Assessing Systemic Risk of the European Insurance Industry

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Assessing Systemic Risk of the European Insurance Industry Elia Berdin 29, Matteo Sottocornola 30 Abstract This paper investigates the systemic relevance of the insurance industry. We do it by analysing
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Assessing Systemic Risk of the European Insurance Industry Elia Berdin 29, Matteo Sottocornola 30 Abstract This paper investigates the systemic relevance of the insurance industry. We do it by analysing the systemic contribution of the insurance industry vis-á-vis other industries by applying three measures, namely the linear Granger causality test, conditional value at risk and marginal expected shortfall, to three groups, namely banks, insurers and non-financial companies listed in Europe over the last 14 years. Our evidence suggests that the insurance industry shows i) a persistent systemic relevance over time, ii) it plays a subordinate role in causing systemic risk compared to banks. In addition, iii) we do not find clear evidence on the higher systemic relevance of SIFI insurers compared to non-sifis. The content of this study does not reflect the opinion of EIOPA. Responsibility for the information and the views expressed therein lies entirely with the authors. 1. Introduction Following the financial crises and the European sovereign debt crises, the interest around systemic risk has become increasingly relevant. 31 After the collapse of Lehman Brothers in particular, the debate on systemic risk has been primarily focused on banks. However, recent empirical evidence suggests that institutions not traditionally associated with systemic risk, such as insurance companies, also play a prominent role in posing it. In particular, some authors find that the insurance industry has become a non-negligible source of systemic risk (e.g. Billio et al. (2012) and Weiß and Mühlnickel (2014)). This is partially in contrast to other authors, who do not find evidence of systemic relevance for the industry as a whole (e.g. Harrington (2009), Bell and Keller (2009) and the Geneva Association 29 International Center for Insurance Regulation and Center of Excellence SAFE Sustainable Architecture for Finance in Europe, Goethe University Frankfurt, 30 EIOPA and Center of Excellence SAFE Sustainable Architecture for Finance in Europe, Goethe University Frankfurt, 31 Throughout this paper, we rely on the definition of systemic risk given by The Group of Ten (2001): Systemic risk is the risk that an event will trigger a loss of economic value or confidence in a substantial segment of the financial system that is serious enough to have significant adverse effects on the real economy with high probability. 57 (2010)). Finally, other authors take a more granular perspective and argue that insurance companies might be systemically relevant, but that such risk stems from non-traditional (banking-related) activities (Baluch et al. (2011) and Cummins and Weiss (2014)) and that in general, the systemic relevance of the insurance industry as a whole is still subordinated with respect to the banking industry (Chen et al. (2013)). As the current literature does not provide a common understanding and clear evidence regarding the systemic relevance of the insurance industry, we aim with this paper to fill this gap by empirically investigating its systemic relevance vis-á-vis other industries. To do so, we test three equity return-based measures of systemic risk, namely 1) the indexes based on linear Granger causality tests proposed by Billio et al. (2012) (Granger test), 2) the Conditional Value at Risk proposed by Adrian and Brunnermeier (2011) (CoVaR) and 3) the Dynamic Marginal Expected Shortfall proposed by Brownlees and Engle (2012) (DMES), on 3 groups: banks, insurers and non-financial companies, all listed in Europe. We test the systemic relevance of each institution with respect to the total system intended as the sum of the companies included in the 3 groups. Based on these estimations, we rank financial institutions according to their average systemic risk contribution over time and create an industry composition index. Our evidence suggests that the insurance industry tends i) to persistently pose systemic risk over time and ii) to play a subordinate role with respect to the banking industry with some distinction in specific periods when the insurance industry becomes more systemic than the banking industry. The paper is organized as follows: section 2 provides a comprehensive literature review, section 3 describes the methodology, section 4 the data; section 5 describes the results and section 6 concludes the analysis. 