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SYSTEMIC RISK AND THE INTERCONNECTEDNESS BETWEEN BANKS AND INSURERS: AN ECONOMETRIC ANALYSIS
... Our dataset represents the stocks issued by companies from the insurance, banking, basic materials, consumer goods, consumer services, health care, industrials, oil and gas, technology, telecommunications, and utilities sectors. As we want our sample to be relatively homogeneous and available over the long term, we use daily prices from 16 Our market selection represented more than 98% of the world (listed) insurance assets and sales in 1996 (see Table 3.1). Unsurprisingly, this proportion fell to 80-85% in 2017 due to the emergence of large insurance companies in emerging markets. ...
... For ease of reading, we smooth the series using three-year moving averages of the interconnectedness measure. 16 All statistical tests are applied to the original (unsmoothed) series. We decompose the interconnectedness measure into the percentage explained by each systematic risk factor, namely, the global, regional, and local economic factors, as well as the regional and global industry factors. ...
... We decompose the interconnectedness measure into the percentage explained by each systematic risk factor, namely, the global, regional, and local economic factors, as well as the regional and global industry factors. As indicated in Table 3.6, the total interconnectedness 16 The size of the moving average is arbitrary. However, we control and confirm that the use of one-year or five-year moving averages does not change the pattern of the series over the long run. ...
This thesis studies the long-term evolution and the main determinants of stock market comovements. These issues are of interest for investors and regulators, as stock market interdependencies (i) are a key element of the benefits of international portfolio diversification and (ii) can affect financial stability by facilitating the spread of shocks between countries. Our contributions to the literature are both methodological and empirical. First, we develop diversification measures that shed new light on the evolution of international diversification benefits. Second, we study the long-term effect of globalization on the rise in international stock return comovements over the past four decades. Finally, we examine insurers’ interconnectedness, which has become a key concern for the macroprudential supervision of the sector.
... Thus, we have found that some authors split total default risk premia into an idiosyncratic and a systematic component (see Chan-Lau (2006), Berndt and Obreja (2007) In this paper, we support the idea of systemic risk as the potential for multiple, simultaneous defaults of major financial institutions (i.e. Chen et al. (2014)) and review the related (in one way or another) literature. Chan-Lau (2006) finds that although the simplest proxy for systemic default risk is the spread of a credit derivatives index, such an index also reacts to idiosyncratic default risk changes. ...
... November 2009, finding that regulators searching for reliable systemic risk indicators should stick to simple, robust indicators based on credit derivatives and market data interest rates. Along this line, Chen et al. (2014) use CDS spreads and stock prices to create a robust systemic risk measure for the insurance sector that investigates the interconnectedness between the banking and insurance industries during the financial crisis. These authors find evidence of significant bidirectional causality, although they state that the impact of banks on insurers is stronger and of longer duration and that although the core activities of insurers are not a significant source of systemic risk, banking functions such as derivatives trading are. ...
... Systemic risk is often triggered by financial institutions that are either "too big to fail" or "too interconnected to fail" (Chen et al., 2014) From an approach more closely related to the contagion issue, as mentioned above, Yang and Zhou (2013) study the role of credit-risk transfers among financial institutions that might be considered "too big to fail," finding that financial institutions that are prime senders or exchange centres of credit-risk information might be systemically important financial institutions (SIFIs); they design and deploy macro-prudential regulation by identifying SIFIs and their connectedness with other financial institutions. ...
In this paper we pay tribute to one of the most successful financial innovations in recent times: the Credit Default Swap (CDS). Through a literature review on financial risks from 2000-2015 we develop a conceptual map to assess the importance and evolution of the CDS, along with the consequences of its use. CDSs emerge as a powerful and meaningful financial instrument. Given the CDS’s versatility, the 21st-century literature about the CDS and its usefulness is very extensive, rendering the CDS a valuable guide with which to investigate the financial risks that have worried researchers, regulators and all of the participants in the financial system.
... in 2009Q1, it did fall below fed funds rate, but it went back above it again in 2009Q2 and has remained above it till the present time. Dutkowsky and VanHoose, 2017 (Tarashev et al., 2010a(Tarashev et al., , 2013and Cummins, 2014). To this end, we introduce a bank-level insolvency indicator, distress indicator, as a probability of default. ...
... This metric can encompass all or any subset of firms in the system and it can be allocated across firms using attribution methods (Tarashev et al. (2010a)). Some papers in this strand propose a simple "systemic risk tax", an insurance premium, to be paid by large banks that would restore an optimal level of risk-taking (Huang et al., 2012 andCummins, 2014). ...
... The expected shortfall (ES) is a standard measure of firm-level risk that refers to portfolio credit losses under extreme conditions and the capital needed to offset those losses. One stream of research uses ES to create a metric of systemic risk by treating the financial system as a portfolio of firms (Acharya et al., 2010;V 2017;Cummins, 2014). For a given firm, ES measures the potential loss it incurs in an extreme event. ...
We investigate the association between deviations of the monetary policy rate from its benchmark, and systemic risk between 2001 and 2017. We adopt an impulse response function framework that uses the local projections method model proposed by Jorda (2005). We find that paying interest on reserves by the Fed beginning in 2008 introduced a monetary policy regime shift between the period that the Fed did not pay interest on reserves and the period that it did. Consequently, while we identify a positive and significant link between deviations of the policy rate from its benchmark and systemic risk in the former period, this link was broken in the latter period. During the former period, upsurges in the fed funds rate raised bank costs and increased bank distress. In contrast, during the latter period, interest payment on reserves exceeded the policy rate, except for 2009Q1, and as a result, banks did not expand lending in response to the Fed's reserve injections, instead, holding large amounts of excess reserves. This practice produced greater bank profitability and reduced bank liquidity risk and credit risk, without increasing systemic risk.
... Dergiades, Martinopoulos, and Tsoulfidis (2013) used the HJ test and the DP test to investigate the nonlinear Granger causality between aggregate energy consumption and economic growth. Chen, Cummins, and Viswanathan (2014) employed the HJ test and the DP test to explore the nonlinear Granger causality between the banking industry and the insurance industry. Ajmi et al. (2015) investigated the nonlinear causal relation between exports and economic growth in South Africa by using the HJ test and DP test. ...
... While Diks and Panchenko (2006) argued that in certain situations, the HJ test could overreject the null hypothesis of no Granger causality, so they proposed the DP test. In order to obtain robust result of nonlinear relationship, many studies (such as Dergiades, Martinopoulos, and Tsoulfidis 2013;Chen, Cummins, and Viswanathan 2014;Ajmi et al, 2015) employed the aforementioned two tests at the same time. Accordingly, we also apply the well-known HJ test and the recent DP test to investigate nonlinear causality between crude oil prices and USD exchange rate. ...
... The first test for this purpose is the most commonly used HP test proposed by Hiemstra and Jones (1994), and the second test is the not frequently used DP test presented in Diks and Panchenko (2006). The motivation to choose the DP test is that under certain variance conditions, it corrects for the over-rejection observed in the HJ test and can be adopted as a robustness check (Chen, Cummins, and Viswanathan 2014). ...
