domestic38other EU67UK, US, JP25RoW17domestic20other EU26UK, US, JP16Gvmt174NFC66FC148a) Further breakdown by domicile region of the issuerb) Further breakdown by credit qualityCQS 024CQS 142CQS 222CQS 351NR34CQS 08CQS 117CQS 240CQS 315NR66CQS 218CQS 324NR15Gvmt174NFC66FC148 
 
 
 
 
We find insurers to be active investors, as for example they reduced bonds in all credit 
quality categories except those without ratings.24 Also, insurers reduced relatively more bonds 
with lower credit quality (Chart 5a)25 and net purchased short-term and long-term bonds, but net 

sold medium-term bonds. This indicates a barbell investment strategy (Chart 5b), which combines 

the advantage of faster availability of cash with higher yields from long-term bonds and is more 

resilient when interest rates rise or fall sharply. 

Chart 5 

Bond transactions of European insurers by credit quality and residual maturity 

(percentages and EUR trillion, first quarter of 2020) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Net transactions, purchases and sales are expressed as a percentage of previous period holdings at market value. Sales 
include matured bonds. CQS refers to credit quality step and ranges from 0 (highest, i.e. AAA) to 6 (lowest, i.e. CCC/D); “NR” 
represents non-rated bonds. The sample refers to insurers in Austria, Belgium, Finland, France, Germany, Ireland, Italy, 
Luxembourg, the Netherlands, Portugal, and Spain. 

Furthermore, the comparison of insurers’ investment behaviour in the first quarter of 2020 

with other quarters points to a pronounced shock reaction. As illustrated in Chart 6, the 

second quarter of 2020 marked a reversal in the investment behaviour of insurers in France, 

Germany, Italy, and Spain compared with the first quarter of 2020. German insurers net purchased 

corporate bonds, but net sold financial sector bonds, mostly in the lowest investment grade 

category (€1.3 billion) and with high yields (€208 million). Overall, bond transaction volumes in the 

second quarter of 2020 were lower than in the first quarter of 2020 (€651 billion versus €681 

billion). When comparing the first quarter of 2020 with the first quarter of 2019 and the first quarter 

of 2021 to account for seasonality in insurance premia, the changes are confirmed. For example, 

insurers in France net purchased government bonds in the first quarter of 2019 and the first quarter 

2021 and net purchased financial sector bonds in the first quarter of 2019. Insurers in Ireland net 

24   Art. 176a (4) of Commission Delegated Regulation (EU) 2015/35 provides for a credit risk capital charge for bonds without 

ratings, which is mostly comparable to the lowest investment grade rating (CQS3). 

25  

In line with this, analysis with SII data suggests that insurers were net sellers of bonds with the lowest investment grade 
credit quality CQS3 in the first quarter of 2020 (EIOPA, 2021a, p. 67). 

Occasional Paper Series No 22 
Descriptive statistics related to COVID-19 

14 

0.00.51.01.52.02.53.0-50-45-40-35-30-25-20-15-10-5051015CQS0CQS1CQS2CQS3CQS4-6NRa) Breakdown by credit qualityNet transactions (%)Purchases (%)Sales (%)Holdings -Q4 2019 (right-hand side)Holdings -Q1 2020 (right-hand side)0.00.51.01.52.02.53.0-50-45-40-35-30-25-20-15-10-5051015Up to 1 year1 to 5 yearsabove 5 yearsb) Breakdown by residual maturity  
 
 
purchased investment funds in the first quarter of 2019 and the first quarter of 2021, while those in 

Spain net purchased government and financial sector bonds. The next subsection and the empirical 

part (Section 4) will shed more light on the country-level heterogeneity in insurers’ investments. 

Chart 6 

Net transactions of European insurers in the first quarter of 2020 compared with other 

quarters 

(EUR billion) 

Sources: ECB SHSS and authors’ calculations. 
Notes: For confidentiality reasons and for other quarters than the first quarter of 2020, information is aggregated for Italy (“equity 
and funds”), money market funds are not disclosed for the Netherlands and Portugal, and equity is not disclosed for Portugal. 
Gvmt., FC and NFC bonds represent bonds issued by governments (S.13), financial sector corporations (S.12) and non-
financial corporations (S.11) following the ESA (2010) definition. Net transactions are purchases minus sales including matured 
bonds as a percentage of previous period holdings at market value. 

Occasional Paper Series No 22 
Descriptive statistics related to COVID-19 

15 

-40-35-30-25-20-15-10-50510152025303540 AT BE DE ES FI FR IE IT LU NL PTBillionsa) Q4 2019Gvmt. bonds FC bonds NFC bonds Investment funds Equity Money market funds Equity and funds -40-35-30-25-20-15-10-50510152025303540ATBEDEESFIFRIEITLUNLPTb) Q1 2020-40-35-30-25-20-15-10-50510152025303540ATBEDEESFIFRIEITLUNLPTBillionsd) Q1 2019-40-35-30-25-20-15-10-50510152025303540ATBEDEESFIFRIEITLUNLPTe) Q1 2020-40-35-30-25-20-15-10-50510152025303540ATBEDEESFIFRIEITLUNLPTBillionsf) Q1 2021-40-35-30-25-20-15-10-50510152025303540ATBEDEESFIFRIEITLUNLPTBillionsc) Q2 2020Bonds  
 
 
 
3.2 

Country-level descriptive statistics 

One difference across national insurers is their liquidity buffers from matured bonds. 

Whereas European insurers typically invest more than two-thirds of their assets in bonds, the 

literature (e.g., Apicella, Gallo and Guazzarotti, 2022) broadly implicitly or explicitly excludes 

matured bonds in its analysis. This is typically the most appropriate approach, as matured bonds 

are not the result of active investment decisions. Given our focus in the descriptive statistics on 

showing the changes in the composition of insurers’ asset portfolios during the COVID-19 shock, 

we include matured bonds in sell decisions and conduct robustness tests excluding matured bonds 
from net transactions.26 The comparison of the two approaches shows possible liquidity buffers. 

Chart 7 illustrates how insurers in some Member States may have increased their liquid assets by 

not reinvesting returned principal from matured bonds in the first quarter of 2020. Possible 

explanations for these newly built cash reserves may be the differences in asset compositions 
across countries illustrated in Chart 2 or the rise in net claims and expenses (ESRB, 2021).27 

Without matured bonds, insurers in France, Italy, Luxembourg, Portugal and Spain, were net bond 
buyers, pointing to countercyclical investment behaviour in the shock period.28 

Chart 7 

Net transactions of European insurers with and without matured bonds 

(EUR billion, first quarter of 2020) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Net transactions are purchases minus sales including matured bonds (panel a) and excluding matured bonds (panel b). 
Gvmt., FC and NFC bonds represent bonds issued by governments (S.13), financial sector corporations (S.12) and non-
financial corporations (S.11) following the ESA (2010) definition. 

26   We find that, on average, 2% of insurers’ bond holdings matured in the first quarter of 2020. 

27   See Chart 4.3.C in ESRB (2021), p.113. On average, 2-3% of EEA insurers’ bond holdings mature each quarter; see 

EIOPA (2020c), p. 16. Unless otherwise stated, sales include matured bonds in our analysis. 

28   German insurers also net purchased more bonds in the first quarter of 2020 when considering matured bonds. French 
insurers have the largest direct bond holdings in Europe of approximately €1.5 trillion as of the fourth quarter of 2019. 

Occasional Paper Series No 22 
Descriptive statistics related to COVID-19 

16 

-40-30-20-100102030ATBEDEESFIFRIEITLUNLPTGvmt. bondsFC bondsNFC bondsInvestment fund sharesEquityMoney market fund sharesNet bond transactions a) With matured bonds-40-30-20-100102030 AT BE DE ES FI FR IE IT LU NL PTb) Without matured bonds 
 
 
Another difference across national insurers is their use of MMFs for liquidity management. 

By selling MMF shares, insurers generate liquidity; by purchasing them they invest liquidity from 
disposed assets.29 With a volume of €16 billion, insurers were the second largest MMF net buyers 
among financial sectors in Europe in the first quarter of 2020.30 Insurers in France (€7 billion), 

Ireland (€4 billion) and Italy (€4 billion) were the largest MMF net buyers (Chart 8), whereas 

insurers in Belgium increased MMFs by more than 30% despite small volumes. As for all analysis 

with country-level data, this does not preclude that an individual insurer behaved differently and that 

there might have been redemptions at the beginning and subscriptions at the end of the quarter. In 

addition to the diversified counterparty risk and the small return offered by non-public debt EU 

MMFs (ESRB, 2021), the trend towards MMF investments may also reflect low deposit rates.  

Chart 8 

MMF share transactions across financial sectors 

(percentages and EUR billion, first quarter of 2020) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Net transactions, purchases and sales are expressed as a percentage of previous period holdings at market value. Data 
from Dutch and German banks are excluded for confidentiality reasons. For comparison, y-axis scales are the same for all 
sectors. For Irish banks the value is 1,079% for net transactions and 1,123% for purchases. 

A third difference across national insurers is their trading intensity. We measure trading 

intensity by comparing the total transaction share across all asset classes with market shares of 

insurers in our sample countries. The points referring to the y-axis in Chart 9 depict the transaction-

to-market-share ratio in the first quarter of 2020. They show that among others, insurers in 

countries with large index-linked and unit-linked insurance markets tend to trade relatively more, 

and an explanation for this is that these products can typically be redeemed by policyholders 

29   See also Fache Rousová, Ghio, Kördel and Salakhova (2020). 

30  

Investment funds net purchased money market fund shares worth €25 billion in the first quarter of 2020. 

Occasional Paper Series No 22 
Descriptive statistics related to COVID-19 

17 

020406080100120140160180200-350-300-250-200-150-100-50050100150200250300350ATBEDEESFIFRIEITLUNLPTa) BanksNet transactions (%)Purchases (%)Sales (%)Holdings -Q4 2019 (right-hand side)Holdings -Q1 2020 (right-hand side)020406080100120140160180200-350-300-250-200-150-100-50050100150200250300350ATBEDEESFIFRIEITLUNLPTb) Insurers020406080100120140160180200-350-300-250-200-150-100-50050100150200250300350ATBEDEESFIFRIEITLUNLPTc) Investment funds 
 
 
without penalty.31 For robustness, we therefore exclude investment fund shares from transactions 

and index-linked and unit-linked insurance from market shares. Despite this adjustment, our 

findings that insurers in Ireland, Portugal and Italy traded the most are confirmed. 

Chart 9 

Gross transactions and trading intensity of European insurers 

(EUR billion, transaction-to-market-share ratios, first quarter of 2020) 

Sources: ECB SHSS; EIOPA insurance statistics, template S.06.02. solo quarterly; and authors’ calculations. 
Notes: Gross transactions are the sum of purchases and sales including matured bonds. Gvmt., FC and NFC bonds represent 
bonds issued by governments (S.13), financial sector corporations (S.12) and non-financial corporations (S.11) following the 
ESA (2010) definition. Trading intensity is the total gross transaction share of insurers in a country divided by their market share 
measured by total assets. Total assets from EIOPA insurance statistics are as of the fourth quarter of 2019. 

Additional descriptive statistics on insurers’ investment transactions in the first quarter of 

2020 are in the annex. These charts show analyses referring to other bond characteristics such as 

issuer region (Chart A1), issuer sector (Chart A2), residual maturity (Chart A3) and credit rating 

(Chart A4). We see a clear trend that insurers in almost all countries purchased very short-term and 

long-term debt with less than one and more than five years’ residual maturity, while net selling 

bonds with medium-term residual maturities. This confirms the barbell investment strategies 

observed at the aggregate level in Section 3.1. 

31  

In the fourth quarter of 2019, the share of index-linked and unit-linked products was 14% of total insurance assets in 
Austria, 13% in Belgium, 54% in Finland, 14% in France, 5% in Germany, 69% in Ireland, 19% in Italy, 68% in 
Luxembourg, 18% in the Netherlands, 24% in Portugal, and 7% in Spain; see EIOPA Insurance Statistics Asset 
Exposures template S.06.02. solo quarterly. Insurers in the larger index-linked and unit-linked markets net sold investment 
funds in the first quarter of 2020. 