2. Literature review The literature on systemic risk has been steadily growing following the crises. In particular, a wide range of new methodologies for testing the systemic contribution of financial institutions has been proposed. Moreover, both academia and regulators have dedicated more attention to the role of non-banking financial institutions: among 58 these institutions, insurance companies emerged as a potential source of systemic risk. 32 Before the crisis, there was substantial agreement among scholars in considering the insurance industry to be not systemically relevant. However, in the literature that emerged in the aftermath of the crisis, although many studies still consider the insurance industry non-systemically relevant as a whole, a clear-cut indication does not emerge anymore. As a matter of fact, looking at the evidences stemming from market based data that rely on the assumption that prices reflect all the necessary information 33, substantial differences in the evaluation of the insurance industry emerge. For instance, Acharya et al. (2010) argue that insurance companies are overall the least systemically relevant financial institutions. The authors provide estimations of the spillover effects through a measure of conditional capital shortfall, i.e. Systemic Expected Shortfall (SES) and Marginal Expected Shortfall (MES) for the US financial industry during the crises. The contribution of Adrian and Brunnermeier (2011) extends the traditional value at risk concept to the entire financial system conditional on institutions being in distress (ΔCoVaR). The authors apply the measure to a set of institutions, including banks and thrifts, investment banks, government sponsored enterprises and insurance companies and find no distinction between the systemic relevance of different types of institutions. By contrast, Billio et al. (2012) apply the linear and non-linear Granger causality test to a sample of banks, insurers, hedge funds and broker dealers operating in the U.S. in order to establish pairwise Granger causality among equity returns of financial institutions. Their evidence suggests that during the 2008 financial crisis, besides banks, insurance companies were a major source of systemic risk. This conclusion is partially in contrast to Chen et al. (2013): the authors agree that the linear Granger causality test attributes to insurance companies a systemic relevance comparable with the systemic relevance of banks. However, they argue that when applying a linear and non-linear Granger causality test to the same series corrected for heteroscedasticity, banks tend to cause more systemic risk and for longer periods of time then insurance companies. 32 A comprehensive review of the literature on systemic risk in the insurance industry is provided by Eling and Pankoke (2012). 33 A comprehensive review of the models applied to systemic risk is provided by Bisias et al. (2012). 59 Both theoretical and empirical research that take into consideration fundamentals of the insurance industry provide ambiguous indications about the systemic relevance of insurers. Even though the common understanding classifies the insurance industry as not systemically relevant, distinctions mainly driven by the engagement in specific business lines emerge. The Geneva Association (2010) conducts an analysis on the role played by insurers during the 2008 crisis and argues that the substantial differences between banks and insurance companies, namely the long-term liability structure of insurers compared to banks and the strong cash flow granted by the inversion of the cycle, is sufficient to rule out any systemically implications of the insurance industry during the financial crises aside from the companies highly exposed towards non-core insurance activities. The higher systemic relevance of non-traditional versus traditional insurance activities is analysed by other authors such as Bell and Keller (2009) who investigate the relevant risk factors stemming from an insurance company, or Cummins and Weiss (2014) who analyse primary indicators and contributing factors. More specifically, Cummins and Weiss (2014) add a further distinction to the dichotomy between traditional and non-traditional activities, namely the higher systemic relevance of traditional life compared to the P&C business: this is mainly driven by the higher leverage, interconnectedness and exposure to credit, market and liquidity risk. Similar conclusions are reached by Baluch et al. (2011), who find that the fundamental reason behind the systemic relevance of the bank-like business type is due to the massive amount of interconnectedness, and by Harrington (2009) who concludes that systemic risk is potentially higher for life insurers due to the higher leverage, sensitivity to asset value decline and potential policyholder withdrawals during a financial crisis. An additional strand of research based both on market and accounting data tend to confirm the difficulties in defining the insurance industry as systemically relevant. Weiß and Mühlnickel (2014) estimate the systemic risk contribution based on CoVaR and MES for a sample of US Insurers during the crisis, inferring that insurers that were most exposed to systemic risk were on average larger, relied more heavily on non-policy holder liabilities and had higher ratios of investment income to net revenues. Weiss et al. (2014) analyse a much broader