This article examines the nonlinear Granger causality and time-varying influence between crude oil prices and the US dollar (USD) exchange rate using the Hiemstra and Jones (HP) test, the Diks and Panchenko (DP) test and the time-varying parameter structural vector autoregression model. By applying the iterated cumulative sums of squares (ICSS) algorithm and the DCC-GARCH model, the effects of structural breaks in volatility of the two markets are also investigated. The empirical analysis indicates that, first, crude oil prices are the nonlinear Granger-cause of the USD exchange rate, but not vice versa. Second, the USD exchange rate exerts a stronger and more stable negative influence on crude oil prices in the short term, and the influence gradually weakens after 2012. Finally, ignoring structural breaks can increase the negative volatility correlation between the oil and USD exchange rate markets, which is particularly remarkable during the financial crisis.
... Some empirical papers support the latter assumption, showing that banks tend to be more systemically relevant than insurance companies within the financial system (Chen et al., 2014;Elyasiani et al., 2015;Geraci and Gnabo, 2018). However, as far as we know, the linkages between insurers and non-financial companies remain unexplored. ...
... Previous studies find mixed results with regard to this question. Billio et al. (2012) and Chen et al. (2014) show that insurance companies are, in general, a nonnegligible source of systemic risk. By contrast, Weiß and Mühlnickel (2014) underline that the size of insurance companies helps determine their exposure and contribution to the risk of the financial system. ...
This paper examines the long‐term evolution of the linkages of the insurance sector with financial and nonfinancial companies. We develop a measure of connectedness using a multifactor model of weekly equity returns. The empirical analysis is conducted from 1973 to 2018, for 16 developed countries, at both the sectoral and institution levels. The results indicate that, unlike other sectors, the connectedness level of the insurance industry has strengthened over time. We also find that the linkages of the largest insurance companies with financial and nonfinancial firms are structurally different but as high as those of the largest banks.
... Likewise, insurance companies hold a high percentage of sovereign bonds in their portfolios in addition to bank debt holdings. The transmission of risk through the sovereign and bank debt channels to the insurance companies has been documented in earlier studies (Bernoth and Pick 2011;Hammoudeh et al. 2011;Chen et al. 2014). In addition, when holding a large portion of sovereign debt in their balance sheets, insurance companies may be vulnerable to the price movements of sovereign bonds that will directly affect Content courtesy of Springer Nature, terms of use apply. ...
... Many scholars investigate the performance and risk of European banks and insurance companies during the global financial crisis of -2009(Chen et al. 2014Cummins and Weiss 2014;Kaserer and Klein 2019). These studies indicate that the observation that both types of FIs have suffered from the financial crisis can be partially explained by a marked increase in correlation between banking and insurance equity. ...
This study investigates the relationship between board risk oversight practices at financial institutions in the EU and systemic risk during the sovereign debt crisis. More specifically, we examine whether European banks and insurance companies that had strong board-level risk oversight in place before the onset of the sovereign debt crisis fared better during the crisis. We construct a risk oversight index based on publicly available, hand-collected data, which captures the strength of the institutions’ board-level risk governance practices. We find that financial institutions with stronger board risk oversight prior to the crisis were less vulnerable to the sovereign debt crisis, after controlling for other factors. The results are consistent and economically relevant for SRISK, probability of default, and Delta-CoVaR, three measures of systemic risk that are commonly used in the context of financial institutions.
... Thus, heavy tailedness and interconnectedness are two features of extreme risks, which can cause sever systemic risk such as the global financial crisis of [2007][2008][2009]. Much research has been devoted to the study of the systemic risks and the lessons from the financial crisis; see e.g., Gorton (2008), Huang et al. (2009), Huang et al. (2012), Chen et al. (2014), and Rivera-Escobar et al. (2022). It is crucial for financial institutions to manage these extreme risks. ...
Heavy tailedness and interconnectedness widely exist in stock returns and large insurance claims, which contributes to huge losses for financial institutions. Diversification ratio (DR) measures the degree of diversification using the Value-at-Risk, which is known to capture extreme risks better than variance. The portfolio optimization strategy based on DR maximizes the effect of diversification for extreme risks. In this paper, we empirically examine the DR strategy by using more than 350 S&P 500 stocks under the assumption that the stock losses are modeled with a flexible multivariate heavy-tailed model. This assumption is verified empirically. The performance of DR strategy is compared with four benchmark strategies: equally weighted portfolio, minimum-variance portfolio, extreme risk index portfolio, and most diversified portfolio. The performance of comparison includes annualized portfolio return, modified Sharpe ratio, maximum drawdown, portfolio concentration, portfolio turnover, and the degree of diversification. DR outperforms other strategies. In particular, DR shows the highest return and maintains the highest level of diversification during the global financial crisis of 2007–2009.
... Before that, researchers seemed to be convinced that the insurance market is systemically irrelevant. After the crisis, some of them upheld their point of view: (Harrington 2009;Bell and Keller 2009;Association 2010;Bednarczyk 2013), while others published papers indicating that systemic risk may be created by the insurance sector: (Billio et al. 2012;Weiß and Mühlnickel 2014;Baluch et al. 2011;Cummins and Weiss 2014;Chen et al. 2013;Czerwińska 2014). In Bierth et al. (2015) basing on the study of a large number of insurers in a long timescale, the authors come to the conclusion that the contribution of the insurance sector to systemic risk is relatively small and its peak was reached during the financial crisis of the years 2007-2008. ...
This paper is part of the research on the interlinkages between insurers and their contribution to systemic risk on the insurance market. Our work constitutes an answer to the recommendations contained in the 2017 report of the European Insurance and Occupational Pensions Authority (EIOPA), an independent EU advisory body to the European Parliament, the Council of Europe and the European Commission, which shows that when analysing systemic risk in the insurance sector, one should take into account, among others, the dynamics of interconnectedness between institutions. The present article is another study of the authors in this subject. Its main purpose is to present the results of the analysis of linkage dynamics and systemic risk in the European insurance sector using hybrid models combining statistical-econometric tools with network modelling and predictive analysis tools. These networks are based on dynamic dependence structures modelled using a copula. Then, we determine the Minimum Spanning Trees. Finally, the linkage dynamics is described by means of the time series of selected topological network indicators.
... Billio et al., 2012;Weiß and Mühlnickel, 2014) and those in which they claim that they can be systematically significant, but this is due to their non-traditional (banking) activities (e.g. Baluch et al., 2011;Cummins and Weiss, 2014) and the overall systemic importance of the insurance sector as a whole is still subordinated to the banking sector (Chen et al., 2013). In turn, in (Bierth et al., 2015), the authors, after examining a very large sample of insurers in the long term, believe that the contribution of the insurance sector to systemic risk is relatively small, however, they claim that it reached its peak during the financial crisis in 2007−2008, which we also confirm in our analysis. ...
This work is an answer to the EIOPA paper “Systemic risk and macroprudential policy in insurance”. It follows from the latter that in order to assess the potential systemic risk (SR) we should take into account the build-up of risk and in particular the risk that arises in time, as well as the interlinkages in the financial sector and the whole economy. Our main tools used to analyse the systemic risk dynamics in the European insurance sector during the years 2005-2019 are the topological indices of minimum spanning trees (MST) and the deltaCoVaR measure. We address the question of the contribution to systemic risk of each of the 28 largest European insurance companies among which there are also those appearing on the G-SIIs list. Moreover, does the most important contribution to systemic risk come from the companies that have the highest betweenness centrality or the highest degree in the MST obtained?
