Conditional expected shortfalls calculated for European insurance companies and banks under stressed market conditions are shown to be of similar magnitudes. Measured at 95% and 99% stress levels, on data covering the period from 1995 to 2011, the equity-return tail losses of insurance undertakings and banks are indistinguishable. Granger causality analysis, on all pairs of banks and insurance companies included in the sample, shows that banks and insurance companies have equal propensity to cause each others price movements. Even though the business model of insurance undertakings is different from the business model typically applied by banks, and even though insurance companies are not depending to a similar degree on short term funding as banks, the empirical results indicate that the financial equity markets in Europe do not differentiate their trading of banks and insurance companies in periods of stress.
Insurance companies perform an important task in the economy by ensuring that economic agents can buy protection against risks that otherwise cannot be hedged. In this way they complement financial markets by making risk-hedging instruments available. A traditional insurance company will build its business model around the collection of premiums over time, from households and firms, and by the implementation of an appropriate asset allocation as a storage of wealth, whereby premiums and investment returns are accumulated and grown with the help of financial market instruments. The aim of such investment activities is to build an asset base that will enable the company to cover future insurance claims, that are uncertain in amount and timing. A traditional bank also serves a central role in the economy by providing credit intermediation for agents with lending and borrowing needs. Traditional banking operations reduce the search costs, and potentially creates better prised and organised markets for credit intermediation, to the benefit of agents with surplus and deficit cash positions. Economic agents can thus turn to a bank to deposit funds and obtain loans, respectively, rather than searching for bilateral agreements in the markets.
These different roles played by insurance companies and banks in the economy naturally lead to differences in their balance sheet compositions and to differences in their risk profiles and potential business-cycle vulnerabilities. An insurance company can relatively freely determine its asset composition and will inherently strive to minimise the shortfall risk stemming from states of the world where its assets do not provide adequate funding of its liabilities. Consequently, the insurance company balance sheet will typically show limited exposure to market risk. However, long-term market trends can adversely affect the solvency position of insurance companies, for example, a prolonged period of low interest rates will reduce (re)investment income on investment assets and increase the present value of the liabilities. Also, structural changes in mortality rates, birth rates, crime rates, and natural disaster frequencies, and severities can erode the profitability of insurance companies on the longer time-horizon. Compared to balance sheet risks existing in traditional banking business, which comprise, among other things, an inherent duration mismatch between assets and liabilities, liquidity and credit risk, the insurance business seem to constitute a type of financial activity that is relatively safe and chiefly affected by long-term secular trends.
The conclusion reached by analysing and comparing the intrinsic and fundamental features of the banking and insurance businesses is therefore that, by construction, banking business entails an amount of systematic risk, while insurance business is more safe. (See among others, publication from the Geneva Association (
In the light of this reality, the current paper sets out to analyse the degree of interconnectedness that exist between exchange traded European insurance companies and banks. This is done by two derived measures: the conditional tail dependance and Granger-causality tests.
To investigate the degree of interconnectedness that may exist between European insurance companies and banks, I rely on the frameworks and ideas presented by Acharya et al. [
As an extension of this frame of thought, I suggest to measure interconnectedness between companies, in the current case between banks and insurance undertakings, simply by the size of their conditional expected shortfall (CES), and by their ranking in distribution of conditional tail losses. In addition to the CES analysis, I also perform Granger-causality tests, as suggested by, among others, Billio et al. [
The used CES measure is defined in the following way: Let
Based on this, interconnected insurance companies and banks, as judged by the equity markets, are defined by the set of companies fulfilling (
It is of course possible to construct a portfolio of nonfinancial equities that fulfills (
I follow Billio et al. [
A number of recent papers have proposed alternative ways to define and quantify the concept interconnectedness, with applications primarily to the banking sector. The common denominator of these papers is that they rely, in one way or the other, on traditional statistical methods. Billio et al. [
The purpose of the current paper is not to compare and contrast different measures of systemic risk or interconnectedness. Rather, the paper sets out to investigate the more narrowly defined topic of whether insurance and banking industries are interconnected. And, for this purpose, as mentioned above, I rely on the frameworks and ideas outlined by Acharya et al. [
Daily equity market data are used in the CES analysis, and weekly return data are used in the Granger-causality test. (Weekly data are used in the Granger-causality tests to optimise data availability on all dates of the data panel. Daily data for the CES analysis are used and here data coverage on the “crisis days” is of importance.) European insurance companies and banks are included in the data sample on the basis of two criteria: the company should
Entities included in the analysis.