sample of insurers over a longer time horizon and find that the systemic risk contribution of the insurance sector is relatively small. However, they also argue that the contribution of insurers to systemic risk peaked during the financial crisis and find that the 60 interconnectedness of large insurers with the insurance industry is a significant driver of the insurers exposure to systemic risk. Finally, they argue that the contribution of insurers to systemic risk appears to be primarily driven by leverage, loss ratios and funding fragility. It is also worth noting that an ambiguous position is attributed to reinsurance companies: studies by Swiss Re (2003) and by The Group of Thirty (2006) exclude any systemic relevance for the reinsurance business. However Cummins and Weiss (2014) claim that, despite historical evidence, both life and P&C insurers are indeed exposed to reinsurance crises. In conclusion, the existing literature provides a diversified and controversial picture of the systemic relevance of the insurance industry. On the one hand, some studies argue that due to its nature, the insurance industry does not pose systemic risk; on the other hand, some studies provide evidence on the role of the insurance industry in posing systemic risk and its growing importance in recent years, particularly driven by the engagement of insurers in nontraditional activities. Moreover the position of reinsurers appears unclear. This paper, shed further light on the systemic relevance of the European insurance industry compared to other industries, namely banks and non-financial institution. Moreover, we aim at assessing the contribution to the riskiness of the whole system of the systemically important vis-á-vis non-systemically important insurance companies. 3. Methodology In order to compare the systemic relevance of the insurance industry with the systemic relevance of other industries we define three groups, namely banks, insurers and non-financials and apply to them three widely used equity-based measures of systemic risk: 1) the Granger causality test proposed by Billio et al. (2012), 2) the ΔCoVaR proposed by Adrian and Brunnermeier (2011) and 3) the DMES proposed by Brownlees and Engle (2012). 34 The literature proposes several equity-based models to assess the systemic relevance of institutions, anyhow no consensus among academia has been found on the best approach. We thus opted for the mentioned three due to i) their diffusion (many central banks and regulators apply these models), ii) their robustness (the models have been thoroughly discussed and challenged both in academia and industry, and 34 An extensive mathematical treatment of the three measures is provided in Appendix A.1. 61 finally iii) our willing to approach the measurement of the systemic relevance of an industry by different perspectives. The three systemic risk measures tend to capture different phenomena and therefore need to be correctly interpreted. The Granger causality test is a measure that allows us to quantify the degree of connectedness of an institution vis-á-vis a system of institutions. By creating a network of pairwise statistical relations, we do not only observe the amount of interdependence, but also the direction thereof. The measure is thus a good proxy for an analysis at an aggregate level (for example industry or other clusters), but its estimation could become cumbersome when the objective is to test the individual interconnection with respect to a system of institutions as proxy for the market. 35 The ΔCoVaR measures the difference between the CoVaR conditional on the distress of an institution, i.e. the value-at-risk of the system conditional on an institution being in distress, and the CoVaR conditional on the normal state of the institution. It is therefore able to capture the marginal contribution of a particular institution to the overall systemic risk. Finally, the DMES measures, in a dynamic setting, the expected drop in equity value of an institution when the system is in distress. It is worth mentioning that this is not a direct measure of systemic risk, but is highly related to it. The contribution of Brownlees and Engle (2012) originates from the proposal of Acharya et al. (2010), in which the marginal expected shortfall of an institution is coupled with its leverage to originate the Systemic Expected Shortfall (SES). SES measures the expected capital shortage of an individual firm conditional on a substantial reduction in the capitalization of the system. Brownlees and Engle propose a similar measure called SRISK, which is based on a dynamic estimation of the Marginal Expected Shortfall (MES) and leverage ratios. A major advantage of such a contribution is its ability to capture time-varying effects, effects which are not observable in the framework of Acharya et al. (2010). However, both measures rely on the estimation of the MES and of pre-determined leverage ratios: in order to avoid additional assumptions that might cast doubts on the reliability of the estimation within the insurance industry, 36 we simply rely on the directly observable part of the 35 By market, we essentially mean a broad measure and proxy for the (real) economic activity such as a major stock index. Throughout the paper, we therefore interchangeably use the terms system and market as (almost) perfect substitutes. 