... However -although many a study still supported the latter point of view -the recent literature offers several articles suggesting the possibility of the insurance market itself creating systemic risk. Let us quote here from a few articles whose authors claim that insurance companies: − generate systemic risk (Billio, Getmansky, Lo, & Pelizzon, 2012;Weiß & Mühlnickel, 2014), − can be systemically important when they conduct investment activities outside of their normal insurance busines (Baluch, Mutenga, & Parsons, 2011;Cummins & Weiss, 2014), while in general the systemic significance of the insurance sector as a whole is still subordinated to the banking sector (Chen et al., 2013;Czerwińska, 2014), − are systemically unimportant due to the low level of interconnections and the lack of strong dependence on external funding (Harrington, 2009;Bell, 2009;Keller, 2009;Geneva Association, 2010). ...
Objective: The objective of this article is to study the correlations between the most
important European insurers and their participation in systemic risk in the insurance
sector. We compare systemic risk in different market regimes.
Research Design & Methods: We use statistical clustering methods for time units
(weeks) to which we assign conditional variances obtained from the estimated Copula-
Dynamic Conditional Correlations-Generalised Auto-Regressive Conditional Heteroskedasticity
model (C-DCC-GARCH). In each of the identified market regimes we determine
the Conditional Value at Risk CoVaR systemic risk measure.
Findings: In this article we show a positive correlation of all the insurance companies
under consideration. During global market crises the correlation appears stronger than
in ‘normal times.’ This confirms that the insurance sector generates systemic risk in the
presence of turbulences on financial markets, since the value level of the compared
index CoVar is much higher in these conditions.
Implications & Recommendations: Our research confirms the insurance sector’s contribution
to Systemic Risk. Thus, it is important to develop an analysis of systemic risk
with a particular attention to the evolution of risk in time and the institutions' interconnectedness
in the context of contagion using also some new modelling tools.
Contribution & Value Added: A novel approach of this article is the analysis of dependencies
in the insurance sector using the C-DCC-GARCH model with taxonomic methods.
Article type: research article
Keywords: systemic risk; insurance market; Copula-DCC-GARCH(C-DCC-GARCH)
JEL codes: G22, C38, C32
... Many researchers analyze the problem of SR in the context of the failure of a significant part of the financial sector and reduction of credit availability, e.g., Acharya et al. (2011). Adrian and Brunnermeier (2011) investigate the negative impact on credit supply; Bach and Nguyen (2012), Rodríguez-Moreno and Peña (2013) financial system failure; Baur et al. (2003), Chen et al. (2013c), Weiss (2011, 2013), Weiß and Mühlnickel (2014) the negative impact on the real economy; Baluch et al. (2011) the chain reaction of financial difficulties; Chen et al. (2013b), Huang et al. (2009) many simultaneously defaulted pledges by large financial institutions; IAIS (2009), Jobst (2014), Radice (2010) the disruption of the flow of financial services, the negative impact on the real economy, and impairment of all or part of the financial system; Klein (2011) studies the market in the context of financial system instability, idiosyncratic events, and infection; Kress (2011) studies infection;Rodríguez-Moreno and Peña (2013) malfunctioning in the financial system and the negative impact on the real economy. In recent years, quantitative analysis of systemic risk using the described approaches has been carried out by, among others, Hautsch et al. (2015), Giglio et al. (2016), Benoit et al. (2017), Jajuga et al. (2017), Bégin et al. (2017), Jurkowska (2018). ...
In the present work, we analyze the dynamics of indirect connections between insurance companies that result from market price channels. In our analysis, we assume that the stock quotations of insurance companies reflect market sentiments, which constitute a very important systemic risk factor. Interlinkages between insurers and their dynamics have a direct impact on systemic risk contagion in the insurance sector. Herein, we propose a new hybrid approach to the analysis of interlinkages dynamics based on combining the copula-DCC-GARCH model and minimum spanning trees (MST). Using the copula-DCC-GARCH model, we determine the tail dependence coefficients. Then, for each analyzed period we construct MST based on these coefficients. The dynamics are analyzed by means of the time series of selected topological indicators of the MSTs in the years 2005–2019. The contribution to systemic risk of each institution is determined by analyzing the deltaCoVaR time series using the copula-DCC-GARCH model. Our empirical results show the usefulness of the proposed approach to the analysis of systemic risk (SR) in the insurance sector. The times series obtained from the proposed hybrid approach reflect the phenomena occurring in the market. We check whether the analyzed MST topological indicators can be considered as systemic risk predictors.
... [Billio et al. 2012], [Weiß, Mühlnickel 2014]) and those in which they claim that they can be systematically significant , but this is due to their non-traditional (banking) activities (e.g. [Baluch et al. 2011], [Cummins, Weiss 2014]) and the overall systemic importance of the insurance sector as a whole is still subordinated to the banking sector ( [Chen et al. 2013] ). In turn, in [Bierth in. ...
This work is an answer to the EIOPA 2017 report. It follows from the latter that in order to assess the potential systemic risk we should take into account the build-up of risk and in particular the risk that arises in time, as well as the interlinkages in the financial sector and the whole economy. Our main tools used to analyse the systemic risk dynamics in the European insurance sector during the years 2005-2019 are the topological indices of minimum spanning trees (MST) and the deltaCoVaR measure. We address the following questions: 1) What is the contribution to systemic risk of each of the 28 largest European insurance companies whose list includes also those appearing on the G-SIIs list? 2) Does the analysis of the deltaCoVaR of those 28 insurance companies and the conclusions we draw agree with the our claims from our latest article [Wanat S., Denkowska A. 2019]. In clear: does the most important contribution to systemic risk come from the companies that have the highest betweenness centrality or the highest degree in the MST obtained?
... Third, most studies (for example, Baranoff and Sager 2002,;Rauch and Wende 2015,;Mankaï and Belgacem 2016) demonstrate that there is a positive relationship between asset risk and insurer capitalization. One interpretation is that insurers will raise capital to offset policyholders' awareness of insolvency when increasing risk-taking (Chen et al. 2014). Our paper also analyzes asset risk by viewing underlying assets, following a geometric Brownian motion with structure-break stochastic processes. ...
The paper develops a structure-break contingent claim model to examine how government bailout affects a life insurer’s performance (policyholder protection and insurer survival). The distressed assets purchased by the government enhance the optimal insurer interest margin, and policyholder protection. Bailout as such helps the life insurer, implying a higher likelihood of survival in particular when a financial crisis deteriorates seriously, thereby contributing to the stability of the insurance system. In addition, we suggest that low participation of the profit-sharing policy increases insurer survival. This strategic participation effect becomes more significant when the economic state of structural break volatility is increased, and thus, enhancing insurer survival and solving financial problems.
... BRT enables evaluation of the relative importance of predictor variables for forecasting and model interaction effects (Döpke et al., 2017) with very high accuracy and is suitable for dealing with seasonal meteorological datasets (Gu et al., 2019). Regression methods used for multivariable time-series analysis and forecasting are common in disciplines such as econometrics (Chen et al., 2014;Park and Phillips, 2001), public health (Elgar et al., 2015;Imai et al., 2015;Wu et al., 2017), and human behavior (Kaytez et al., 2015). To our knowledge, no study has incorporated LAI measurements into time-series models in order to define the interrelations between the meteorological and LAI variables and their effect on ET c or for forecasting purposes. ...