Occasional Paper Series No 22 
Descriptive statistics related to COVID-19 

18 

0.00.51.01.52.02.5050100150200250ATBEDEESFIFRIEITLUNLPTBillionsGovmt. bondsFC bondsNFC bondsInvestment fund sharesEquityMoney market fund sharesTrading intensity (right-hand scale) 
 
 
4 

Regressions 

Having shown insurers’ shock reaction and country-level heterogeneity in investment behaviour in 

the descriptive statistics in Section 3, we next investigate insurers’ role as stabilisers of financial 

markets over a longer time horizon. To this end, we focus on debt securities holdings and replicate 

Timmer’s (2018) study on cyclical investment behaviour. The sample for the regression analysis 

and its coverage of SII data is set out in Section 2 and Chart 2.  

4.1 

Summary statistics 

To make our study comparable to Timmer (2018), we use the same summary statistics. As 

shown in Table 1, European insurers on average hold individual debt securities worth €68.4 million 

(Table 1a) and individual government debt securities worth €118.5 million (Table 1b). Insurers’ 

median bond holdings of €4.2 million and median government bond holdings of €7.0 million are a 

lot smaller, reflecting the country-level differences illustrated in Table 2.  

Despite insurers’ preference for government bond investments, there is wide variation in the 

average value of individual government bond holdings, ranging from €6.0 million in 

Luxembourg to €331.5 million in France. For insurers in Europe’s second-largest insurance 

market, Germany, we find average government bond holdings of €49.3 million. The highest number 

of buy-and-sell observations are in France, and the lowest are in Portugal (Table 2). In line with 

their larger individual holdings, insurers’ government bond transactions in Italy and France exceed 

those in other countries. Relevant for our investment behaviour study, the percentage changes in 

holdings are the largest in the Netherlands and Germany, although the securities with ISINs and 

quarterly changes in holdings included in our sample represent only a small portion of German 

insurers’ total bond assets (Chart 2). While insurers increase (decrease) their holdings by 12.5% 

(14.2%) on average (Table 1b), Dutch insurers increase (decrease) their holdings by 21.2% 

(24.6%) on average and German insurers by 16.8% (20.3%) on average (Table 2). By contrast, the 

changes in holdings are the smallest in Austria and Spain, with average increases and decreases 

of less than 7%.  

The standard deviation of the NetBuy variable suggests that for bonds with changes in 

holdings, German insurers transact relatively larger amounts on average than insurers in 

other countries. The standard deviation for changes in nominal government bond holdings NetBuy 

is 43.2% on average (Table 1b) and 50.1% for German insurers, compared with 17.0% for Austrian 

and 20.1% for Spanish insurers (Table 2). Given the higher NetBuy standard deviation of 67% for 

German insurers in Timmer (2018), their stable direct bond holdings (Chart 2) and the small 

absolute values of holdings with changes (median of €7.4 million in Germany versus €43.6 million 

in France; see Table 2), an explanation may be low levels of trading coupled with large and stable 

core bond portfolios.  

Occasional Paper Series No 22 
Regressions 

19 

 
 
 
Reflecting the low interest rates in the sample period, the average return for government 

bonds across holder countries is only 0.2%, with a standard deviation of 5.1% and small 
variations across countries.32 The highest average return of 0.5% with the highest standard 

deviation of 7.7% is earned by insurers in Belgium. 

Table 1 

Summary statistics of European insurers’ bond holdings 

(EUR million and percentages, first quarter of 2017 to second quarter of 2022) 

Sources: ECB SHSS and authors’ calculations.  
Notes: All bonds represent bonds issued by governments (S.13), financial sector corporations (S.12) and non-financial 
corporations (S.11) following the ESA (2010) definition. “Std” refers to the standard deviation. “Holdings” is the nominal value of 
government bonds held by insurers. “Buy” and “Sell” refer to the amount bought and sold in EUR million. “NetBuy” is the change 
in the log of the nominal amount held. “Buy%” and “Sell%” are the changes in the log of the nominal amount held if positive or 
negative, respectively. “Return” is the holding period return defined as the quarterly change in the price plus the quarterly 
coupon divided by the price in the previous quarter. The sample refers to insurers in Austria, Belgium, France, Germany, 
Ireland, Italy, Luxembourg, the Netherlands, Portugal, and Spain. 

32   Reflecting the higher credit risk, Table 1 suggests that the average return for the sample including government, FC and 

NFC bonds is higher at 0.4% and has a higher standard deviation of 5.8%.  

Occasional Paper Series No 22 
Regressions 

20 

All bonds (Gvmt., FC, NFC bonds)    Holdings Buy Sell NetBuy Buy% Sell% Return Mean 68.38 2.96 3.24 -0.005 0.117 0.132 0.004 Median 4.20 0.06 0.08 0.000 0.018 0.020 0.002 Std 460.27 23.71 23.80 0.406 0.371 0.402 0.058 Observations 458,758 234,166 223,592 458,758 234,166 224,592 458,758 Gvmt. bonds          Holdings Buy Sell NetBuy Buy% Sell% Return Mean 118.47 5.17 5.75 -0.002 0.125 0.142 0.002 Median 7.01 0.10 0.12 0.000 0.019 0.021 0.001 Std 671.24 33.63 31.79 0.432 0.391 0.432 0.051 Observations 208,584 109,355 99,229 208,584 109,355 99,229 208,584   
 
 
 
 
Table 2 

Summary statistics of European insurers’ government bond holdings by country 

(EUR million and percentages, first quarter of 2017 to second quarter of 2022) 

Occasional Paper Series No 22 
Regressions 

21 

AT         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 26.48 0.55 1.31 -0.010 0.024 0.057 0.000 Median 10.67 0.07 0.12 0.000 0.007 0.013 0.000 Std 42.41 2.93 4.07 0.17 0.121 0.214 0.044 Observations 12,520 7,805 4,715 12,520 7,805 4,715 12,520 BE         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 92.53 3.23 2.58 -0.011 0.153 0.180 0.005 Median 0.68 0.03 0.05 0.000 0.037 0.049 0.004 Std 494.35 21.58 12.35 0.452 0.406 0.434 0.077 Observations 14,405 7,338 7,067 14,405 7,338 7,067 14,405 DE         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 49.29 6.28 7.55 -0.002 0.168 0.203 0.004 Median 7.37 0.18 0.40 0.005 0.032 0.039 0.005 Std 140.97 29.30 22.64 0.401 0.450 0.483 0.043 Observations 31,014 16,848 14,166 31,014 16,848 14,166 31,014 ES         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 146.72 4.30 3.94 0.008 0.063 0.053 0.002 Median 13.40 0.09 0.07 0.000 0.005 0.004 0.001 Std 602.06 21.43 20.62 0.209 0.212 0.188 0.043 Observations 17,680 9,338 8,342 17,680 9,338 8,342 17,680 FR         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 331.50 10.20 10.21 0.003 0.086 0.080 0.002 Median 43.60 0.22 0.22 0.000 0.008 0.008 0.001 Std 1371,64 56.53 53.76 0.350 0.337 0.336 0.050 Observations 31,182 15,629 15,553 31,182 15,629 15,553 31,182   
 
 
 
Sources: ECB SHSS and authors’ calculations.  
Notes: Bonds represent bonds issued by governments (S.13) following the ESA (2010) definition. “Std” refers to the standard 
deviation. “Holdings” is the nominal value of government bonds held by insurers. “Buy” and “Sell” refer to the amount bought 
and sold in EUR million. “Net Buy” is the change in the log of the nominal amount held. “Buy%” and “Sell%” are the changes in 
the log of the nominal amount held if positive or negative, respectively. “Return” is the holding period return defined as the 
quarterly change in the price plus the quarterly coupon divided by the price in the previous quarter. 

Chart 10 illustrates the average changes in nominal government bond holdings from one 

quarter to the next. Higher mean changes in holdings of up to 25% for German, Dutch and 
Belgian insurers point to more active investment strategies.33 At the onset of the COVID-19 

pandemic in the first quarter of 2020, the drop in mean changes in Ireland and the Netherlands 

indicates net selling. Meanwhile, the rising interest rate environment since 2022 appears to be 

associated with government bond net purchases in five countries (Belgium, Ireland, Italy, 

33   The portion of bonds with holding changes and ISINs for German insurers, however, is small and approximately 12% of 

their direct bond holdings as of the fourth quarter of 2019 (Chart 2).  

Occasional Paper Series No 22 
Regressions 

22 

IE         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 18.98 2.49 2.37 0.000 0.150 0.160 0.002 Median 4.33 0.07 0.10 0.000 0.024 0.025 0.001 Std 37.11 11.20 7.79 0.479 0.435 0.470 0.044 Observations 29,981 15,479 14,502 29,981 15,479 14,502 29,981 IT         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 258.74 10.51 12.27 0.006 0.113 0.127 0.001 Median 17.02 0.22 0.35 0.002 0.013 0.020 0.001 Std 910.93 57.07 54.77 0.439 0.361 0.490 0.056 Observations 24,785 13,792 10,993 24,785 13,792 10,993 24,785 LU         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 6.00 0.64 0.40 -0.002 0.158 0.164 0.003 Median 1.23 0.02 0.02 0.000 0.021 0.021 0.002 Std 14.38 2.5 2.01 0.482 0.439 0.470 0.056 Observations 25,521 12,852 12,669 25,521 12,852 12,669 25,521 NL         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 95.83 5.37 8.75 -0.039 0.212 0.246 0.003 Median 3.89 0.20 0.40 -0.007 0.040 0.043 0.002 Std 300.92 19.67 29.98 0.453 0.503 0.510 0.036 Observations 12,126 5,470 6,656 12,126 5,470 12,126 12,126 PT         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 30.96 2.65 3.23 -0.007 0.111 0.131 0.000 Median 3.65 0.02 0.03 0.000 0.006 0.011 0.000 Std 131.13 14.95 20.31 0.453 0.437 0.436 0.036 Observations 9,370 4,804 4,566 9,370 4,804 4,566 9,370   
 
 
Luxembourg, and Portugal), confirming the findings of Fache Rousová and Giuzio (2019b) as 

regards insurers’ investment behaviour in response to risk-free rates. This trend is confirmed in the 

aggregate changes in bond holdings in Chart A6 in the annex. 

Occasional Paper Series No 22 
Regressions 

23 

 
Chart 10 

Mean changes in nominal government bond holdings of European insurers by country 

(percentages of previous period holdings, first quarter of 2017 to second quarter of 2022) 

Occasional Paper Series No 22 
Regressions 

24 

-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022a) AT-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022b) BE-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022c) DE-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022d) ES-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022e) FR-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022f) IE 
 
Sources: ECB SHSS and authors’ calculations. 
Notes: Government bonds are as defined by ESA (2010). Changes are the change in the log of the nominal amount held. AT: 
n=12,520; BE: n=14,405; DE: n=31,014; ES: n=17,680; FR: n=31,182; IE: n=29,981; IT: n=24,785; LU: n=25,521; NL: 
n=12,126; PT: n=9,370. 

4.2 

Main results 

We attempt to shed light on the question of whether insurers under SII act as stabilisers of 

financial markets and whether their investment behaviour differs across countries. Our 

regressions are in the spirit of Timmer (2018), and we investigate how investment decisions relate 

to past securities returns. The lack of information about the timing of transactions and returns within 

a quarter impedes the assessment of cyclical investment behaviour. To rule out the possibility that 

transactions are executed before returns are observed and that these returns reflect the price 

impact of insurers’ transactions, we regress changes in nominal holdings on the lagged securities’ 

returns. 