36 However, it is worth noting that Brownlees and Engle (2012) provide a series of robustness checks on the stability of the parametrization of the SRISK measure. 62 measure, i.e. the DMES, which is sufficient to provide information on the individual fragility of the individual institution with respect to market tail events, which in turn have potential systemic implications. 37 In addition, for each systemic risk measure and for each group, we compute the average contribution of the individual institution towards the total system composed by the three groups. 38 We then calculate the average contribution of each industry by taking the median of the month (for the ΔCoVaR and the DMES, whereas the Granger causality test is calculated on a monthly basis) and the average through the institutions of the same industry. 39 Finally, at each point in time, we rank the institutions systemic relevance with respect to the total system from the most to the least systemically relevant according to each measure. We then select the top ten institutions at each point in time and calculate the relative weight of each industry within the top ten over time, thereby creating three indexes. Finally, we group all three indexes and form the Industry Composition Index displaying in percentage the top ranked institutions by industry. 4. Data The data set for the industry analysis consists of equity returns of 60 companies listed in Europe over a time window of 14 years, from January 1999 to December 2013, which is 17 years (i.e. from January 1996 to December 2013) for the Granger causality test due to the lag on the series. 40 For each control group, we select the top 20 institutions in terms of capitalization from STOXX Euro 600 Banks, STOXX Euro 600 Insurance and STOXX Europe 600 for banks, insurers and non-financials respectively. 41 Table 1, displayed in Annex A.3, reports the list of the selected institutions for each group. 37 Another major issue we face regarding the estimation of the SRISK is the frequency of the accounting data: since we focus on European insurers, we do not possess sufficiently long quarterly series of balance sheet data. 38 An extensive mathematical explanation of how the three cases are calculated is provided in Appendix A.1 39 A formal representation of the index's construction is provided in Appendix A.3 40 Data was downloaded from Datastream 41 Within each control group, companies are ranked according to the yearly average market capitalization over the 14- year time frame. We selected those companies which were continuously listed over the period. The list of the companies included in each group is reported in Appendix A.3 63 Data were collected both at daily and monthly frequencies. To calculate the ΔCoVaR, we rely on a set of state variables as proposed in Adrian and Brunnermeier (2011), namely i) Market volatility (VIX for Europe), ii) Liquidity spread (3M Repo - 3M Bubill), iii)} change in the short-term interest rate (3M Bubill), iv) the slope of the yield curve (10Y Bund - 3M Bubill), v) credit spread (BAA 5-7Y Corporate (Bank of America) - EURO Sovereign 5-7Y (Barclays)), vi) market returns (STOXX EURO 600 All shares). 5. Empirical results 5.1. The Granger causality test (Billio et al., 2012) (a) Full insurance group (b) Insurance group split into SIFI and Non-SIFI Figure 1: Total cause connections towards total system. The figure displays for each group the number of significant cause and receives linear Granger causality connections over the total number of possible cause and receive connections. The statistical significance level is set at 5%. Results are calculated using Newey West standard errors. Figure 5.1a above reports the evolution over time of the total number of causing (Granger-causal) significant connections over the total number of possible connections from each group towards the total system. During the pre-crisis period the measure reports a generalized decrease in the connectivity level across the three groups: particularly in the period from 1999 to the end of 2004, the level of connectivity goes from roughly 20%-25% to 10%-15%. Starting form 2005 the graph shows a general increase of the significant connections that move to average values of 20%-25% with peaks of 35% in the beginning of 2007 and Looking at the single curves it is worth noting how during tranquil periods, namely in a low level relevant connection environment, the non-financial sector tends to play a more active role in comparison to the financial sector. The opposite occurs when the financial crises approach: financial companies almost doubled the number of relevant causing connection. As a matter of fact, starting from 2008 when Lehman Brothers filed for bankruptcy and Ame
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