The interactions between temperature, relative humidity, radiation, wind speed and their effect on plant transpiration in the context of water consumption for irrigation purposes have been studied for over a century. Leaf area has also been established as an important factor affecting water consumption. We analyzed a multivariable time series composed of both meteorological and vegetative variables with a daily temporal resolution for the growing seasons of 2013–2016 for Vitis vinfera ‘Cabernet Sauvignon’ vineyards in the mountainous region in Israel. Time-series analysis of this data was used to characterize seasonal patterns affecting water consumption (ETc) of vines and to quantify interrelations between meteorological and vegetative factors affecting vine water consumption. Moreover, we applied a machine learning regression model to determine the relative influence of meteorological and vegetative factors on ETc during four growing seasons. Finally, we developed an ensemble model for temporally forecasting vine ETc for an additional season using a training dataset of multiple variables. Our findings show that decomposing the time-series dataset uncovered a wider variety of underlying temporal patterns, and enabled quantification of seasonal and daily relationships. Leaf area had a substantial impact on ETc and was found to have a relative influence ranging between 62 and 86% for the different growing seasons. Mean temperature was ranked second followed by minor effects of relative humidity, solar radiation and wind speed that were interchangeably ordered. The ensemble model produced reliable results, with cross validation coefficients ~ 0.9. Incorporating leaf area measurements into the regression model improved both the performance of the model and the training data correlation. Using time-series statistics to explore meteorological and vegetative temporal characteristics, patterns, interrelations and relative effect on evapotranspiration may facilitate the understanding of water consumption processes and assist in generating more effective and skillful irrigation models.
... Before that, researchers seemed to be convinced that the insurance market is systemically irrelevant. After the crisis, some of them upheld their point of view: [11], [3], [10], [2], while others published papers indicating that systemic risk may be created by the insurance sector: [5], [26], [1], [7], [6], [8]. In [4] basing on the study of a large number of insurers in a long timescale, the authors come to the conclusion that the contribution of the insurance sector to systemic risk is relatively small and its peak was reached during the financial crisis of the years 2007-2008. ...
This paper is part of the research on the interlinkages between insurers and their contribution to systemic risk on the insurance market. Its main purpose is to present the results of the analysis of linkage dynamics and systemic risk in the European insurance sector which are obtained using correlation networks. These networks are based on dynamic dependence structures modelled using a copula. Then, we determine minimum spanning trees (MST). Finally, the linkage dynamics is described by means of selected topological network measures.
... [Billio et al.. 2012] , [Weiß, Mühlnickel 2014]); can be systemically important, but only due to their non-traditional (banking) activities (e.g. [Baluch et al.. 2011], [Cummins, Weiss 2014]), while in general the systemic significance of the insurance sector as a whole is still subordinated to the banking sector ( [Chen et al. 2013], [Czerwińska 2014]); ...
The subject of the present article is the study of correlations between large insurance companies and their contribution to systemic risk in the insurance sector. Our main goal is to analyze the conditional structure of the correlation on the European insurance market and to compare systemic risk in different regimes of this market. These regimes are identified by monitoring the weekly rates of returns of eight of the largest insurers (five from Europe and the biggest insurers from the USA, Canada and China) during the period January 2005 to December 2018. To this aim we use statistical clustering methods for time units (weeks) to which we assigned the conditional variances obtained from the estimated copula-DCC-GARCH model. The advantage of such an approach is that there is no need to assume a priori a number of market regimes, since this number has been identified by means of clustering quality validation. In each of the identified market regimes we determined the commonly now used CoVaR systemic risk measure. From the performed analysis we conclude that all the considered insurance companies are positively correlated and this correlation is stronger in times of turbulences on global markets which shows an increased exposure of the European insurance sector to systemic risk during crisis. Moreover, in times of turbulences on global markets the value level of the CoVaR systemic risk index is much higher than in "normal conditions".
... Statistical analysis such as generalized additive models (GAM) can be used for analysing non-linearity in social-ecological systems (e.g.Hossain et al. 2016b), while structural equation models (Santos-Martın et al. 2013) and vector auto regressive models can be used for analysing links between HWB and ecosystems. In addition, econometric methods such as the nonlinear granger causality test (Chen et al. 2013) and feedback models (Granger 1969) can provide insight on dynamic interrelationships (e.g. causality and feedbacks) between ES and HWB. ...
We aimed to identify priority research questions in the field of biodiversity, ecosystem services and sustainability (BESS), based on a workshop held during the NRG BESS Conference for Early Career Researchers on BESS, and to compare these to existing horizon scanning exercises. This work highlights the need for improved data availability through collaboration and knowledge exchange, which, in turn, can support the integrated valuation and sustainable management of ecosystems in response to global change. In addition, clear connectivity among different research themes in this field further emphasises the need to consider a wider range of topics simultaneously to ensure the sustainable management of ecosystems for human wellbeing. In contrast to other horizon scanning exercises, our focus was more interdisciplinary and more concerned with the limits of sustainability and dynamic relationships between social and ecological systems. The identified questions could provide a framework for researchers, policy makers, funding agencies and the private sector to advance knowledge in biodiversity and ES research and to develop and implement policies to enable sustainable future development.
... A fundamental reason as to why insurance companies have been hit quite hard by the GFC has been their high degree of interconnectedness with the banking sector through non-traditional business activities such as, raising premiums through " bancassurance " and investment of insurance funds in equity and bond market (Baluch et al., 2011). It is this high degree of interconnectedness that has been highlighted as a major systemic risk for the insurance industry, see for exampleChen et al. (2013).The impact of the GFC on the banking sector has been analysed thoroughly in the literature providing suggestions as to how the insurance industry could be better isolated (Lehmann and Hofmann, 2010;Eling and Schmeiser, 2010;Ashby, 2011;Baluch et al., 2011). ...
In this paper we compare the Islamic insurance industry (Takaful) to the conventional insurance across 14 countries over the 2005–2014 period. Our methodology relies on panel regressions and accounts for the periods during and post the global financial crisis (GFC). Specifically, we investigate: i) the difference in the insurance demand dynamics of the two insurance types; ii) if Islamic insurance demand has been boosted in the period that followed the crisis. To allow for cross-country heterogeneities we form sub-samples of high/low insurance regions and ASEAN/Middle East. We find Islamic and conventional insurance demand to be negatively affected by GDP/capita, albeit the Islamic showing a greater resilience during crisis. A negative link between conventional insurance and saving rate shows that conventional saving products work as substitutes to conventional insurance. Higher average income is positively (negatively) related to Islamic insurance demand in the Middle East (ASEAN), a finding plausibly related to the different practices relating to Islamic finance in the two regions.
... This literature introduced the CDS paradox 3 (Nijskens and Wagner, 2011). Another line of CDS literature dealt with the "too big to fail/save" case as shown in the work of Chen et al. (2014) which addresses the relevance of a country´s membership in a monetary union in relation to spillover effects (Dieckmann and Plank, 2012) or the market interventions of public authorities (Ejsing and Lemke, 2011). Over the past years CDS were also applied as alternative measures of country risk (Remolona, Scatigna and Wu, 2008). ...
... Effectively measuring tail dependence plays an important role in understanding and managing systemic risk. SeeAllen et al. (2012)for measuring systemic risk and using the measure to predict future economic downturns;Chen et al. (2013)for a connection of systemic risk between banks and insurers; an excellent review on systemic risk is given byBisias et al. (2012). Extreme co-movement usually requires measuring tail dependence of several variables. ...
Systemic risk concerns extreme co-movement of several financial variables, which involves characterizing tail dependence. The coefficient of tail dependence was proposed by Ledford and Tawn (1996, 1997) to distinguish asymptotic independence and asymptotic dependence. Recently a new measure based on the conditional Kendall’s tau was proposed by Asimit et al. (2015) to measure the tail dependence and to distinguish asymptotic independence and asymptotic dependence. For effectively constructing a confidence interval for this new measure, this paper proposes a smooth jackknife empirical likelihood method, which does not need to estimate any additional quantities such as asymptotic variance. A simulation study shows that the proposed method has a good finite sample performance.