Occasional Paper Series No 22 
Regressions 

25 

-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022g) IT-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022h) LU-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022i) NL-25-20-15-10-50510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022j) PT 
 
 
 
Instead of comparing insurers with other financial sectors we compare insurers across 

European countries. To this end, we estimate FE panel data models with two autoregressive 
lags.34 Whereas Timmer (2018) defines dummy variables for sectors, we estimate an interaction 

term of insurers’ country of incorporation (“holder country”, hc) and the lagged return of individual 

debt securities s held by insurers in a country Returns,hc,t-1. Austria is thereby our base case, and 

the investment behaviour of Austrian insurers is captured by the return coefficient. More precisely, 

we regress the percentage change in the nominal amount held by insurers in a country 
𝑁𝑒𝑡𝐵𝑢𝑦𝑠,ℎ𝑐,𝑡 on the interaction of the holder country hc and the lagged security return 
𝑅𝑒𝑡𝑢𝑟𝑛𝑠,ℎ𝑐,𝑡−1 by estimating the following model: 

𝑁𝑒𝑡𝐵𝑢𝑦𝑠,ℎ𝑐,𝑡 = 𝛽1𝑅𝑒𝑡𝑢𝑟𝑛𝑠,ℎ𝑐,𝑡−1 + 𝛽2𝑅𝑒𝑡𝑢𝑟𝑛𝑠,ℎ𝑐,𝑡−1 ∗ ℎ𝑐𝑡 + 𝛼𝑠,𝑡 + 𝛼ℎ𝑐,𝑡 + 𝜖𝑠,ℎ𝑐,𝑡 

(2) 

While the dependent variable NetBuy is the change in log nominal holdings, Returnt-1 refers to the 

lagged security return (see also Section 2). We control for different combinations of security and 

holder country FE, αs,t and αhc,t, which can be constant or time-variant and affect insurers’ 

investment behaviour through rating downgrades or the size and investment strategy of insurers in 

a country, for example. To investigate home bias, we also conduct separate regressions for a sub-

sample of domestic securities, which we define as securities with the same issuer and holder 

country. 

As illustrated in equation (3) and with a dummy variable for the first and second quarters of 

2020, we investigate insurers’ reaction to the COVID-19 shock in the regressions. 
Accordingly, we estimate a triple interaction term 𝐶𝑂𝑉𝐼𝐷 ∗ 𝑅𝑒𝑡𝑢𝑟𝑛𝑠,ℎ𝑐,𝑡−1 ∗ ℎ𝑐𝑡 to investigate whether 
insurers in a country exhibit procyclical or countercyclical investment behaviour in the shock.  

𝑁𝑒𝑡𝐵𝑢𝑦𝑠,ℎ𝑐,𝑡 = 𝛽1𝑅𝑒𝑡𝑢𝑟𝑛𝑠,ℎ𝑐,𝑡−1 + 𝛽2𝐶𝑂𝑉𝐼𝐷 ∗ 𝑅𝑒𝑡𝑢𝑟𝑛𝑠,ℎ𝑐,𝑡−1 ∗ ℎ𝑐𝑡 + 𝛼𝑠,𝑡 + 𝛼ℎ𝑐,𝑡 + 𝜖𝑠,ℎ𝑐,𝑡 

(3) 

To account for the stable characteristics of European insurance markets, we test FE models. 

To consistently estimate regressions, variation in the explanatory variables is needed. The 

characteristics of the national European insurance markets, however, are relatively stable. For 

example, the share of index-linked and unit-linked insurance is higher than 70% of total insurance 

assets in Ireland and Luxembourg, but this changes by less than 1% in 95% of all observations. As 

separate country-level control variables can therefore lead to over-specified models, we choose a 

parsimonious regression approach with an FE model. To capture the differences across countries 

and given our specific research question about cyclical investment behaviour across European 

countries, our unit of observation are individual securities held by insurers in a holder country. 

Different from ordinary least squares (OLS) regressions, FE panel data models control for time-

invariant characteristics as they consider deviations from the mean for individual units only. Models 

capturing variation within rather than between holder countries over time appear better suited to 

this analysis, and OLS models can therefore be considered inferior. Without information on 

individual insurers, however, no further insights on country-level heterogeneity can be provided, as 

all explanatory variables, for example solvency ratios, are captured by the country-level FE. 

Following Timmer (2018), our focus is therefore on variation at the security level. 

34   The Durbin-Watson test statistics show that insurers’ transactions tend to be autocorrelated; for simplicity the two lags of 

the dependent variable included as explanatory variables are not shown in the regression equations. 

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26 

 
 
As buying (selling) securities whose price has risen (fallen) is associated with procyclical 

investment behaviour, we interpret positive coefficients as a signal of procyclical behaviour 

and negative coefficients as a signal of countercyclical behaviour. The regression results refer 

to changes in holdings in response to changes in returns. To ensure the results’ robustness and for 

each model equation (2) and (3), we estimate several specifications controlling for different 

combinations of FE. In column (1) in the regression tables we consider constant security FE, in 

column (2) constant security and time FE, in column (3) time-variant holder country FE, in column 

(4) constant security and time-variant holder country FE, and in column (5) constant and time-

variant security and holder country FE. Column (6) replicates column (4) with log returns to account 
for non-normal return distributions.35 Constant holder country FE are considered in all models when 

estimating interaction effects with countries. Time FE account for events which are market-wide. 

Security FE are characteristics such as the bond’s credit rating, maturity, or coupon. Holder country 

FE are characteristics which may relate to the size of insurers’ investments, to national investment 

strategies or to national investment regulations, for example. While these characteristics are 

typically constant, certain events such as natural catastrophes may affect certain countries at a 

specific moment in time. Our idea is to control in different model specifications for an increasing 

number of factors which can affect insurers’ investment behaviour. Security returns may be 

interpreted as time-variant security characteristics, which is why the level of returns cannot be 
identified in the estimations with time-variant security FE in column (5).36 The coefficient on the 

interaction term in column (5) therefore shows how much more procyclical or countercyclical 

insurers in a country invest in comparison with insurers in the base country of Austria. Given our 

unit of observation, individual securities held by insurers in a country, preferences for certain 

securities by insurers in a country are also captured by the FE model. To account for the fact that 

transactions by insurers in a country can be correlated over time, we use clustered standard errors 

at the security and holder country level. 

Tables 3 and 4 show that insurers in the Netherlands invest countercyclically overall, as the 

negative coefficients indicate a decrease in holdings when past returns are positive. Column 

(5) in Table 3 indicates that a return of 10% in the last quarter is associated with a 4.1 percentage 

point larger decrease in nominal holdings compared with Austrian insurers. This countercyclical 

behaviour is confirmed in all model specifications and is more pronounced for government bonds 
(Table 4), which account for 28.9% of Dutch insurers’ investments.37 Differences between our 

results for the Netherlands and those of Duijm and Steins Bisschop (2018) can be explained by a 

different pre-SII sample and by a different method, as the authors assess return differentials 

between equities and bonds. We further find countercyclical investment behaviour for the 

Netherlands’ neighbouring country of Belgium. Column (5) in Table 4 indicates that a return of 10% 

in the last quarter is associated with a 3.0 percentage point larger decrease in nominal government 

bond holdings compared with Austrian insurers. This behaviour is confirmed when including 

corporate bonds in the sample, as shown in column (2) in Table 3 (see also Table 8 in Section 4.3 

for a focus on non-financial corporate bonds). 

35   Harvey and Siddique (2000) show that asset returns are non-normal as they have systematic skewness. See also Kraus 

and Litzenberger (1976). 

36   To account for time-variant security FE, insurers in at least two countries must trade the same security in a quarter (see 

also Timmer, 2018). 

37   See EIOPA Insurance Statistics, Solvency II reporting template S.06.02. solo quarterly, fourth quarter of 2019. 

Occasional Paper Series No 22 
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Table 3 

Heterogeneity in cyclical investment behaviour – interactions (all bonds) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Standard errors are in parentheses (∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01). Securities are bonds as defined by ESA (2010) 
(government sector code starting with S.13, financial sector code with S.12 and non-financial sector code with S.11). “Net Buy” 
is the change in the log of the nominal amount of security s held at the end of the quarter t by insurers in holder country hc. 
“Return” is the quarterly change in the price plus the coupon divided by the price of the security in the past quarter. The 
regression includes control variables for security s interacted with time t and for holder country hc interacted with time t. To 
account for heteroscedasticity and autocorrelation of the residuals, two lagged terms of the dependent variable are included as 
regressors, and standard errors are clustered at the security and holder country level. The estimations are FE panel data 
models spanning security, holder country and time FE. Significant FE are indicated by “Yes”.  

Occasional Paper Series No 22 
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Dependent Variable: NetBuy  (1) (2) (3) (4) (5) (6)   Base Base Base Base Base Log returns  Return 0.0231 0.0308 0.0478 0.0107  -0.0099   (0.035) (0.037) (0.036) (0.044)  (0.031)  Return*BE -0.1328*** -0.1235** -0.0779 -0.0537 -0.0649 -0.0345   (0.048) (0.048) (0.049) (0.057) (0.155) (0.044)  Return*DE -0.2569*** -0.2243*** 0.1428** 0.1928*** 0.3913** 0.1438**   (0.056) (0.063) (0.060) (0.074) (0.165) (0.063)  Return*ES 0.1253 0.1182 0.1416 0.1489 0.0884 0.2673**   (0.087) (0.087) (0.105) (0.110) (0.137) (0.124)  Return*FR 0.1030** 0.1053** 0.0189 0.0792 0.1163 0.0800**   (0.068) (0.045) (0.046) (0.054) (0.136) (0.042)  Return*IE -0.0149 0.000 -0.0329 0.0187 0.2178 -0.2591   (0.117) (0.115) (0.112) (0.130) (0.145) (0.158)  Return*IT 0.0857 0.1019* 0.1244 0.1845** 0.2973** 0.2584***   (0.061) (0.060) (0.075) (0.074) (0.148) (0.073)  Return*LU 0.0738 0.0795 0.0455 0.1034 0.0426 0.0989*   (0.061) (0.061) (0.063) (0.069) (0.143) (0.053)  Return*NL -0.1410** -0.1233* -0.4011*** -0.3916*** -0.4108*** -0.3039***   (0.066) (0.065) (0.075) (0.081) (0.163) (0.065)  Return*PT -0.1010 -0.1038 -0.1921* -0.1923* -0.1949 -0.1318   (0.088) (0.088) (0.099) (0.109) (0.172) (0.112)  Security FE Yes Yes No Yes - Yes  Time FE No Yes - - - -  Sec.*Time FE No No No No Yes No  HC*Time FE No No Yes Yes Yes Yes  R2 0.0748 0.0764 0.0163 0.0820 0.3114 0.0834  Observations 340,407 340,407 342,249 340,407 254,456 340,407    
 
 
 
The specific pattern in German insurers’ net transactions in Chart 10 shows why controls 

for time-varying country FE are important in cross-country studies. FE models control for 

unobserved variables, i.e. specific events in a country and period, by demeaning the dependent 

variables, in our case net transactions. Through this demeaning only differences between net 

transactions and their averages for a country and period are analysed. When these averages 

change, controls for time-varying country FE can lead to different assessments as shown in 

columns (1), (2) and (3) to (6) for Germany in Table 3. As the net transactions in Chart 10 do not 
reflect data errors and their volatility is lower than in Timmer (2018), we do not winsorise them.38 

While controlling for time FE may lead to some over-controlling, the coefficient in column (2) for 

Germany in the single country estimations in Table 5 can be interpreted as a robustness test. When 

comparing our estimations with Timmer (2018) and following the author’s definition of cyclicality, we 

find that German insurers invest less countercyclically. We find them to increase their bond 

holdings by 1.9 percentage points after a 10% return in the last quarter in Table 3 column (4), 

whereas Timmer (2018) finds that they decrease their bond holdings by 6.7 percentage points in 
their pre-SII sample.39 

In specific countries and situations, insurers invest procyclically by selling securities 

following periods of negative returns. By considering country FE (Tables 3 and 4) and 

conducting separate regressions for each holder country (Table 5), we find that Italian insurers 

invest procyclically overall and that this trend is stronger for government bonds. To a lesser extent, 
we note the same trend for French insurers.40 The opposite behaviour, however, is observed in Italy 

for domestic government bonds, which account for more than 70% of Italian insurers’ government 

bond holdings (Chart 1b). Following column (3) in Table A4 in the annex, Italian insurers increase 

their domestic government bond holdings by 3.7 percentage points following a 10% decline in 

returns in the last quarter. This confirms the findings of Apicella, Gallo and Guazarotti (2022) that 

Italian insurers’ investment strategies are stabilising overall.  