Bloomberg ticker | Name | Country | Market cap (mill) | Number of employees |
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Insurance | ||||
AGN NA Equity | Aegon Nv | The Netherlands | 5204 | 30092 |
AGS BB Equity | Ageas | Belgium | 3222 | 12000 |
ALV GY Equity | Allianz Se-Reg | Germany | 28084 | 150170 |
AML LN Equity | Amlin Plc | Britain | 1403 | 1249 |
AV/LN Equity | Aviva Plc | Britain | 7970 | 45341 |
CS FP Equity | Axa Sa | France | 20047 | 102957 |
FSA IM Equity | Fondiaria-Sai Spa | Italy | 604 | 7833 |
G IM Equity | Assicurazioni Generali | Italy | 17608 | 85019 |
INGA NA Equity | Ing Groep Nv-Cva | The Netherlands | 17823 | 107305 |
JLT LN Equity | Jardine Lloyd Thompson Group | Britain | 1384 | 6212 |
LGEN LN Equity | Legal & General Group Plc | Britain | 5437 | 8662 |
MUV2 GY Equity | Muenchener Rueckver Ag-Reg | Germany | 14595 | 47039 |
RSA LN Equity | Rsa Insurance Group Plc | Britain | 3793 | 22078 |
SAMAS FH Equity | Sampo Oyj-A Shs | Finland | 9738 | 6928 |
SCR FP Equity | Scor Se | France | 2797 | 1822 |
SLHN VX Equity | Swiss Life Holding Ag-Reg | Switzerland | 2915 | 7483 |
SREN VX Equity | Swiss Re Ag | Switzerland | 14447 | 10448 |
STB NO Equity | Storebrand Asa | Norway | 12188 | 2163 |
TOP DC Equity | Topdanmark A/S | Denmark | 12609 | 2523 |
VIG AV Equity | Vienna Insurance Group Ag | Austria | 3300 | 24968 |
ZURN VX Equity | Zurich Financial Services Ag | Switzerland | 25522 | 54934 |
Banks | ||||
ALPHA GA Equity | Alpha Bank A.E. | Greece | 695 | 14673 |
BARC LN Equity | Barclays Plc | Britain | 17873 | 146100 |
BCP PL Equity | Banco Comercial Portugues-R | Portugal | 1319 | 21365 |
BES PL Equity | Banco Espirito Santo-Reg | Portugal | 2197 | 9858 |
BNP FP Equity | Bnp Paribas | France | 32331 | 205348 |
BPE IM Equity | Banca Popol Emilia Romagna | Italy | 2012 | 11647 |
BPSO IM Equity | Banca Popolare Di Sondrio | Italy | 1662 | 3014 |
CBK GY Equity | Commerzbank Ag | Germany | 8688 | 58255 |
CRG IM Equity | Banca Carige Spa | Italy | 2564 | 6177 |
CSGN VX Equity | Credit Suisse Group Ag-Reg | Switzerland | 26473 | 50700 |
DANSKE DC Equity | Danske Bank A/S | Denmark | 66433 | 21536 |
DBK GY Equity | Deutsche Bank Ag-Registered | Germany | 22308 | 101694 |
DNBNOR NO Equity | Dnb Nor Asa | Norway | 91783 | 13124 |
ETE GA Equity | National Bank of Greece | Greece | 2601 | 36588 |
GLE FP Equity | Societe Generale | France | 13015 | 160704 |
HSBA LN Equity | Hsbc Holdings Plc | Britain | 88135 | 302327 |
ISP IM Equity | Intesa Sanpaolo | Italy | 17440 | 101169 |
KBC BB Equity | Kbc Groep Nv | Belgium | 5111 | 52949 |
LLOY LN Equity | Lloyds Banking Group Plc | Britain | 24014 | 103859 |
MB IM Equity | Mediobanca Spa | Italy | 4917 | 2567 |
PMI IM Equity | Banca Popolare Di Milano | Italy | 589 | 8570 |
POH1S FH Equity | Pohjola Bank Plc-A Shs | Finland | 2366 | 3083 |
RBS LN Equity | Royal Bank of Scotland Group | Britain | 25716 | 148300 |
SEBA SS Equity | Skandinaviska Enskilda Ban-A | Sweden | 78698 | 17576 |
SHBA SS Equity | Svenska Handelsbanken-A Shs | Sweden | 100891 | 11078 |
STAN LN Equity | Standard Chartered Plc | Britain | 30144 | 85231 |
SWEDA SS Equity | Swedbank Ab—A Shares | Sweden | 81943 | 17008 |
SYDB DC Equity | Sydbank A/S | Denmark | 6980 | 2274 |
TPEIR GA Equity | Piraeus Bank S.A. | Greece | 560 | 13135 |
UBSN VX Equity | Ubs Ag-Reg | Switzerland | 39393 | 65707 |
UCG IM Equity | Unicredit Spa | Italy | 13621 | 160562 |
This table shows the insurance companies and banks included in the sample. Displayed market cap values and number of employees are recorded as of end August 2011.