... In their empirical investigation, Bernal et al. (2013), for example, found that banks contribute relatively the most to systemic risk in the Eurozone, while the insurance industry is the most systemically risky sector in the US for the period 2004-2012. Recently, Chen et al. (2013) and Billio et al. (2012), using univariate Granger-causality analyses, show a significant two-way interconnection between banking and insurance sectors. Our model-based approach instead is able to investigate the contribution of each sector to the risk of all the remaining ones in a multivariate framework. ...
In this paper we consider a multivariate model-based approach to measure the
dynamic evolution of tail risk interdependence among US banks, financial
services and insurance sectors. To deeply investigate the risk contribution of
insurers we consider separately life and non-life companies. To achieve this
goal we apply the multivariate student-t Markov Switching model and the
Multiple-CoVaR (CoES) risk measures introduced in Bernardi et. al. (2013b) to
account for both the known stylised characteristics of the data and the
contemporaneous joint distress events affecting financial sectors. Our
empirical investigation finds that banks appear to be the major source of risk
for all the remaining sectors, followed by the financial services and the
insurance sectors, showing that insurance sector significantly contributes as
well to the overall risk. Moreover, we find that the role of each sector in
contributing to other sectors distress evolves over time accordingly to the
current predominant financial condition, implying different interconnection
strength.
... Billio et al., 2012;Weiß and Mühlnickel, 2014) and those in which they claim that they can be systematically significant, but this is due to their non-traditional (banking) activities (e.g. Baluch et al., 2011;Cummins and Weiss, 2014) and the overall systemic importance of the insurance sector as a whole is still subordinated to the banking sector (Chen et al., 2013). In turn, in (Bierth et al., 2015), the authors, after examining a very large sample of insurers in the long term, believe that the contribution of the insurance sector to systemic risk is relatively small, however, they claim that it reached its peak during the financial crisis in 2007−2008, which we also confirm in our analysis. ...
This paper was inspired by the work of B. Beauzamy and S. Guerre [3], who gave a new version of the strong law of large numbers tak-ing a generalization of Cesaro averages and then considering independent random variables with values in Lp spaces. We first investigate analogues of this theorem with Cesaro-type averages given by Orlicz functions and then we modify the random variables so as to place ourselves in a modular space.
As a developing country, China's insurance market is already the second largest insurance market in the world, and its development is inseparable from the comprehensive development of insurance companies' business. At present, insurance companies are engaged in both traditional and nontraditional insurance businesses. Thus, to avoid extreme financial events, the insurance market requires more risk management. Hence, examining systemic risk in the insurance market in China is an interesting research topic. This paper studies the comovement between all listed insurance companies and the insurance market in China by modeling the average and extreme dependence structure using different copula specifications. Furthermore, it analyzes both downside and upside risk spillover effects from insurance companies to the insurance market by quantifying the following three market risk measures: the value-at-risk (VaR), the conditional VaR (CoVaR) and the delta CoVaR (ΔCoVaR). Finally, we test the significance of risk spillover effects and provide a formal ranking of the listed insurance companies with respect to their contribution to both the downside and upside systemic risk of the insurance market.
The purpose of this study is to explain the Systemic Risk Model with Marginal Expected Shortfall Approach (MES) as regards the banks listed on the Tehran Stock Exchange. The research population includes 15 banks that were present in Tehran Stock Exchange or Iran’s Over-The-Counter (OTC) for the period 2013 to 2018. Data analysis showed that according to the MES criterion, systemic risk has been declining in the period under review. However, the developments of this index can be divided into two sub-periods 2013-2015 and 2016-2018. In the first period (2013-2015), the level of systemic risk based on this criterion was significantly higher than the level of systemic risk in the second period (2016-2018); Nonetheless, over the time, in the second sub-period, on average, the values amounted to about half of what they were in the first-period level.
This research examines the relationship between agricultural commodity prices and the local housing market. Previous literature focuses on the unidirectional impacts from commodity prices to the local economy, but we propose that in some cases local asset markets can also influence commodity prices due to the agglomeration of commodity productions and the linkages of financial markets, and thus the causalities between them can be bilateral. In addition, according to a new regional economic model that we develop about local asset market and commodity price shocks, we further predict the causalities between the commodity prices and the local house price are nonlinear. We test these hypotheses using data of the major agricultural commodities of an agriculturally focused region – California Central Valley area. The results are consistent with our expectations: there is no evidence for linear causalities between the price returns of these agricultural commodities and the local housing market, but the nonlinear Granger causalities between them are significant.
In the present work we analyze the dynamics of indirect connections between insurance companies that result from market price channels. In our analysis we assume that the stock quotations of insurance companies reflect market sentiments which constitute a very important systemic risk factor. Interlinkages between insurers and their dynamics have a direct impact on systemic risk contagion in the insurance sector. We propose herein a new hybrid approach to the analysis of interlinkages dynamics based on combining the copula-DCC-GARCH model and Minimum Spanning Trees (MST). Using the copula-DCC-GARCH model we determine the correlation coefficients in the distribution tails. Then, for each analysed period we construct MST based on these coefficients. The dynamics is analysed by means of time series of selected topological indicators of the MSTs. Our empirical results show the usefulness of the proposed approach to the analysis of systemic risk in the insurance sector. The times series obtained from the proposed hybrid approach reflect the phenomena occurring on the market. The analysed MST topological indicators can be considered as systemic risk predictors.
Implementation of information technologies has led to the concept of the digital economy which is based on the use of the most advanced innovations. Digital transformation is realized in every economic sector including the banking sector. Nowadays, banking is one of the most important sectors which introduce and use the latest achievements in the field of information and communication technologies. Despite the increasing relevance of digitalization, surprisingly little is known about the determinants of digital banks performance. This paper attempts to fill this gap by evaluating the link between inner factors and bank performance in Russia. We use regression models on the dataset of 16 digital banks. Performance of digital banks is measured by return on assets in 2018. The results show that digital banks which have more customers and more transactions through digital communication, have higher performance. Our results remain rather inconclusive with respect to a share of loans and deposits through remote banking. These results imply that the identified inner factors of a digital bank play an important role while contributing to its success.
Our evidence indicates that U.S. life insurers’ decisions to buy and sell individual corporate bonds are correlated across companies within the life insurance industry. On average, the correlation in sell decisions is greater in smaller bonds, bonds with lower ratings, bonds that have been downgraded, and bonds that have recently experienced relatively large abnormal returns. Correlated trading was also elevated during the financial crisis. In addition, correlated buying and selling are greater when insurers designated as systemically important financial institutions are actively trading. We also find that the bonds that insurers sell in a correlated manner exhibit negative average abnormal returns during the quarter in which insurers are selling. One explanation is that insurers’ correlated selling is temporarily pushing bond prices below their fundamental value. In this case, we would expect prices to bounce back in the subsequent quarter. However, we do not find a rebound in prices and therefore our evidence supports the alternative explanation that insurers’ correlated selling is impounding information into bond prices.