38   Possible explanations for larger relative net transactions are transfers to and from funds or partial order executions due to 

lower liquidity. German insurers invest approximately a third of their total assets via investment funds (see public EIOPA 
insurance statistics). 

39   These results refer to column (1) in Table 2 in Timmer (2018). 

40   Chart A5 shows that Italian and French insurers are among the largest European direct government bond holders. 

Occasional Paper Series No 22 
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Table 4 

Heterogeneity in cyclical investment behaviour – interactions (government bonds) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Standard errors are in parentheses (∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01). Securities are bonds issued by the government 
sector as defined by ESA (2010) (government sector code starting with S.13). “Net Buy” is the change in the log of the nominal 
amount of security s held at the end of the quarter t by insurers in holder country hc. “Return” is the quarterly change in the price 
plus the coupon divided by the price of the security in the past quarter. The regression includes control variables for security s 
interacted with time t and for holder country hc interacted with time t. To account for heteroscedasticity and autocorrelation of 
the residuals, two lagged terms of the dependent variable are included as regressors, and standard errors are clustered at the 
security and holder country level. The estimations are FE panel data models spanning security, holder country and time FE. 
Significant FE are indicated by “Yes”. 

Occasional Paper Series No 22 
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Dependent Variable: NetBuy  (1) (2) (3) (4) (5) (6)   Base Base Base Base Base Log returns  Return 0.1187*** 0.1354*** 0.0978 0.0931**  0.0252   (0.032) (0.038) (0.060) (0.046)  (0.023)  Return*BE -0.2068*** -0.2114*** -0.1443** -0.1429** -0.3048** -0.0800**   (0.044) (0.048) (0.070) (0.058) (0.148) (0.038)  Return*DE -0.4470*** -0.3958*** 0.1591* 0.1224 0.3451** 0.1513**   (0.055) (0.055) (0.086) (0.077) (0.172) (0.060)  Return*ES -0.0146 -0.0249 -0.0350 -0.0433 0.0550 0.0440   (0.046) (0.048) (0.071) (0.062) (0.126) (0.050)  Return*FR 0.1642** 0.1632*** 0.0562 0.0951 0.1907 0.1850***   (0.055) (0.055) (0.084) (0.070) (0.122) (0.059)  Return*IE 0.0406 0.0585 0.0536 0.0505 0.1022 0.0723   (0.084) (0.083) (0.107) (0.108) (0.133) (0.079)  Return*IT -0.0097 0.0083 0.0853 0.1715** 0.3207*** 0.2806***   (0.062) (0.062) (0.099) (0.082) (0.118) (0.085)  Return*LU -0.1342 -0.1346 -0.1289 -0.1024 -0.2326 0.0042   (0.090) (0.090) (0.107) (0.101) (0.144) (0.087)  Return*NL -0.2001*** -0.1882*** -0.4207*** -0.4468*** -0.5401*** -0.3201***   (0.069) (0.071) (0.094) (0.087) (0.153) (0.066)  Return*PT -0.1376 -0.1428 -0.1555 -0.2022 -0.1425 -0.1175   (0.121) (0.121) (0.183) (0.182) (0.212) (0.182)  Security FE Yes Yes No Yes - Yes  Time FE No Yes - - - -  Sec.*Time FE No No No No Yes No  HC*Time FE No No Yes Yes Yes Yes  R2 0.0521 0.0540 0.0169 0.0619 0.2832 0.0619  Observations 167,873 167,873 168,201 167,873 134,971 167,873    
 
 
 
Table 5 

Heterogeneity in cyclical investment behaviour – country-level regressions (government 

bonds) 

Sources: ECB SHSS and authors’ calculations.  
Notes: Standard errors are in parentheses (∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01). Securities are bonds issued by the government 
sector as defined by ESA (2010) (government sector code starting with S.13). “NetBuy” is the change in the log of the nominal 
amount of government bond security s held at the end of quarter t by insurers in holder country hc. “Return” is the quarterly 
change of the price plus the coupon divided by the price of the security in the past quarter. The regression includes control 
variables for security s interacted with time t. To account for heteroscedasticity and autocorrelation of the residuals, two lagged 
terms of the dependent variable are included as regressors, and standard errors are clustered at the security level. The 
estimations are FE panel data models spanning security and time FE. Significant FE are indicated by “Yes”.  

Occasional Paper Series No 22 
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Dependent Variable: NetBuy  AT BE DE  (1) (2) (1) (2) (1) (2) Return 0.0806** 0.0449 -0.0692** -0.482 -0.3500*** -0.1098*  (0.037) (0.053) (0.030) (0.036) (0.045) (0.058) Security FE Yes Yes Yes Yes Yes Yes Time FE No Yes No Yes No Yes R2 0.1305 0.1353 0.2215 0.2317 0.1726 0.1940 Obs. 10,482 10,482 10,886 10,886 23,335 23,335 Dependent Variable: NetBuy  ES FR IE  (1) (2) (1) (2) (1) (2) Return 0.0495 0.0059 0.2157*** 0.0946* 0.2192** 0.1756  (0.032) (0.042) (0.046) (0.054) (0.089) (0.112) Security FE Yes Yes Yes Yes Yes Yes Time FE No Yes No Yes No Yes R2 0.1311 0.1380 0.1457 0.1485 0.1181 0.1223 Obs. 14,823 14,823 26,363 26,363 23,619 23,619 Dependent Variable: NetBuy  IT LU NL  (1) (2) (1) (2) (1) (2) Return 0.1130** 0.2140*** 0.0017 0.0044 -0.4402** -0.4032***  (0.055) (0.072) (0.089) (0.096) (0.063) (0.075) Security FE Yes Yes Yes Yes Yes Yes Time FE No Yes No Yes No Yes R2 0.1506 0.1547 0.1067 0.1138 0.1621 0.1779 Obs. 20,294 20,294 20,582 20,582 8,705 8,705  Dependent Variable: NetBuy  PT  (1) (2) Return 0.0289 0.1189  (0.111) (0.177) Security FE Yes Yes Time FE No Yes R2 0.1289 0.1345 Obs. 7,733 7,733   
 
Table 6 

Heterogeneity in cyclical investment behaviour in the first and second quarters of 2020 - 

interactions (government bonds) 

Sources: ECB SHSS and authors’ calculations.  
Notes: Standard errors are in parentheses (∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01). Securities are bonds issued by the government 
sector as defined by ESA (2010) (government sector code starting with S.13). “NetBuy” is the change in the log of the nominal 
amount of government bond security s held at the end of quarter t by insurers in holder country hc. “Return” is the quarterly 
change of the price plus the coupon divided by the price of the security in the past quarter. The regression includes control 
variables for security s interacted with time t and for holder country hc interacted with time t. To account for heteroscedasticity 
and autocorrelation of the residuals, two lagged terms of the dependent variable are included as regressors, and standard 
errors are clustered at the security level. The estimations are FE panel data models spanning security, holder country and time 
FE. Significant FE are indicated by “Yes”.  

Occasional Paper Series No 22 
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Dependent Variable: NetBuy  (1) (2) (3) (4) (5) (6)   Base Base Base Base Base Log returns  Covid*Return -0.0726 -0.1108 -0.0299 0.0150  0.0800   (0.079) (0.089) (0.112) (0.105)  (0.091)  Covid*Return*BE 0.0651 0.0654 -0.1676 -0.1739 0.7549* -0.2204**   (0.105) (0.115) (0.139) (0.134) (0.452) (0.110)  Covid*Return*DE 0.9442*** 0.8924*** 0.2042 0.2310 1.0062** 0.0502   (0.152) (0.153) (0.187) (0.188) (0.477) (0.157)  Covid*Return*ES 0.3367* 0.3483 0.1470 0.1939 0.1477 0.0993   (0.192) (0.192) (0.225) (0.223) (0.379) (0.203)  Covid*Return*FR 0.0128 0.0181 -0.0607 -0.0416 0.4865 -0.0649   (0.152) (0.151) (0.188) (0.184) (0.385) (0.165)  Covid*Return*IE -0.0025 -0.0021 -0.1918 -0.2036 0.5417 -0.1505   (0.207) (0.208) (0.221) (0.227) (0.433) (0.185)  Covid*Return*IT 0.0730 0.0557 -0.2753 -0.3164 0.2396 -0.3959**   (0.126) (0.127) (0.176) (0.166) (0.391) (0.158)  Covid*Return*LU 0.2798 0.2801 0.2613 0.1825 0.8810** 0.0693   (0.176) (0.180) (0.206) (0.193) (0.441) (0.173)  Covid*Return*NL 0.0780 0.0551 -0.0157 0.0416 1.0252** 0.0341   (0.158) (0.166) (0.184) (0.186) (0.450) (0.151)  Covid*Return*PT -0.4095 -0.4107 -0.4048 -0.3059 -0.3622 -0.4161   (0.366) (0.366) (0.471) (0.466) (0.590) (0.470)  Security FE Yes Yes No Yes - Yes  Time FE No Yes - - - -  Sec.*Time FE No No No No Yes No  HC*Time FE No No Yes Yes Yes Yes  R2 0.0529 0.0545 0.0169 0.0620 0.2833 0.0620  Observations 167,873 167,873 168,201 167,873 134,971 167,873    
 
 
 
During the COVID-19 shock, European insurers in a few Member States invested 

procyclically following the definition of Timmer (2018). The positive coefficients for 𝐶𝑂𝑉𝐼𝐷 ∗
𝑅𝑒𝑡𝑢𝑟𝑛𝑠,ℎ𝑐,𝑡−1 ∗ ℎ𝑐𝑡 in the most complete model in column (5) of Table 6 for Germany and the 
Netherlands show the shock reaction and correspond to the observed behaviour in Chart 6 in 

Section 3. Whereas Chart 6 shows insurers’ net transactions in the first and second quarters of 

2020, the regressions consider a lagged reaction to securities’ returns. The findings for the 

Netherlands confirm insurers’ reaction to shocks in the literature (e.g., Bijlsma and Vermeulen, 

2016) and observed in Chart 10. The findings for Germany must be viewed in the context of the 

relatively small number of government bonds with ISINs and changes in holdings as shown in Chart 

2. The positive and significant interaction terms show that, in the first and second quarters of 2020, 

German and Dutch insurers reduced their government bond holdings by over 10 percentage points 

more than Austrian insurers following a negative 10% return in the last quarter. There are also 

signs that insurers in Luxembourg and Belgium invested procyclically in government bonds during 

the COVID-19 shock. 

The scatterplot showing the actual versus predicted values of the regressions with all bonds 

confirms our findings on trading intensity in the descriptive statistics in Section 3.2. Chart 

11 relates lagged securities’ returns (x-axis) to changes in nominal bond holdings (y-axis). The 

range of returns of securities held by insurers in Ireland and Luxembourg is larger than in other 

countries, pointing to riskier bond investments. The observations for changes in nominal holdings 

also spread a lot more in Ireland and Luxembourg, indicating higher trading intensity as illustrated 

in Chart 9. Conversely, the relatively small changes in nominal holdings in Austria and Spain are in 

line with the below-average standard deviations in the NetBuy variable in Table 2 and the overall 

insignificant regression results for these countries. 

Occasional Paper Series No 22 
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Chart 11 

Scatterplot of the regression explaining insurers’ investment behaviour with previous period 

returns  

(returns (x-axis) and NetBuy (y-axis); first quarter of 2017 to second quarter of 2022) 

Sources: ECB SHSS and authors’ calculations.  
Notes: ”NetBuy” refers to the change in the log of the nominal amount of bond security s held at the end of quarter t by insurers 
in Austria, Belgium, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, Portugal, and Spain. Securities are bonds 
as defined by ESA (2010) (government sector code starting with S.13, financial sector code with S.12 and non-financial sector 
code with S.11); n=458,758. “Return” is the quarterly change of the price plus the coupon divided by the price of the security in 
the past quarter. The regression to compute linear predictions includes control variables for security s interacted with time t and 
for holder country hc interacted with time t and is an FE panel data model. To account for heteroscedasticity and autocorrelation 
of the residuals, two lagged terms of the dependent variable are included as regressors.  