In the CES analysis, it is necessary to identify the exact dates at which markets are deemed to be stressed. Two different indices are used for this purpose. One is the German equity market index, the DAX, and the other is a US market index, the Dow Jones Industrial Average. These two indices are hence used as the conditioning information set in the calculation of (
Two sets of results are produced on the basis of the analysis framework outlined in Section
Results—CES for insurance and banking.
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Insurance | −3.83 | −3.17 | −2.48 | −2.17 | −2.02 | |
Average | Banking | −3.81 | −3.21 | −2.55 | −2.19 | −2.03 |
Difference | −0.02 | 0.04 | 0.07 | 0.02 | 0.01 | |
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Insurance | −6.99 | −5.62 | −4.20 | −3.59 | −3.37 | |
Minimum | Banking | −6.18 | −4.78 | −3.91 | −3.30 | −2.99 |
Difference | −0.81 | −0.84 | −0.28 | −0.29 | −0.38 | |
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Insurance | −0.60 | −0.68 | −0.62 | −0.62 | −0.58 | |
Maximum | Banking | −0.61 | −0.64 | −0.54 | −0.57 | −0.59 |
Difference | 0.01 | −0.04 | −0.08 | −0.05 | 0.01 | |
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Insurance | −3.99 | −3.3 | −2.58 | −2.25 | −2.09 | |
Average worst 20 | Banking | −4.62 | −3.91 | −3.13 | −2.71 | −2.47 |
Difference | 0.63 | 0.61 | 0.55 | 0.46 | 0.38 | |
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Insurance | −5.19 | −4.23 | −3.24 | −2.82 | −2.64 | |
Average worst 10 | Banking | −5.25 | −4.41 | −3.54 | −3.03 | −2.76 |
Difference | 0.06 | 0.18 | 0.3 | 0.21 | 0.13 | |
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Insurance | −5.88 | −4.74 | −3.60 | −3.14 | −2.97 | |
Average worst 5 | Banking | −5.63 | −4.58 | −3.74 | −3.19 | −2.90 |
Difference | −0.26 | −0.16 | 0.14 | 0.05 | −0.07 | |
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Insurance | −5.59 | −4.39 | −3.76 | −3.35 | −3.11 | |
Average | Banking | −5.02 | −3.86 | −3.32 | −2.95 | −2.75 |
Difference | −0.57 | −0.53 | −0.44 | −0.40 | −0.37 | |
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Insurance | −9.67 | −7.55 | −6.11 | −5.25 | −4.93 | |
Minimum | Banking | −7.80 | −6.36 | −5.45 | −4.88 | −4.47 |
Difference | −1.88 | −1.19 | −0.66 | −0.37 | −0.46 | |
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Insurance | −1.37 | −0.93 | −0.82 | −0.74 | −0.67 | |
Maximum | Banking | −1.55 | −0.98 | −0.81 | −0.78 | −0.67 |
Difference | 0.17 | 0.05 | −0.01 | 0.03 | 0.00 | |
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Insurance | −5.80 | −4.56 | −3.91 | −3.48 | −3.24 | |
Average worst 20 | Banking | −6.12 | −4.74 | −4.08 | −3.60 | −3.37 |
Difference | 0.32 | 0.18 | 0.18 | 0.12 | 0.13 | |
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Insurance | −7.42 | −5.97 | −5.03 | −4.48 | −4.15 | |
Average worst 10 | Banking | −6.99 | −5.49 | −4.68 | −4.14 | −3.84 |
Difference | −0.43 | −0.48 | −0.35 | −0.35 | −0.31 | |
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Insurance | −8.13 | −6.49 | −5.48 | −4.85 | −4.53 | |
Average worst 5 | Banking | −7.44 | −5.96 | −5.04 | −4.48 | −4.15 |
Difference | −0.69 | −0.53 | −0.43 | −0.37 | −0.38 |
This table shows summary statistics: average, minimum, maximum, and average CES for portfolios containing the 20, 10, and 5 worst entities ranked according to their individual CES figures. The CES is calculated according to (
Table
A casual look at the calculated differences between summary statistics for insurance and banks clearly indicates that there is no material economical difference between these two groups of financial firms. This is true across all produced summary statistics, and across all confidence levels, as well as for both of the used market indices. It is noted that conditional tail loss are smaller when using the DOW as a conditioning information set, as compared to the DAX. However, this result is consistent for both insurance companies and banks.