This article mainly focuses on investigating the nonlinear co-integration and nonlinear causality relationships between oil prices and Chinese stock market at the overall and sectoral levels by using nonlinear autoregressive distributed lags (NARDL) model and Diks and Panchenko (DP) test. The empirical results show that there are not significantly asymmetric co-integration effects between oil prices and Chinese stock market for the overall and sectoral levels. However, the significantly nonlinear causality between oil prices and Chinese stock market can be found. Specifically, oil prices can widely affect Chinese stock indices through nonlinear channel. The cases in the reverse also work for overall indices and Mining, Utilities, Financial and Real Estate sectors. Furthermore, the potential sources of these nonlinear causality linkages are examined. The results suggest that volatility persistence rather than asymmetrical co-integration is the major factor that accounts for the nonlinear causality between oil prices and Chinese stock market.
Causal relationships in econometrics are typically based on the concept of predictability and are established by testing Granger causality. Such relationships are susceptible to change, especially during times of financial turbulence, making the real‐time detection of instability an important practical issue. This article develops a test for detecting changes in causal relationships based on a recursive evolving window, which is analogous to a procedure used in recent work on financial bubble detection. The limiting distribution of the test takes a simple form under the null hypothesis and is easy to implement in conditions of homoskedasticity and conditional heteroskedasticity of an unknown form. Bootstrap methods are used to control family‐wise size in implementation. Simulation experiments compare the efficacy of the proposed test with two other commonly used tests, the forward recursive and the rolling window tests. The results indicate that the recursive evolving approach offers the best finite sample performance, followed by the rolling window algorithm. The testing strategies are illustrated in an empirical application that explores the causal relationship between the slope of the yield curve and real economic activity in the United States over the period 1980–2015.
The empirical literature regarding financial institution interconnectedness and potential systemic risk has burgeoned in the last decade. Broad conclusions from this research pertaining to credit default swaps (“CDSs”) include the following: (i) Single-name CDSs on corporate, banking, and sovereign reference entities are a source of interconnectedness and contain information that may be valuable to policy makers in measuring potential systemic risk, but there is a dearth of empirical evidence to indicate that single-name CDSs are systemically destabilizing; (ii) single-name sovereign CDSs are transmission mechanisms for economic shocks but not generally a cause of those shocks; and (iii) a “sovereign-bank” loop integrates the financial condition of the banking sector with sovereign credit risks.
This study investigates how insurers contribute to systemic risk in the global financial system. In a modeling framework embracing publicly traded and nonpublic firms, the financial system is represented by 201 major banks and insurers over the period from 2004 through 2014. In the aggregate, the insurance sector contributes relatively little to systemic losses; during the financial crisis and the European sovereign debt crisis, its risk share averaged 9 percent. Individually, however, several multi‐line and life insurers appear to be as systemically risky as the riskiest banks. Our results, therefore, affirm that some insurers are systemically important and indicate that insurers’ level of systemic risk varies by line of business. We discuss several important implications of our results for managing systemic risk in insurance, arguing for a combination of entity‐ and activity‐based regulation.
In this chapter Agboraw and Jones explore theories of natural resource scarcity and economic growth. By examining how natural resources fit within current theory the issues of how scarcity of resources, or resource prices, link to a better understanding of economic risk is outlined.
Identifying contagion effects during periods of financial crisis is known to be complicated by the changing volatility of asset returns during periods of stress. To untangle this we propose a GARCH (generalized autoregressive conditional heteroskedasticity) common features approach, where systemic risk emerges from a common factor source (or indeed multiple factor sources) with contagion evident through possible changes in the factor loadings relating to the common factor(s). Within a portfolio mimicking factor framework this can be identified using moment conditions. We use this framework to identify contagion in three illustrations involving both single and multiple factor specifications: to the Asian currency markets in 1997–1998, to US sectoral equity indices in 2007–2009 and to the CDS (credit default swap) market during the European sovereign debt crisis of 2010–2013. The results reveal the extent to which contagion effects may be masked by not accounting for the sources of changed volatility apparent in simple measures such as correlation.
We investigate cross-sector financial contagion over the period 2006–2014 for a sample of large European banks and insurers. We use CDS spreads and define contagion as correlation over and above what is explained by fundamental factors. Moreover, we assess the impact of different business models on contagion and the channels through which it spreads. We find that, for insurers, size and investment income raise contagion, while for banks capital adequacy, funding and income diversification are the most relevant factors. Furthermore, leverage is crucial in both sectors. We also provide evidence of the main risk transmission channels: the asset-holding and the guarantee channel for insurers and the additional collateral channel for banks. Our results offer new insight on how credit risk spillovers spread across sectors and call for further regulatory and supervisory effort in understanding if and where cross-industry similarities increase contagion risks. © 2017 The International Association for the Study of Insurance Economics
We develop a portfolio credit risk model that includes firm-specific Markov-switching regimes as well as individual stochastic and endogenous recovery rates. Using weekly credit default swap premiums for 35 financial firms, we analyze the credit risk of each of these companies and their statistical linkages, putting emphasis on the 2005–2012 period. Moreover, we study the systemic risk affecting both the banking and insurance subsectors.
This article reviews the extant research on systemic risk in the insurance sector and outlines new areas of research in this field. We summarize and classify 48 theoretical and empirical research papers from both academia and practitioner organizations. The survey reveals that traditional insurance activity in the life, nonlife, and reinsurance sectors neither contributes to systemic risk nor increases insurers? vulnerability to impairments of the financial system. However, nontraditional activities (e.g., credit default swap underwriting) might increase vulnerability, and life insurers might be more vulnerable than nonlife insurers due to higher leverage. Whether nontraditional activities also contribute to systemic risk is not entirely clear; however, the activities with the potential to contribute to systemic risk include underwriting financial derivatives and providing financial guarantees. This article is not only likely of interest to academics but also highly relevant for the industry, regulators, and policymakers.
The recent global financial crisis brought numerous financial institutions to the brink of collapse and led authorities to take far-reaching measures to safeguard financial stability. The causes of the crisis have been extensively analysed and lessons have been drawn. One lesson stands out: the need to strengthen the supervision of the financial system as a whole, from a macroprudential perspective. As a complement to microprudential supervision, such a perspective considers potential systemic interactions between the individual institution and the broader financial system. This lesson is of particular relevance for the banking sector, as banks are more likely to be considered systemically important than insurers. By nature, banks are highly interconnected through the interbank money market, play a pivotal role in the payment system and are exposed to liquidity risk. Nevertheless, contagion channels also exist between the insurance sector and other parts of the financial system, both in theory and practice.
We use Internet search volume data to measure idiosyncratic and market-wide crisis sentiment to explain insurer stock return volatility. We find that market-level crisis sentiment was a significant predictor of stock return volatility of U.S. insurers between 2006 and 2010. Higher levels of crisis sentiment are associated with higher levels of price uncertainty. This effect is strongest for insurers with less exposure to the adverse effects of the financial crisis. Further, crisis sentiment also affects the cross-section of movements in insurer stock prices. Our results imply that investors exited insurer stocks mainly due to crisis sentiment rather than a rational assessment of the insurers' actual exposure to the crisis.
This chapter considers the broader issues of whether insurance entities pose systemic risk and the challenges and potential adverse consequences of designating additional insurance entities for enhanced supervision by the Federal Reserve. It begins with an overview of research and analyses of whether insurance activities and entities pose systemic risk. The chapter then summarizes the FSOC process for designating nonbank systemically important financial institutions (SIFIs) and the Financial Stability Oversight Council's (FSOC) stated rationale for designating American International Group (AIG) as systemically important. Three issues are then briefly considered: (i) regulatory and compliance costs and potential undesirable market disruptions from designation of insurance entities as SIFIs subject to enhanced supervision; (ii) the design of enhanced capital requirements for insurer SIFIs; and (iii) the risk that designation of insurer SIFIs would ultimately reduce market discipline by expanding “too big to fail” policy. The concluding section reiterates the chapter's main arguments.