To identify the reasons for the different investment behaviour of insurers in different 

countries, firm-level data are needed. Similar research indicates that insurers’ capitalisations 

matter for their investment behaviour. At the onset of the COVID-19 pandemic, a small percentage 

of German life insurers that would have been unable to fulfil their own fund requirements without 
transitional LTG measures41 acted procyclically by being net sellers of risky bonds in the first 

quarter of 2020, whereas well-capitalised life insurers acted countercyclically (Deutsche 

Bundesbank, 2021). In Italy, less capitalised insurers on average reduced their exposure to 

securities whose prices had fallen in the first half of 2020 (Apicella, Gallo and Guazzarotti, 2022). 

41   The transitional measures on the risk-free rate and on technical provisions provide capital relief for insurers by allowing a 

phasing-in of the valuation differences between Solvency I and Solvency II until 2032; see also ESRB (2018). 

Occasional Paper Series No 22 
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34 

 
 
 
 
 
 
 
To gain insights into how investment behaviour and solvency could be related, we connect 

SII and SHSS data. Given the lack of firm-level data, Chart 12 is explorative by nature. It relates 

the average solvency ratios of the 10th percentile of least capitalised insurers to purchases and 

sales of bonds by insurers. The use of the LTG capital support measures is very heterogeneous 

across countries, and their impact on average solvency ratios ranges from as low as 1 and 2 

percentage points in Ireland and Luxembourg to as much 40 and 47 percentage points in Portugal 

and Germany as of the fourth quarter of 2019 (EIOPA, 2020b). One reason for these differences 

may be the heterogeneous insurance product offerings and their respective capital requirements. 

The points in dark red in Chart 12 indicate countries where the impact of LTG measures on 

average solvency ratios is larger than 39 percentage points, which corresponds to the 3rd and 4th 

quartile across countries as of the fourth quarter of 2019.  

In regressions, however, country-level data on LTG measures have no additional 

explanatory power as their impact is captured by country FE in our model. As for other stable 

characteristics of national insurance markets, the overall usage of LTG measures and their impact 

on average solvency ratios changes only slightly across countries. Variation in the data, however, is 

a prerequisite for regressions. In addition to the lack of variation in aggregate country-level data on 

LTG measures, an additional caveat is their annual frequency.  

In the absence of firm-level data, distributional country-level data on solvency ratios are a 

proxy for vulnerable insurers. Therefore, we select the average solvency ratios of the 10th 

percentile of least capitalised insurers in a country. The variation in transactions and solvency 

points to interesting insights. First, large changes in transactions (y-axis) indicate active investment 

strategies. Second, average solvency ratios (x-axis), ranging from 100% to 200%, change more 

over time in some countries. While it is difficult to draw conclusions about causal effects or 

correlations, country samples with more variation in solvency ratios may be an interesting study 

field. 

Occasional Paper Series No 22 
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35 

 
Chart 12 

Solvency ratios of the 10th percentile of least capitalised insurers and gross bond 

transactions 

(x-axis: solvency ratios, y-axis: percentages, first quarter of 2019 to first quarter of 2021) 

Occasional Paper Series No 22 
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36 

05101520253035100120140160180200a) AT05101520253035100120140160180200b) BE05101520253035100120140160180200c) DE05101520253035100120140160180200d) ES05101520253035100120140160180200e) FR05101520253035100120140160180200f) IE 
 
Sources: Public EIOPA insurance statistics, Solvency II reporting template S.23.01 quarterly solo; ECB SHSS; EIOPA (2020a) 
and authors’ calculations. 
Notes: Gross bond transactions on the y-axis are expressed as a percentage of previous period holdings at nominal values. The 
x-axis refers to solvency ratios in percent. Solvency ratios describe the coverage ratios of the solvency capital requirement with 
insurers’ own funds and are averages for insurers in the lowest 10th percentile. Red colour indicates countries where the impact 
of removing LTG measures on average solvency ratios is larger than 39 percentage points as of the end of 2019. 

4.3 

Robustness tests 

To put our findings on insurers’ investment behaviour in perspective, we explore other debt 

securities than government bonds in the regressions. In doing so, we distinguish between 

financial and non-financial sector corporate bonds (“FC and NFC bonds”). Their holdings on 

average are smaller than for insurers’ government bonds. As shown in Table A1 in the annex, 

European insurers on average hold individual financial sector bonds worth €28.2 million and 

individual non-financial sector bonds worth €19.5 million. Whereas our sample of security-level 

observations for financial sector bonds (203,693) is nearly as large as for government bonds 

(208,584), it is substantially smaller for non-financial sector bonds (46,481). Tables A2 and A3 in 

the annex show the summary statistics for insurers’ FC and NFC bond holdings for the national 

insurance markets. 

Occasional Paper Series No 22 
Regressions 

37 

05101520253035100120140160180200g) IT05101520253035100120140160180200h) LU05101520253035100120140160180200i) NL05101520253035100120140160180200j) PT 
 
 
 
Different from government bonds, there are no signs of stabilising investment behaviour for 

financial sector bonds, which substantiates the risk of interconnectedness. On the contrary, 

on top of procyclical buying or selling in response to last quarter returns in Italy (columns 1-4 and 6) 

and France (columns 1, 2 and 6), Table 7 indicates a similar investment strategy for Spanish 

insurers. For example, column (6) indicates that a 10% decline in returns is associated with a 4.1 

percentage points reduction in nominal holdings. Whereas there has been a sector-wide trend 

towards reducing exposure to banks (EIOPA, 2020c), the different behaviour in financial sector 

bonds may be an indication of interconnectedness and of insurers’ reactions to deteriorating credit 

risk.  

Occasional Paper Series No 22 
Regressions 

38 

 
 
 
Table 7 

Heterogeneity in cyclical investment behaviour – interactions (FC bonds) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Standard errors are in parentheses (∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01). Securities are bonds issued by the financial 
corporate sector as defined by ESA (2010) (financial sector code starting with S.12). “NetBuy” is the change in the log of the 
nominal amount of financial corporate bond security s held at the end of quarter t by insurers in holder country hc. “Return” is 
the quarterly change of the price plus the coupon divided by the price of the security in the past quarter. The regression includes 
control variables for security s interacted with time t and for holder country hc interacted with time t. To account for 
heteroscedasticity and autocorrelation of the residuals, two lagged terms of the dependent variable are included as regressors, 
and standard errors are clustered at the security level. The estimations are FE panel data models spanning security, holder 
country and time FE. Significant FE are indicated by “Yes”. 

Occasional Paper Series No 22 
Regressions 

39 

Dependent Variable: NetBuy  (1) (2) (3) (4) (5) (6)   Base Base Base Base Base Log returns  Return -0.1151* -0.0954 -0.2880 -0.1059  -0.1802   (0.065) (0.065) (0.049) (0.074)  (0.113)  Return*BE 0.0311 0.0362 0.1144 0.1607 0.7017 0.2538   (0.129) (0.128) (0.109) (0.141) (0.461) (0.160)  Return*DE 0.1642 0.1546 0.1368 0.3834 0.5861 0.3224**   (0.181) (0.181) (0.093) (0.208) (0.410) (0.149)  Return*ES 0.4460** 0.4477** 0.4464** 0.5009* 0.3641 0.5941***   (0.225) (0.219) (0.219) (0.262) (0.381) (0.184)  Return*FR 0.1449** 0.1418** 0.0394 0.1493* 0.2645 0.1936*   (0.072) (0.071) (0.057) (0.081) (0.381) (0.116)  Return*IE 0.0527 0.0537 -0.0625 0.0703 0.6185 -0.1450   (0.186) (0.186) (0.158) (0.201) (0.400) (0.189)  Return*IT 0.3154** 0.8327** 0.3148* 0.3119* 0.5423 0.4703***   (0.134) (0.133) (0.171) (0.161) (0.446) (0.159)  Return*LU 0.2468** 0.2410** 0.1500 0.2481** 0.4495 0.2409*   (0.100) (0.099) (0.091) (0.110) (0.392) (0.124)  Return*NL 0.0453 0.0480 -0.3594* -0.3890* -0.4089 -0.1386   (0.171) (0.169) (0.184) (0.216) (0.542) (0.185)  Return*PT 0.0449 -0.0363 -0.0963 -0.0530 0.0086 0.0739   (0.142) (0.144) (0.124) (0.159) (0.407) (0.196)  Security FE Yes Yes No Yes - Yes  Time FE No Yes - - - -  Sec.*Time FE No No No No Yes No  HC*Time FE No No Yes Yes Yes Yes  R2 0.1122 0.1138 0.0225 0.1192 0.3630 0.1238  Observations 140,983 140,983 142,283 140,983 95,276 140,983    
 
 
 
The few findings for insurers’ investments in non-financial sector bonds point to 

opportunistic search-for-yield strategies. Similar to government bonds, insurers in several 

European countries (Belgium, France, Germany, the Netherlands, and Spain) tend to purchase 

non-financial sector bonds after periods of negative returns. Further robustness tests to improve the 

understanding of the drivers of insurers’ investment behaviour are needed. These could include 

regressions with bond yields in excess of risk-free rates and after accounting for credit risk as an 

explanatory variable to capture liquidity risk. This could support the design of new liquidity risk 
management tools in the SII review.42 

Further robustness tests to validate our results on the cyclicality of insurers’ investment 

behaviour during shocks may be conducted in future research. Examples of these robustness 

tests include using contemporaneous securities returns as an explanatory variable in the 

regressions. Whereas using previous period returns has its merits for endogeneity, this measure 

may be less adapted to shocks with opposite movements of returns in consecutive quarters. In 

addition, conducting tests with a shock dummy variable for a single quarter may yield 

complementary information. 

42   See Art. 144a and Art. 144b in Proposal for a directive of the European Parliament and of the Council amending 

Directive 2009/138/EC as regards proportionality, quality of supervision, reporting, long-term guarantee measures, 
macro-prudential tools, sustainability risks, group and cross-border supervision, 22.9.2021, COM(2021) 581 final 
2021/0295(COD). 

Occasional Paper Series No 22 
Regressions 

40 

 
 
 
 
Table 8 

Heterogeneity in cyclical investment behaviour – interactions (NFC bonds) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Standard errors are in parentheses (∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01). Securities are bonds issued by the non-financial 
corporate sector as defined by ESA (2010) (non-financial sector code starting with S.11). “NetBuy” is the change in the log of 
the nominal amount of non-financial corporate bond security s held at the end of quarter t by insurers in holder country hc. 
“Return” is the quarterly change of the price plus the coupon divided by the price of the security in the past quarter. The 
regression includes control variables for security s interacted with time t and for holder country hc interacted with time t. To 
account for heteroscedasticity and autocorrelation of the residuals, two lagged terms of the dependent variable are included as 
regressors, and standard errors are clustered at the security level. The estimations are FE panel data models spanning security, 
holder country and time FE. Significant FE are indicated by “Yes”. 