As shown in the first line of the two panels contained in Table
It could (perhaps) be argued, since the sample of banks is larger, measured by the total number of included firms, compared to the sample of insurance companies (The former containing thirty one entities and the latter twenty one entities.), that the banking results are muted by a higher degree of “portfolio” diversification effects, compared to the insurance results, and that this distorts the reported average CES figures. However, this is not the case. When looking at portfolio results including the same number of entities, the referred conclusion remain unchanged. Table
In summary, the results displayed in Table
The Granger-causality between all pairs of banks and insurance companies are tested at a
Granger causality between banks and insurance—99% confidence level.
Bank |
Aegon | Ageas | Allianz | Amlin | Generali | Aviva | Axa | Fondiaria | ING | Jardine Lloyd | Legal & General | Muenchener Re | RSA Insurance | Sampo | Scor | Storebrand | Swiss Life Holding | Swiss Re | Topdanmark | Vienna Insurance | Zurich Fin. |
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Alpha Bank |
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Banca Carige |
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Banca Pop. Emi. Romagna |
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Banca Pop. Di Milano |
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Banca Pop. Di Sondrio |
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Banco Com. Portugues |
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Banco Espirito Santo |
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Barclays |
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Bnp Paribas |
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Commerzbank |
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Credit Suisse Group |
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Danske Bank |
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Deutsche Bank |
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Dnb Nor Asa |
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Kbc Groep |
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Nat. Bank of Greece |
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Piraeus Bank |
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RBS |
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Skandinaviska Enskilda |
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Societe Generale |
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Standard Chartered |
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Swedbank |
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Sydbank |
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UBS |
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Unicredit |
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This table shows the direction of Granger-causality between banks and insurance companies measured at a 99% level of confidence. The calculations follow the setup presented in Section
While the business models and the core functions performed by insurance companies and banks in the economy are very different, this paper concludes that according to the trading behaviour of European equity markets, insurance companies and banks are tightly interconnected and equally systemically important.
An empirical analysis is presented of the conditional expected shortfall (CES) of European insurance companies and banks. Daily equity return data covering the period from January 1995 to August 2011 form the basis of the analysis. Expected shortfalls are calculated for individual firms, conditional on the worst trading-day returns of the Dow Jones Industrial Average (DOW) and the German Equity Index (DAX). Summary statistics for different portfolio compositions of insurance companies and banks fail to document any material difference between the equity market trading of insurance companies and banks, when markets are under stress. The performed Granger-causality tests on all pairs of banks and insurance companies fail to detect any systematic structure of one particular group Granger-causing the other. One might have conjectured at the outset that there would be a greater propensity for banks to Granger-cause insurance company equity movements. However, this conjecture is not supported by the empirical results. In fact, measured at a 99% confidence level, there are equally many instances of banks being Granger-caused by insurance comapnies, as there are instances of insurance companies being Granger-caused by banks.
The views and opinions expressed in this paper are of the author, and they do not necessarily reflect those of the The European Insurance and Occupational Pensions Authority. The author would like to thank the anonymous referee, Casper Christophersen, Melle Bijlsma, Roberto Buzzi, and Jarl Kure for providing comments on earlier drafts. Any errors are naturally of the author.