This chapter explores the vulnerability of the U.S. life insurance industry to hypothetical asset valuation and policyholder redemption shocks. It compares life insurer balance sheets to those of banks, who are vulnerable to depositor runs. The chapter analyzes the liquidity of the liabilities and the assets of the life insurance industry in an effort to better understand the ability of the industry. Following a brief overview of the life insurance business model, it characterizes the liquidity of life insurer liabilities based on how easy it is for policyholders to withdraw funds from various product classes. The chapter describes life insurer asset holdings and uses historical price data to consider what would happen to the value of life insurer assets if these assets were to suffer severe shocks. Life insurer holdings of liquid assets are compared to the demands for cash estimated under the moderate and extreme withdrawal shock scenarios.
We present an economic model of systemic risk in which undercapitalization of the financial sector as a whole is assumed to harm the real economy, leading to a systemic risk externality. Each financial institution’s contribution to systemic risk can be measured as its systemic expected shortfall (SES), that is, its propensity to be undercapitalized when the system as a whole is undercapitalized. SES increases in the institution’s leverage and its marginal expected shortfall (MES), that is, its losses in the tail of the system’s loss distribution. We demonstrate empirically the ability of components of SES to predict emerging systemic risk during the financial crisis of 2007–2009.
Received December 1, 2015; editorial decision August 5, 2016 by Editor Andrew Karolyi.
In this paper we develop two fast procedures for valuing tranches of collateralized debt obligations and nth to default swaps. The procedures are based on a factor copula model of times to default and are alternatives to using fast Fourier transforms. One involves calculating the probability distribution of the number of defaults by a certain time using a recurrence relationship; the other involves using a "probability bucketing" numerical procedure to build up the loss distribution. We show how many different copula models can be generated by using different distributional assumptions within the factor model. We examine the impact on valuations of default probabilities, default correlations, the copula model chosen, and a correlation of recovery rates with default probabilities. Finally we look at the market pricing of index tranches and conclude that a "double t- distribution" copula fits the prices reasonably well.
We adopt a systemic risk indicator measured by the price of insurance against systemic financial distress and assess individual
banks’ marginal contributions to the systemic risk. The methodology is applied using publicly available data to the 19 bank
holding companies covered by the U.S. Supervisory Capital Assessment Program (SCAP), with the systemic risk indicator peaking
around $1.1 trillion in March 2009. Our systemic risk contribution measure shows interesting similarity to and divergence
from the SCAP loss estimates under stress test scenarios. In general, we find that a bank’s contribution to the systemic risk
is roughly linear in its default probability but highly nonlinear with respect to institution size and asset correlation.
KeywordsDistress insurance premium–Systemic risk–Macroprudential regulation–Large complex financial institution–Too-big-to-fail–Too-connected-to-fail
In this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks and forecasted asset return correlations. Importantly, using realized correlations estimated from high-frequency equity return data can significantly improve the accuracy of forecasted correlations. Our stress testing methodology, using an integrated micro–macro model, takes into account dynamic linkages between the health of major US banks and macro-financial conditions. Our results suggest that the theoretical insurance premium that would be charged to protect against losses that equal or exceed 15% of total liabilities of 12 major US financial firms stood at $110 billion in March 2008 and had a projected upper bound of $250 billion in July 2008.
This article analyzes whether market-based financial stability indicators (FSIs) should be included in monetary policy models and, if so, how.1 Since the economy and interest rates affect financial sector credit risk, and the financial sector affects the economy, this article builds a model of financial sector vulnerability and integrates it into a macroeconomic framework, typically used for monetary policy analysis. More specifically, should the central bank explicitly include the financial stability indicator in its monetary policy (interest rate) reaction function? This is the most important question to be answered in this article. The alternative would be to react only indirectly to financial risk by reacting to inflation and gross domestic product (GDP) gaps, since they already include the effect that financial factors have on the economy.
This study has two purposes. One is to examine the substantive question: Is there statistical evidence that money is "exogenous" in some sense in the money- income relationship? The other is to dis- play in a simple example some time-series methodology not now in wide use. The main methodological novelty is the use of a direct test for the existence of unidirec- tional causality. This test is of wide im- portance, since most efficient estimation techniques for distributed lags are invalid unless causality is unidirectional in the sense of this paper. Also, the paper illus- trates the estimation of long lag distribu- tions without the imposition of the usual restrictions requiring the shape of the dis- tribution to be rational or polynomial. The main empirical finding is that the hypothesis that causality is unidirectional from money to income agrees with the postwar U.S. data, whereas the hypoth- esis that causality is unidirectional from income to money is rejected. It follows that the practice of making causal inter- pretations of distributed lag regressions of income on money is not invalidated (on the basis of this evidence) by the existence of "feedback" from income to money.
We provide a framework for integration of high--frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Building on the theory of continuous--time arbitrage--free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen/Dollar spot exchange rates, we find that forecasts from a simple long--memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal--normal mixture distribution produces well--calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation, and financial risk management applications. Copyright The Econometric Society 2003.
This paper examines the potential for the U.S. insurance industry to cause systemic risk events that spill over to other segments of the economy. We examine primary indicators that determine whether institutions are systemically risky as well as contributing factors that exacerbate vulnerability to systemic events. Evaluation of systemic risk is based on a detailed financial analysis of the insurance industry, its role in the economy, and the interconnectedness of insurers. The primary conclusion is that the core activities of the U.S. insurers do not pose systemic risk. However, life insurers are vulnerable to intra-sector crises because of leverage and liquidity risk; and both life and property-casualty insurers are vulnerable to reinsurance crises arising from counterparty credit exposure. Non-core activities such as derivatives trading have the potential to cause systemic risk, and most global insurance organizations have exposure to derivatives markets. To reduce systemic risk from non-core activities, regulators need to develop better mechanisms for insurance group supervision.
This paper extends the approach of measuring and stress-testing the systemic risk of a banking sector in Huang, Zhou, and Zhu (2009) to identifying various sources of financial instability and to allocating systemic risk to individual financial institutions. The systemic risk measure, defined as the insurance cost to protect against distressed losses in a banking system, is a summary indicator of market perceived risk that reflects expected default risk of individual banks, risk premia as well as correlated defaults. An application of our methodology to a portfolio of twenty-two major banks in Asia and the Pacific illustrates the dynamics of the spillover effects of the global financial crisis to the region. The increase in the perceived systemic risk, particularly after the failure of Lehman Brothers, was mainly driven by the heightened risk aversion and the squeezed liquidity. Further analysis, which is based on our proposed approach to quantifying the marginal contribution of individual banks to the systemic risk, suggests that “too-big-to-fail” is a valid concern from a macroprudential perspective of bank regulation.
The behavior of exchange rates is examined as they evolve continuously over time. The data consist of Swiss franc/U.S. dollar rates for nine days during the years 1978–1980 as quoted by a major Swiss dealer operating on the interbank market. Since this market is highly organized, the observations are market prices at the same time. The distributions of relative changes in exchange rates measured over one minute are highly leptokurtic. The normal distribution is rather rapidly approached when the measurement interval is lengthened from one up to ten minutes. Time series analysis reveals that the natural logarithms of exchange rates are adequately described by a random walk, the same stochastic process as has been found for daily, weekly, monthly and quarterly observations. For short time intervals, significant autocorrelations sometimes occur at the first few lags, which are, however, not stable enough over time to form a basis for reliable forecasts.