Occasional Paper Series No 22 
Regressions 

41 

Dependent Variable: NetBuy  (1) (2) (3) (4) (5) (6)   Base Base Base Base Base Log returns  Return 0.2368* 0.3561** 0.1879 0.0929  0.0883   (0.138) (0.146) (0.137) (0.145)  (0.145)  Return*BE -0.4739*** -0.5303*** -0.2939* -0.2223 -0.2783 -0.2230   (0.171) (0.175) (0.169) (0.185) (0.481) (0.174)  Return*DE -0.4197** -0.4024** -0.1606 0.2526 0.3960 0.2596   (0.167) (0.168) (0.224) (0.243) (0.562) (0.248)  Return*ES -0.6346*** -0.6339*** -0.4002* -0.3832 -0.4136 -0.3639   (0.216) (0.215) (0.231) (0.248) (0.451) (0.242)  Return*FR -0.2744 -0.3245* -0.1610 -0.1214 -0.0674 -0.1275   (0.174) (0.175) (0.177) (0.192) (0.428) (0.173)  Return*IE -0.2575 -0.2980* 0.0929 -0.0222 0.1044 0.0272   (0.162) (0.167) (0.200) (0.178) (0.427) (0.160)  Return*IT -0.2700 -0.2873 -0.1720 -0.1371 -0.0527 -0.1193   (0.176) (0.177) (0.182) (0.186) (0.422) (0.167)  Return*LU -0.1503 -0.2161 -0.0374 0.0537 0.0514 0.0661   (0.145) (0.159) (0.154) (0.163) (0.426) (0.167)  Return*NL -0.8537*** -0.8576*** -0.5692* -0.3809 -0.7625 -0.4392   (0.209) (0.214) (0.310) (0.340) (0.878) (0.349)  Return*PT -0.3624 -0.3442 -0.3904 -0.2514 -0.0372 -0.2009   (0.237) (0.249) (0.265) (0.270) (0.484) (0.255)  Security FE Yes Yes No Yes - Yes  Time FE No Yes - - - -  Sec.*Time FE No No No No Yes No  HC*Time FE No No Yes Yes Yes Yes  R2 0.0839 0.0885 0.0226 0.0995 0.3679 0.0996  Observations 31,530 31,530 31,765 31,530 24,209 31,530    
 
5 

Conclusion 

In this paper, we investigate the impact of the market turmoil at the onset of the COVID-19 

pandemic in early 2020 on insurers’ investment behaviour. This was the first shock since the 

inception of the market-based SII regime. We find that, in response to the shock, insurers in 11 EU 

Member States increased their trading activity to more than €680 billion in the first quarter of 2020. 

These portfolio adjustments took place to a large extent in European government bonds. 

Furthermore, with security-level data over a longer time period and for a cross-country 

sample, we assess whether European insurers under SII act cyclically in their investments. 

To this end, we follow the methodology proposed by Timmer (2018). When assessing the cyclicality 

of investment behaviour, it is important to rule out the possibility that transactions are executed 

before returns are observed and that these returns reflect the price impact of insurers’ transactions. 

With fixed effects regressions, we find that European insurers act procyclically in particular holder 

or issuer countries. Indeed, we find differences in investment behaviour across European national 

insurance markets, with more procyclical behaviour in countries with large direct European 

government bond holdings and for financial sector bonds. At the same time, we confirm the home 

bias towards the domestic sovereign found by Fache Rousová and Giuzio (2019b).  

Our research is important to generate knowledge about European insurers in multiple 

jurisdictions operating under the SII framework. It is not without policy conclusions on the time 

horizon of insurers’ investments and may support reviews of the SII regulation. Notwithstanding 

European insurers’ capacity to hold assets in the long term, we find them active and not long-term 

buy-and-hold investors in most jurisdictions, confirming their role as important financial 

intermediaries (Brunnermeier and Pedersen, 2009). The implication of this result may be relevant 

when determining capital requirements for investments, when designing anti-procyclicality 

mechanisms and, more broadly, when assessing insurers’ liquidity risk profile. 

To understand the underlying mechanisms of insurers’ investment behaviour, firm-level 

data are needed. Questions for future research include, for instance, whether regulation 

considering ratings or bond durations leads insurers to invest in a specific manner or whether 

characteristics such as capitalistic cross-border ties or index-linked and unit-linked business matter. 

Interesting study fields are national insurance markets with active investment strategies and 

observations of procyclical and countercyclical behaviour, such as in the Netherlands and 

Germany. Investigating the relationship between European insurers’ solvency positions, their use of 

long-term guarantee measures and the cyclicality of their investment behaviour are other areas for 
investigation.43 For example, Deutsche Bundesbank (2021) identifies procyclical behaviour for 

those German life insurers that only met their regulatory capital requirements by means of the SII 
transitional measures, i.e. with SCR ratios below 100% without transitional measures. 44 These 

43   See also Acharya, Philippon and Richardson (2017), who measure systemic risk for insurers by investigating fire sales and 

solvency risks. 

44   The majority of German life insurers had an SCR ratio above 100% even without transitional measures. With a sample 
including investments within funds, Deutsche Bundesbank (2021) finds the majority of German life insurers acted 
countercyclically during the COVID-19 shock. See also Carvalho and Schmitz (2021) on different findings when including 
investment fund look-through data.  

Occasional Paper Series No 22 
Conclusion 

42 

 
 
results suggest that strong capitalisation is at least as important from a financial stability 

perspective as that of mitigating procyclical behaviour. Good capitalisation is thus often a 

prerequisite for countering systemic risks. 

Occasional Paper Series No 22 
Conclusion 

43 

 
References 

Acharya, V.V., Philippon, T. and Richardson, M. (2016), “Measuring Systemic Risk for Insurance 

Companies”, in Hufeld, S., Koijen, R.S.J. and Thimann, C. (eds.), The Economics, Regulation and 

Systemic Risk of Insurance Markets, Oxford University Press. 

Apicella, F., Gallo, R. and Guazzarotti, G. (2022), “Insurers’ investments before and after the 

Covid-19 outbreak”, Working Papers, No 1363, Banca d’Italia, February. 

Austrian Financial Market Authority (2019), “Bericht der FMA 2019 zur Lage der 

österreichischen Versicherungswirtschaft”, October. 

Baluch, F., Mutenga, S. and Parsons, C. (2011), “Insurance, Systemic Risk and the Financial 

Crisis”, Geneva Papers on Risks and Insurance – Issues and Practice, Vol. 36, No 1, pp. 126-163. 

Banca d’Italia (2021), Financial Stability Report, No 2, November. 

Bank for International Settlements (2011), “Fixed income strategies of insurance companies 

and pension funds”, CGFS Papers, No 44, July. 

Becker, B. and Ivashina, V. (2015), “Reaching for Yield in the Bond Market”, The Journal of 

Finance, Vol. 70, No 5, pp. 1863-1902. 

Bijlsma, M. and Vermeulen, R. (2016), “Insurance companies’ trading behaviour during the 

European sovereign debt crisis: flight home or flight to quality?”, Journal of Financial Stability, 

Vol. 27, pp. 137-154. 

Bilio, M., Getmansky, M., Lo, A.W. and Pelizzon, L. (2012), “Econometric measures of 

connectedness and systemic risk in the finance and insurance sectors”, Journal of Financial 

Economics, Vol. 104, Issue 3, pp. 535-595. 

Brinkhoff, J. and Solé, J. (2022), “Did Insurers Become Risk-Loving During ‘Low-for-Long’?”, 

Working Papers, No 202, International Monetary Fund, September. 

Brunnermeier, M. and Pedersen, L.H. (2009), “Market Liquidity and Funding Liquidity”, The 

Review of Financial Studies, Vol. 22, Issue 6, pp. 2201-2238. 

Carvalho, D. and Schmitz, M. (2021), “Shifts in the portfolio holdings of euro area investors in 

the midst of COVID-19: looking-through investment funds”, Working Paper Series, No 2526, 

ECB, Frankfurt am Main, February. 

Chodorow-Reich, G., Ghent, A. and Haddad, V. (2021), “Asset Insulators”, The Review of 

Financial Studies, Vol. 34, Issue 3, pp. 1509-1534. 

Czech, R., Gual-Ricart, B., Lillis, J. and Worlidge, J. (2021), “The role of non-bank financial 

intermediaries in the ‘dash for cash’ in sterling markets”, Financial Stability Papers, No 47, 

Bank of England, June. 

Occasional Paper Series No 22 
References 

44 

 
Deloitte (2020), “Should insurers leverage Solvency 2 discount rate techniques when valuing 

insurance liabilities under IFRS 17?”, July. 

Deutsche Bundesbank (2021), Financial Stability Review, November. 

Domanski, D., Shin, H.S. and Sushko, V. (2015), “The hunt for duration: not waving but 

drowning?”, Working Papers, No 519, Bank for International Settlements, February. 

Duijm, P. and Steins Bisschop, S. (2015), “Short-termism of long-term investors? The 

investment behaviour of Dutch insurance companies and pension funds”, DNB Working 

Papers, No 489, De Nederlandsche Bank, December. 

Duijm, P. and Steins Bisschop, S. (2018), “Short-termism of long-term investors? The 

investment behaviour of Dutch insurance companies and pension funds”, Applied Economics, 

Vol. 50, Issue 31, pp. 3376-3387. 

Düll, R., König, F. and Ohls, J. (2017), “On the exposure of insurance companies to sovereign 

risk – Portfolio investments and market forces”, Journal of Financial Stability, Vol. 31, pp. 93-

106. 

European Central Bank (2020), Financial Stability Review, November. 

European Insurance and Occupational Pensions Authority (2020a), Financial Stability Report, 

July. 

European Insurance and Occupational Pensions Authority (2020b), “Report on long-term 

guarantee measures and measures on equity risk 2020”, December. 

European Insurance and Occupational Pensions Authority (2020c), Financial Stability Report, 

December. 

European Insurance and Occupational Pensions Authority (2021a), Financial Stability Report, 

July. 

European Insurance and Occupational Pensions Authority (2021b), “2021 Insurance Stress Test”, 

December. 

European Insurance and Occupational Pensions Authority (2022), Financial Stability Report, 

June. 

European Systemic Risk Board (2018), “Macroprudential provisions, measures and 

instruments for insurance”, November. 

European Systemic Risk Board (2020a), “ESRB letter to EIOPA on liquidity risks in the 

insurance sector”, June. 

European Systemic Risk Board (2020b), “A system-wide scenario analysis of large-scale 

corporate bond downgrades”, July. 

Occasional Paper Series No 22 
References 

45 

 
European Systemic Risk Board (2020c), “ESRB letter to the European Commission on a 

consultation of the European Commission on the review of Solvency II”, October. 

European Systemic Risk Board (2021a), “A Review of Macroprudential Policy in the EU in 

2020”, July. 

European Systemic Risk Board (2021b), “Issues note on systemic vulnerabilities of and 

preliminary policy considerations to reform money market funds (MMFs)”, July. 

European Systemic Risk Board (2022), “EU Non-bank Financial Intermediation Risk Monitor 

2022”, July. 

Eurostat (2010), “European system of accounts – ESA 2010”. 

Fache Rousová, L., Ghio, M., Kördel, S. and Salakhova, D. (2020), “Interconnectedness of 

derivatives markets and money market funds through insurance corporations and pension 

funds”, in ECB (ed.), Financial Stability Review, November. 

Fache Rousová, L. and Giuzio, M. (2019a), “Box 9: Insurers’ investment in alternative assets”, 

in ECB (ed.), Financial Stability Review, May. 

Fache Rousová, L. and Giuzio, M. (2019b), “Insurers’ investment strategies: pro- or 

countercyclical?”, Working Paper Series, No 2229, ECB, Frankfurt am Main, July. 

Giese, J., Joyce, M., Meaning, M. and Worlidge, J. (2021), “Preferred habitat investors in the UK 

government bond market”, Staff Working Papers, No 939, Bank of England, September.  

Harvey, C.R. and Siddique, A. (2000), “Conditional Skewness in Asset Pricing Tests”, The 

Journal of Finance, Vol. 55, No 3, pp. 1263-1295. 

International Association of Insurance Supervisors (2019), “Holistic Framework for Systemic 

Risk in the Insurance Sector”, November. 

Jourde, T. (2022), “The rising interconnectedness of the insurance sector”, Journal of Risk and 

Insurance, Vol. 89, Issue 1, pp. 1-29. 

Kaufmann, C., Leyva, H. and Storz, M. (2022), “Where to put 9 trillion euros? Insurance 

corporations, financial stability and monetary policy”, DGMF Seminar Series, ECB, Frankfurt am 

Main, 13 October. 

Kirti, D. (2018), “When gambling for resurrection is too risky”, Working Paper Series, No 69, 

ESRB, Frankfurt am Main, February. 

Kirti, D. and Shin, M.Y. (2020), “Impact of COVID-19 on Insurers”, Special Series on COVID-19, 

International Monetary Fund, May. 

Korpiaho, T. (2022), “Insurance sector in Finland”, presentation to the ESRB Expert Group on 

Insurance, 2 May. 