This article shows that any coherent risk measure is given by a convex combination of expected shortfalls, and an expected shortfall (ES) is optimal in the sense that it gives the minimum value among the class of plausible coherent risk measures. Hence, it is of great practical interest to estimate the ES with given confidence level from the market data in a stable fashion. In this article, we propose an extrapolation method to estimate the ES of interest. Some numerical results are given to show the efficiency of our method.
In this paper we introduce a new nonparametric test for Granger non-causality which avoids the over-rejection observed in the frequently used test proposed by Hiemstra and Jones [1994. Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance 49, 1639–1664]. After illustrating the problem by showing that rejection probabilities under the null hypothesis may tend to one as the sample size increases, we study the reason behind this phenomenon analytically. It turns out that the Hiemstra–Jones test for the null of Granger non-causality, which can be rephrased in terms of conditional independence of two vectors X and Z given a third vector Y, is sensitive to variations in the conditional distributions of X and Z that may be present under the null. To overcome this problem we replace the global test statistic by an average of local conditional dependence measures. By letting the bandwidth tend to zero at appropriate rates, the variations in the conditional distributions are accounted for automatically. Based on asymptotic theory we formulate practical guidelines for choosing the bandwidth depending on the sample size. We conclude with an application to historical returns and trading volumes of the Standard and Poor's index which indicates that the evidence for volume Granger-causing returns is weaker than suggested by the Hiemstra–Jones test.
Value-at-Risk (VaR) has become a standard risk measure for financial risk management. However, many authors claim that there are several conceptual problems with VaR. Among these problems, an important one is that VaR disregards any loss beyond the VaR level. We call this problem the “tail risk”. In this paper, we illustrate how the tail risk of VaR can cause serious problems in certain cases, cases in which expected shortfall can serve more aptly in its place. We discuss two cases: concentrated credit portfolio and foreign exchange rates under market stress. We show that expected shortfall requires a larger sample size than VaR to provide the same level of accuracy.
This article considers the role of American International Group (AIG) and the insurance sector in the 2007-2009 financial crisis and the implications for insurance regulation. Following an overview of the causes of the crisis, I explore the events and policies that contributed to federal government intervention to prevent bankruptcy of AIG and the scope of federal assistance to AIG. I discuss the extent to which insurance in general poses systemic risk and whether a systemic risk regulator is desirable for insurers or other nonbank financial institutions. The last two sections of the article address the financial crisis's implications for proposed optional and/or mandatory federal chartering and regulation of insurers and for insurance regulation in general. Copyright (c) The Journal of Risk and Insurance, 2009.
We propose several econometric measures of systemic risk to capture the interconnectedness among the monthly returns of hedge funds, banks, brokers, and insurance companies based on principal components analysis and Granger-causality tests. We find that all four sectors have become highly interrelated over the past decade, increasing the level of systemic risk in the finance and insurance industries. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power for the current financial crisis. Our results suggest that hedge funds can provide early indications of market dislocation, and systemic risk arises from a complex and dynamic network of relationships among hedge funds, banks, insurance companies, and brokers.
An operational macroprudential approach to financial stability requires tools that attribute system-wide risk to individual institutions. Making use of constructs from game theory, we propose an attribution methodology that has a number of appealing features: it can be used in conjunction with popular risk measures, it provides measures of institutions’ systemic importance that add up exactly to the measure of system-wide risk and it easily accommodates uncertainty about the validity of the risk model. We apply this methodology to a number of constructed examples and illustrate the interactions between drivers of systemic importance: size, the institution’s risk profile and strength of exposures to common risk factors. We also demonstrate how the methodology can be used for the calibration of macroprudential capital rules.
This paper presents a test of independence that can be applied to the estimated residuals of any time series model that can be transformed into a model driven by independent and identically distributed errors. The first order asymptotic distribution of the test statistic is independent of estimation error provided that the parameters of the model under test can be estimated [image omitted] -consistently. Because of this, our method can be used as a model selection tool and as a specification test. Widely used software1 written by Dechert and LeBaron can be used to implement the test. Also, this software is fast enough that the null distribution of our test statistic can be estimated with bootstrap methods. Our method can be viewed as a nonlinear analog of the Box-Pierce Q statistic used in ARIMA analysis.
This paper generalizes a market-based indicator for financial sector surveillance using a multifactor latent structure in the determination of the default probabilities of an nth-todefault credit default swap (CDS) basket of large complex financial institutions (LCFIs). To estimate the multifactor latent structure, we link the market risk (the covariance of the LCFIs' equity) to credit risk (the default probability of the CDS basket) in a coherent manner. In addition, to analyze the response of the probabilities of default to changing macroeconomic conditions, we run a stress test by generating shocks to the latent multifactor structure. The results unveil a rich set of default probability dynamics and help in identifying the most relevant sources of risk. We anticipate that this approach could be of value to financial supervisors and risk managers alike.
This paper focuses on the asymptotic single-risk-factor (ASRF) model in order to analyze the impact of specification and calibration errors on popular measures of portfolio credit risk. Violations of key assumptions of this model are found to be virtually inconsequential, especially for large, welldiversified portfolios. By contrast, flaws in the calibrated interdependence of credit risk across exposures, caused by plausible small-sample estimation errors or rule-of-thumb values of asset return correlations, can lead to significant inaccuracies in measures of portfolio credit risk. Similar inaccuracies arise under standard assumptions regarding the tails of the distribution of asset returns.
Linear and nonlinear Granger causality tests are used to examine the dynamic relation between daily Dow Jones stock returns and percentage changes in New York Stock Exchange trading volume. The authors find evidence of significant bidirectional nonlinear causality between returns and volume. They also examine whether the nonlinear causality from volume to returns can be explained by volume serving as a proxy for information flow in the stochastic process generating stock return variance as suggested by P. Clark's (1973) latent common-factor model. After controlling for volatility persistence in returns, the authors continue to find evidence of nonlinear causality from volume to returns. Copyright 1994 by American Finance Association.
Reinsurance-A Systemic Risk
- Swiss Re
Swiss Re, 2003, Reinsurance-A Systemic Risk, Sigma No. 5/2003 (Zurich, Switzerland:
Swiss Re).
Reinsurance and International Financial Markets
- Thirty Group
Group of Thirty, 2006, Reinsurance and International Financial Markets (Washington,
DC: Group of Thirty).
Do Insurance Companies Pose Systemic Risk? Powerpoint presentation, National Association of Insurance Commissioners Winter Meeting
- J D Cummins
Cummins, J. D., 2009, Do Insurance Companies Pose Systemic Risk? Powerpoint
presentation, National Association of Insurance Commissioners Winter Meeting,
San Francisco, CA, December 3.
- X Huang
- H Zhou
- H Zhu
Huang, X., H. Zhou, and H. Zhu, 2012b, Systemic Risk Contributions, Journal of
Financial Services Research, 42: 55-83.
Systemic Risk in Insurance: An Analysis of Insurance and Financial Stability
Geneva Association, 2010, Systemic Risk in Insurance: An Analysis of Insurance and
Financial Stability (Geneva, Switzerland: Geneva Association).
Systemic Risk and the U.S. Insurance Sector, Working paper, National Association of Insurance Commissioners
- M A Weiss
Weiss, M. A., 2010, Systemic Risk and the U.S. Insurance Sector, Working paper,
National Association of Insurance Commissioners, Washington, DC.