Occasional Paper Series No 22 
References 

46 

 
Kraus, A. and Litzenberg, R.H. (1976), “Skewness Preference and the Valuation of Risk 

Assets”, The Journal of Finance, Vol. 31, No 4, pp. 1085-1100. 

Kubitza, C. (2022), “Investor-Driven Corporate Finance: Evidence from Insurance Markets”. 

Reuters (2018), “Sweden leads European bond trading volume drop in Q2 - AFME”, 19 

September. 

Timmer, Y. (2018), “Cyclical investment behaviour across financial institutions”, Journal of 

Financial Economics, Vol. 129, Issue 2, pp. 268-286. 

Van Binsbergen, J. and Brandt, M.W. (2016), “Optimal Asset Allocation in Asset Liability 

Management”, in Veronesi, P. (ed.), Handbook of Fixed-Income Securities, Wiley, pp. 237-275. 

Occasional Paper Series No 22 
References 

47 

 
Annex 

A1 

Methodology 

The ECB Securities Holdings Statistics (SHS) are the main dataset used for the analysis. The 

ECB SHS include two databases, one related to banking groups (SHSG) and one to holder sectors 

(SHSS). To include cross-sector and cross-asset analysis, we use the SHSS as a reference 

database. This database provides quarterly information on securities held by euro area investors at 

the holder country and holder sector level, broken down by instrument type, issuer country and 

further classifications. Holdings are reported by the national central banks, while transaction data 

are also calculated based on the change in holdings not due to price, foreign exchange, and other 
effects.45 As opposed to other sector-specific data such as Solvency II (SII), SHSS do not 

discriminate between investments for index-linked and unit-linked insurance products. 

Our analysis focuses on insurers’ investment behaviour with respect to bond market 

segments. Bond market segments are classified according to ESA (2010) as government, financial 

corporate and non-financial corporate. Concerning the maturity profile and for the descriptive 

statistics, bonds have been grouped by their residual maturity below one year, from one to five 

years and above five years to capture short-term, medium-term and long-term debt. All bonds that 
matured within a quarter were assigned the security status “matured”46, which is confirmed by other 

characteristics such as negative residual maturity days. 

Assessing the credit risk of insurers’ bond portfolios required a more meticulous approach. 

Holdings and transaction data from SHSS have been merged with ratings data from the Centralised 

Securities Database (CSDB). Following the methodology of the Deutsche Bundesbank, credit 

ratings have been allocated to credit quality steps (CQSs) by translating the ratings available in 

SHSS (or CSDB more precisely) into the CQSs of the SII framework. Ratings considered were 

issued by the main credit rating agencies for long-term debt in local or foreign currency at the issue 
and issuer level.47 Sales of downgraded bonds were identified when sales exceeded holdings in the 

previous quarter. For example, if a specific bond held in the fourth quarter of 2019 had a CQS 4 

and was downgraded to CQS 5 or 6 in the first quarter of 2020, the transactions in this bond are 

recorded under the new CQS. 

Finally, regarding the geographic allocation, securities are grouped by the issuer domicile. 

This can be “domestic” when the issuer domicile country is the same as the country of incorporation 

of the insurer, “other EU” when it is not domestic but inside the EU, “UK, US and Japan” when the 

issuer is domiciled in one of these developed economies and “rest of the world” when it belongs to 

none of the categories. The analysis for the descriptive statistics also comprises cross-sectoral and 

45   The ECB Directorate General of Statistics calculates transactions, together with foreign exchange, price and other changes 

since 2016. 

46   The security status flag (SHSS code security status) indicates whether a security is “alive” (active) or not (not active). 

47   The three main credit rating agencies are Fitch, Moody’s and Standard and Poor’s. DBRS by Morningstar is included as a 

credit rating agency. 

Occasional Paper Series No 22 
Annex 

48 

 
 
cross-asset class studies. A less granular analysis is conducted at the cross-sectoral and cross-

asset class levels (bonds, listed equities, investment funds and money market funds). 

A2 

Additional analyses 

Chart A1 

Bond transactions of European insurers by issuer region and market segment 

(percentages and EUR trillion, first quarter of 2020) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Net transactions, purchases and sales in the first quarter of 2020 are expressed as a percentage of previous period 
holdings at market value. Sales include matured bonds. Gvmt, FC and NFC represent bonds issued by governments (S.13), 
financial sector corporations (S.12) and non-financial corporations (S.11) following the ESA (2010) definition. The data refer to 
insurers in Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, Portugal, and Spain. 

Occasional Paper Series No 22 
Annex 

49 

0.00.51.01.52.02.53.0-50-45-40-35-30-25-20-15-10-5051015DomesticOther EUUK, US, JapanRest of theWorldNet transactions (%)Purchases (%)Sales (%)Holdings -Q4 2019 (right-hand side)Holdings -Q1 2020 (right-hand side)a) Breakdown by issuer region0.00.51.01.52.02.53.0-50-45-40-35-30-25-20-15-10-5051015Gvmt bondsNFC bondsFC bondsb) Breakdown by market segment  
 
Chart A2 

Bond transactions of European insurers by bond market segment 

(percentages and EUR trillion, first quarter of 2020) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Net transactions, purchases and sales are expressed as a percentage of previous period holdings at market value. Sales 
include matured bonds. Gvmt., FC and NFC bonds represent bonds issued by governments (S.13), financial sector corporations 
(S.12) and non-financial corporations (S.11) following the ESA (2010) definition. 

Chart A3 

Bond transactions of European insurers by residual maturity 

(percentages and EUR trillion, first quarter of 2020) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Net transactions, purchases and sales are expressed as a percentage of previous period holdings at market value. Sales 
include matured bonds. Bonds are government, financial and non-financial sector bonds following the ESA (2010) definition.  

Occasional Paper Series No 22 
Annex 

50 

0.00.10.20.30.40.50.60.70.80.91.01.11.2-70-60-50-40-30-20-10010203040ATBEDEESFIFRIEITLUNLPTa) Gvmt0.00.10.20.30.40.50.60.70.80.91.01.11.2-70-60-50-40-30-20-10010203040ATBEDEESFIFRIEITLUNLPTb) FC0.00.10.20.30.40.50.60.70.80.91.01.11.2-70-60-50-40-30-20-10010203040ATBEDEESFIFRIEITLUNLPTc) NFCNet transactions (%)Purchases (%)Sales (%)Holdings -Q4 2019 (right-hand side)Holdings -Q1 2020 (right-hand side) 0.00.20.40.60.81.01.21.41.61.82.0-70-60-50-40-30-20-10010203040ATBEDEESFIFRIEITLUNLPTa) <1 year0.00.20.40.60.81.01.21.41.61.82.0-70-60-50-40-30-20-10010203040ATBEDEESFIFRIEITLUNLPTb) 1 to 5 years0.00.20.40.60.81.01.21.41.61.82.0-70-60-50-40-30-20-10010203040ATBEDEESFIFRIEITLUNLPTc) >5 yearsNet transactions (%)Purchases (%)Sales (%)Holdings -Q4 2019 (right-hand side)Holdings -Q1 2020 (right-hand side)  
 
 
Chart A4 

Bond transactions of European insurers by credit quality 

(percentages and EUR trillion, first quarter of 2020) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Net transactions, purchases and sales are expressed as a percentage of previous period holdings at market value. Sales 
included matured bonds. Bonds are government, financial and non-financial sector bonds following the ESA (2010) definition. 
CQS refers to credit quality step and ranges from 0 (highest, i.e. AAA) to 6 (lowest, i.e. CCC/D); “NR” represents non-rated 
bonds.  

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0.00.20.40.60.81.01.21.41.61.82.0-60-55-50-45-40-35-30-25-20-15-10-50510152025303540ATBEDEESFIFRIEITLUNLPTa) CQS 0-1-20.00.20.40.60.81.01.21.41.61.82.0-60-55-50-45-40-35-30-25-20-15-10-50510152025303540ATBEDEESFIFRIEITLUNLPTb) CQS 30.00.20.40.60.81.01.21.41.61.82.0-60-55-50-45-40-35-30-25-20-15-10-50510152025303540ATBEDEESFIFRIEITLUNLPTc) CQS 4-5-60.00.20.40.60.81.01.21.41.61.82.0-60-55-50-45-40-35-30-25-20-15-10-50510152025303540ATBEDEESFIFRIEITLUNLPTd) NRNet transactions (%)Purchases (%)Sales (%)Holdings -Q4 2019 (right-hand side)Holdings -Q1 2020 (right-hand side)  
 
Chart A5 

Government bond holdings of European insurers 

(EUR trillion, first quarter of 2017 to third quarter of 2021) 

Sources: ECB SHSS and authors’ calculations.  
Notes: Government bonds (S.13) follow the ESA (2010) definition and have a fixed annual or semi-annual coupon and a market 
or paid price in SHSS.  

Table A1 

Summary statistics of bond holdings of European insurers 

(EUR million and percentages, first quarter of 2017 to second quarter of 2022) 

Sources: ECB SHSS and authors’ calculations.  
Notes: Bonds are as defined by ESA (2010) with sector classifications starting with S.12 for financial corporate bonds and S.11 
for non-financial corporate bonds). “Std” refers to the standard deviation. “Holdings” is the nominal value of government bonds 
held by insurers. “Buy” and “Sell” refer to the amount bought and sold in EUR million. “NetBuy” is the change in the log of the 
nominal amount held. “Buy%” and “Sell%” are the changes in the log of the nominal amount held if positive or negative, 
respectively. “Return” is the holding period return defined as the quarterly change in the price plus the quarterly coupon divided 
by the price in the previous quarter. The sample refers to insurers in Austria, Belgium, France, Germany, Ireland, Italy, 
Luxembourg, the Netherlands, Portugal, and Spain. 

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02004006008001,0001,2001,400Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q320172018201920202021BillionsFRITDEESBENLIEATPTLUFC bonds          Holdings Buy Sell NetBuy Buy% Sell% Return Mean 28.23 1.13 1.30 -0.007 0.113 0.122 0.006 Median 3.00 0.04 0.05 0.000 0.016 0.020 0.003 Std 101.11 8.18 13.44 0.393 0.367 0.382 0.062 Observations 203,693 99,959 103,734 203,693 99,959 103,734 203,693 NFC bonds   Holdings Buy Sell NetBuy Buy% Sell% Return Mean 19.57 0.64 1.06 -0.006 0.099 0.127 0.004 Median 2.22 0.04 0.05 0.000 0.022 0.024 0.003 Std 59.18 3.03 4.76 0.335 0.288 0.344 0.070 Obs. 46,481 24,852 21,629 46,481 24,852 21,629 46,481   
 
 
Table A2 

Summary statistics of European insurers’ FC bond holdings by country 

(EUR million and percentages, first quarter of 2017 to second quarter of 2022) 

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AT         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 13.58 0.22 0.64 -0.010 0.022 0.061 0.003 Median 6.09 0.04 0.09 0.000 0.007 0.016 0.001 Std 24.47 1.22 2.65 0.150 0.089 0.205 0.047 Obs. 12,624 8,017 4,607 12,624 8,017 4,607 12,624 BE         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 12.21 0.91 0.61 -0.001 0.167 0.101 0.008 Median 2.23 0.02 0.13 -0.011 0.041 0.021 0.009 Std 22.76 3.61 1.98 0.362 0.398 0.295 0.055 Obs. 11,096 4,111 6,985 11,096 4,111 6,985 11,096 DE         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 29.66 2.80 3.55 -0.015 0.196 0.198 0.003 Median 5.50 0.16 0.26 -0.005 0.034 0.036 0.003 Std 104.94 10.17 10.11 0.551 0.533 0.498 0.042 Obs. 13,271 6,176 7,095 13,271 6,176 7,095 13,271 ES         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 21.33 0.71 0.92 0.001 0.055 0.055 0.003 Median 8.50 0.03 0.03 0.000 0.003 0.003 0.001 Std 49.51 2.32 4.44 0.193 0.175 0.195 0.040 Obs. 12,715 6,447 6,268 12,715 6,447 6,268 12,715 FR         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 101.55 2.91 2.82 -0.007 0.066 0.063 0.007 Median 22.64 0.13 0.15 0.000 0.009 0.010 0.003 Std 205.85 14.57 26.06 0.298 0.298 0.287 0.063 Obs. 36,891 16,082 20,809 36,891 16,082 20,809 36,891   
 
 
 
Sources: ECB SHSS and authors’ calculations. 
Notes: Bonds represent bonds issued by financial sector corporations (S.12) following the ESA (2010) definition. “Std” refers to 
the standard deviation. “Holdings” is the nominal value of bonds held by insurers. “Buy” and “Sell” refer to the amount bought 
and sold in EUR million. “Net Buy” is the change in the log of the nominal amount held. “Buy%” and “Sell%” are the changes in 
the log of the nominal amount held if positive or negative, respectively. “Return” is the holding period return defined as the 
quarterly change in the price plus the quarterly coupon divided by the price in the previous quarter. 

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IE         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 4.10 0.55 0.40 -0.003 0.149 0.146 0.006 Median 1.33 0.02 0.02 0.000 0.023 0.025 0.004 Std 12.91 8.49 7.57 0.495 0.471 0.473 0.079 Obs. 40,199 19,418 20,781 40,199 19,418 20,781 40,199 IT         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 31.19 0.94 1.75 -0.011 0.053 0.102 0.004 Median 12.17 0.10 0.21 0.002 0.008 0.022 0.002 Std 49.81 3.27 4.83 0.287 0.224 0.338 0.036 Obs. 20,444 11,963 8,481 20,444 11,963 8,481 20,444 LU         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 2.60 0.35 0.32 -0.008 0.158 0.164 0.007 Median 0.59 0.02 0.02 0.000 0.030 0.032 0.004 Std 10.53 5.73 9.51 0.430 0.391 0.405 0.072 Obs. 44,144 21,338 22,806 44,144 21,338 22,806 44,144 NL         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 15.73 3.53 2.69 -0.011 0.259 0.269 0.004 Median 7.40 0.50 0.60 -0.003 0.052 0.625 0.002 Std 23.98 9.18 6.41 0.566 0.491 0.510 0.043 Obs. 4,737 2,316 2,421 4,737 2,316 2,421 4,737 PT         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 5.32 0.30 0.43 -0.012 0.069 0.106 0.003 Median 2.01 0.01 0.01 0.000 0.005 0.008 0.001 Std 15.87 1.25 1.69 0.312 0.276 0.325 0.036 Obs. 7,572 4,091 3,481 7,572 4,091 3,481 7,572   
 
 
 
Table A3 

Summary statistics of European insurers’ NFC bond holdings by country 

(EUR million and percentages, first quarter of 2017 to second quarter of 2022) 

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AT         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 7.14 0.14 0.59 -0.020 0.002 0.008 0.002 Median 3.05 0.02 0.05 0.000 0.007 0.018 0.003 Std 10.31 0.61 1.89 0.160 0.079 0.234 0.042 Obs. 1,874 1,201 673 1,874 1,201 673 1,874 BE         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 3.66 0.29 0.42 -0.001 0.146 0.152 0.009 Median 0.13 1.02 0.01 0.001 0.041 0.037 0.008 Std 9.32 0.01 1.83 0.367 0.349 0.366 0.089 Obs. 4,499 2,282 2,217 4,499 2,282 2,217 4,499 DE         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 10.56 1.30 1.50 -0.004 0.145 0.179 0.004 Median 3.99 0.15 0.20 0.006 0.035 0.043 0.003 Std 23.06 5.25 4.53 0.408 0.353 0.400 0.058 Obs. 3,213 1,734 1,479 3,213 1,734 1,479 3,213 ES         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 12.05 0.52 0.57 0.002 0.054 0.057 0.001 Median 5.51 0.01 0.01 0.000 0.002 0.002 0.001 Std 24.82 1.53 2.11 0.186 0.162 0.192 0.041 Obs. 2,607 1,364 1,243 2,607 1,364 1,243 2,607 FR         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 83.20 1.63 3.01 -0.003 0.063 0.069 0.004 Median 21.65 0.12 0.20 0.000 0.017 0.014 0.003 Std 123.48 5.53 9.80 0.267 0.242 0.272 0.067 Obs. 7,541 3,739 3,802 7,541 3,739 3,802 7,541   
 
 
 
Sources: ECB SHSS and authors’ calculations.  
Notes: Bonds represent bonds issued by non-financial sector corporations (S.11) following the ESA (2010) definition. “Std” 
refers to the standard deviation. “Holdings” is the nominal value of bonds held by insurers. “Buy” and “Sell” refer to the amount 
bought and sold in million euros. “Net Buy” is the change in the log of the nominal amount held. “Buy%” and “Sell%” are the 
changes in the log of the nominal amount held if positive or negative, respectively. “Return” is the holding period return defined 
as the quarterly change in the price plus the quarterly coupon divided by the price in the previous quarter. 

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IE         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 2.94 0.22 0.27 -0.002 0.113 0.138 0.004 Median 1.15 0.02 0.02 0.001 0.025 0.026 0.004 Std 4.16 0.70 0.87 0.375 0.324 0.386 0.072 Obs. 9,233 5,022 4,211 9,233 5,022 4,211 9,233 IT         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 22.44 0.59 1.50 -0.014 0.046 0.096 0.001 Median 12.55 0.10 0.24 0.003 0.009 0.023 0.001 Std 31.00 2.31 3.71 0.229 0.177 0.264 0.056 Obs. 5,011 2,891 2,120 5,011 2,891 2,120 5,011 LU         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 2.29 0.17 0.22 -0.013 0.126 0.156 0.006 Median 0.77 0.02 0.02 0.000 0.031 0.034 0.004 Std 4.45 0.68 0.85 0.365 0.309 0.364 0.084 Obs. 8,865 4,495 4,370 8,865 4,495 4,370 8,865 NL         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 10.65 2.00 2.18 0.006 0.191 0.257 0.000 Median 4.35 0.21 0.26 0.021 0.045 0.045 0.001 Std 16.15 5.17 4.96 0.479 0.390 0.472 0.064 Obs. 1,808 1,063 745 1,808 1,063 745 1,808 PT         Holdings Buy Sell NetBuy Buy% Sell% Return Mean 4.29 0.46 0.41 -0.014 0.063 0.121 0.000 Median 1.52 0.01 0.01 0.001 0.007 0.010 0.001 Std 8.78 0.94 1.25 0.304 0.226 0.361 0.048 Obs. 1,830 1,061 769 1,830 1,061 769 1,830   
 
 
 
Chart A6 

Median changes in nominal bond holdings of European insurers 

(percentages, first quarter of 2017 to second quarter of 2022) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Holdings – used to calculate percentages – refer to current and to previous period holdings and are in nominal terms. 
Bonds are as defined by ESA (2010) (government sector code starting with S.13, financial sector code with S.12 and non-
financial sector code with S.11) with fixed annual or semi-annual coupons. All bonds: n= 458,758; Gvmt. bonds: n=208,584; FC 
bonds: n= 203,693; NFC bonds: n=46,481. Observations of individual bonds (all, government, FC, NFC) are held by insurers in 
Austria, Belgium, Germany, France, Ireland, Italy, Luxembourg, the Netherlands, Portugal, and Spain. 

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-2-10123456Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022a) All bonds-2-10123456Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022b) Gvmt.-2-10123456Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022c) FC-2-10123456Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2201720182019202020212022d) NFC 
 
 
 
Table A4 

Heterogeneity in cyclical investment behaviour – interactions (domestic government bonds) 

Sources: ECB SHSS and authors’ calculations. 
Notes: Standard errors are in parentheses (∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01). NetBuy is the change in the log of the nominal 
amount of government bond security s held at the end of quarter t by insurers in holder country hc. Return is the quarterly 
change of the price plus the coupon divided by the price of the security in the past quarter. The regression includes control 
variables for security s interacted with time t and for holder country hc interacted with time t. To account for heteroscedasticity 
and autocorrelation of the residuals, two lagged terms of the dependent variable are included as regressors, and standard 
errors are clustered at the security level. The estimations are FE panel data models spanning security, holder country and time 
FE. Significant FE are indicated by “Yes”. 

Occasional Paper Series No 22 
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Dependent Variable: NetBuy  (1) (2) (3) (4) (5) (6)   Base Base Base Base Base Log returns  Home*Return 0.0889** 0.0889** 0.0478 0.1243 -0.0620 0.1443*   (0.032) (0.032) (0.090) (0.088) (0.187) (0.084)  Home*Return*BE -0.4285** -0.3533* 0.0210 -0.1749 0.0161 -0.1833   (0.147) (0.178) (0.289) (0.284) (0.359) (0.285)  Home*Return*DE 0.1100 0.0884 -0.0007 0.0656 0.0080 0.0787   (0.110) (0.093) (0.386) (0.391) (0.533) (0.382)  Home*Return*ES 0.0252 0.0245 0.0097 0.0107 0.3797 0.0124   (0.066) (0.070) (0.109) (0.112) (0.231) (0.110)  Home*Return*FR -0.0461 -0.0676 0.1570 -0.0835 0.1303 -0.1102   (0.061) (0.052) (0.135) (0.132) (0.220) (0.130)  Return*IE 0.0173 -0.0021 0.0224 -0.0875 0.5155 -0.0674   (0.083) (0.063) (0.195) (0.197) (0.453) (0.197)  Return*IT -0.2395** -0.2962*** -0.4194*** -0.2688** 0.0230 -0.3513**   (0.091) (0.087) (0.150) (0.123) (0.225) (0.125)  Return*LU -0.0389 -0.2200 -0.3976 -0.7521** -1.0451 -0.7802**   (0.103) (0.220) (0.285) (0.308) (2.539) (0.306)  Return*NL 0.5573*** 0.5169*** 0.5259 0.4018 0.0336 0.5306   (0.124) (0.130) (0.395) (0.364) (0.513) (0.363)  Return*PT -0.6171*** -0.5560*** -0.4039 -0.5823 -0.4938 -0.6927*   (0.132) (0.136) (0.396) (0.380) (0.373) (0.415)  Security FE Yes Yes No Yes - Yes  Time FE No Yes - - - -  Sec.*Time FE No No No No Yes No  HC*Time FE No No Yes Yes Yes Yes  R2 0.0542 0.0542 0.0171 0.0621 0.2833 0.0621  Observations 167,816 167,816 168,144 167,816 134,919 167,816    
 
 
Imprint and acknowledgements 

Valuable comments from members of the ESRB’s Expert Group on Insurance (chaired by Patrick Darlap) and 
the ESRB’s Advisory Technical Committee (chaired by Pablo Hernández de Cos) are gratefully acknowledged. 
We thank Mara Aquilani, Rainer Beckmann, Linda Fache, Alessandro Fontana, Natalia Fusco, Mónica 
González Perdiguero, Camille Graciani, Jyrki Haajanen, Dieter Hendrickxs, Laura Izquierdo Rio, Anke Kablau, 
Laura Languedoc, Axel Möhlmann, Jana Ohls, Federica Pallante, Olaf Weeken and Mattias Wrienz for their 
helpful comments. 
Our gratitude also goes to Tina Ražić and Thorsten Radke as well as to Ilias Aarab, Magnus Andersson, Emilia 
Bonaccorsi di Patti, Lars Brausewetter, Jesus Crespo Cuaresma, Gaëtan Le Floch, Raffaele Giuliana, Elena 
Lanza an anonymous referee and Banque de France. 
The views expressed in this paper are those of the authors and do not represent the official views of the 
institutions to which the authors are affiliated. 

Constanze Fay 
Austrian Financial Market Authority (FMA), Vienna, Austria; e-mail: constanze.fay@fma.gv.at 

Angelica Ghiselli 
Central Bank of Ireland, Dublin, Ireland; e-mail: angelica.ghiselli@centralbank.ie 

© European Systemic Risk Board, 2023 

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For specific terminology please refer to the ESRB glossary (available in English only). 

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ISBN 978-92-9472-347-5, ISSN 2467-0669, doi:10.2849/71208, DT-AC-23-001-EN-N