Presentation on theme: "Do Insurance Companies Pose Systemic Risk? J. David Cummins 2013 China International Conference on Insurance and Risk Management Kunming, China July 18,"— Presentation transcript:
Do Insurance Companies Pose Systemic Risk? J. David Cummins 2013 China International Conference on Insurance and Risk Management Kunming, China July 18, 2013 Copyright J. David Cummins, 2013, all rights reserved. Not to be reproduced without author’s permission.
Outline of Presentation What is Systemic Risk? Systemic Risk: Primary Indicators – Factors Used to Identify Systemically Important Financial Institutions Systemic Risk: Contributing Factors – Factors Exacerbating Vulnerability to Crises Conclusions: Does Insurance Pose Systemic Risk? Based on primary indicators and contributing factors Are Insurers Instigators and/or Victims of Systemic Risk? An econometric analysis using Granger causality
This Presentation Based on Two Papers Cummins, J. David and Mary A. Weiss, 2013, “Systemic Risk and the Insurance Industry,” forthcoming in Georges Dionne, ed., Handbook of Insurance, 2d ed. (Springer). Chen, Hua, J. David Cummins, Krupa Viswanathan, and Mary A. Weiss, 2013, “Systemic Risk and the Inter-Connectedness between Banks and Insurers: An Econometric Analysis,” forthcoming, Journal of Risk and Insurance. Other relevant papers Chen, Hua, J. David Cummins, Krupa Viswanathan, and Mary A. Weiss, 2013, “Systemic Risk Measures in the Insurance Industry: A Copula Approach,” working paper, Temple University, Philadelphia Cummins, J. David and Mary A. Weiss, 2013, “Systemic Risk and the Regulation of the U.S. Insurance Industry,” working paper, Temple University, Philadelphia. Cummins, J. David and Mary A. Weiss, 2012, “Systemic Risk and the U.S. Insurance Sector,” forthcoming, Journal of Risk and Insurance. To obtain the papers, please email: email@example.com.
What is Systemic Risk?
What Is Systemic Risk? The risk that an event will trigger a loss of economic value or confidence in a substantial segment of the financial system serious enough to have significant adverse effects on the real economy. Group of 10 (2001). Systemic financial risk involves A system-wide financial crisis... accompanied by a sharp decline in asset values and economic activity The spread of instability throughout the financial system (contagion) Sufficient to affect the real economy World Economic Forum (2008). Systemic risk is exposure to extreme correlations
Financial Crises and Systemic Risk Financial Crises Prices of risky assets drop sharply Prices of safe assets increase (flight to quality) Asset price volatility increases Liquidity dries up (rising bid-ask spread & price impact) Financial institutions become financially distressed Credit markets dry up, economic activity depressed Financial systemic risk: Financial crisis in which many institutions become financially distressed, with a potential impact on real economic activity Financial distress of one or a few institutions does not necessarily equal systemic risk!
Too-Big-To-Fail Has Been With Us For a Long Time and Isn’t Confined to Financial Institutions Industry/CompanyYearType of Assistance Penn Central Railroad1970$676.3 million in loan guarantees – Gov’t spent $19.7 billion and got back about $4 billion Lockheed1971Government loan which was paid off New York City1975Loans and loan guarantees Chrysler1980Loan guarantees and warrants – Gov’t earned a profit of about $660 million Continental Illinois1984Government took 80% ownership and phrase TBTF was coined Airline Industry2001Government bought stock below market and provided loan guarantees Automobile industry 2008- 2009 Government takes equity stake in GM & Chrysler = $80 billion
Primary Indicators and Contributing Factors The Question: How to identify systemically risky markets and institutions? Primary indicators: Factors used to identify systemic markets and institutions Contributing factors: Determine the vulnerability of an institution or market to systemic events An institution may be systemic in terms of primary indicators but not vulnerable in terms of contributing factors
Primary Indicators of Systemic Risk Size – Macroeconomic Importance of Insurers Size not limited to conventional measures such as assets Volume of transactions, exposure to off-balance sheet positions, and derivatives also play a role Interconnectedness – degree of correlation and potential for contagion among institutions Lack of substitutability – Are there effective substitutes for an institution’s products? Are the products critical to the functioning of the financial system
Primary Indicators: Size Conventional measures – assets or equity, absolutely or relative to GDP Off-balance sheet exposures and volume of transactions processed “Callability” of positions taken – marginability, etc. Notional value of derivatives exposure (e.g., AIG’s credit default swaps positions) “Too Big to Fail” being replaced by “Systemically Important Financial Institution (SIFI)” Reflects inadequacy of conventional size measures
Designating SIFIs Financial Stability Oversight Commission (FSOC) Established as part of U.S. Treasury in 2010 Can designate banks and non-banks as SIFIs Thresholds for SIFIs ≥ $50 billion of assets, and Meets or exceeds any one of several thresholds ● $30 billion notional CDS for which firm is reference credit ● $3.5 billion of derivative liabilities ● $20 billion of total outstanding debt (bonds, etc.) ● 15-to-1 leverage ratio (assets/equity) ● 10% ratio of short-term debt to assets
Primary Indicators: Interconnectedness Interconnectedness – extent to which financial distress at one or a few institutions increases probability of distress at other institutions Network or “chain” effects on both sides of balance sheet or through derivatives exposures, off-balance sheet commitments, etc. Existence of “contagion” in the economy Interconnectedness creates conditions that trigger “runs on the bank” Could Insurers Cause a “Run on the Bank”?
All Systemic Financial Crises Involve “Runs” In crises, investors seek cash at all costs As prices no longer adjust supply, access to credit becomes central Maturity mismatch compounds shock and spreads runs Rapid withdrawals lead to “fire sales,” prices crash Losses induce margin calls, more fire sales Runs are both cause and consequence of extreme correlation Crises that do not spread to general credit market do not qualify (e.g., “dot-com” bubble collapse in 2000-2001)
Types of “Runs” In the past (e.g., 1930s), financial crises often involved “retail runs” Many depositors try to withdraw money from banks simultaneously Deposit insurance has virtually eliminated retail bank runs Recent financial crisis involved “wholesale runs” Banks refused to provide liquidity to troubled institutions such as Lehman Brothers AIG counterparties demanded higher margin deposits and the unwinding of asset lending relationships Essentially, a “run” on the shadow banking system
“Runs” Triggered by Exposure to Common Shocks Common shock may be exposure to agricultural depression, real estate, or oil prices In the Crisis of 2007-2009, common shock was the bursting of the housing price bubble Bursting of bubble triggered crises in Inter-bank lending Commercial paper Market for short-term repurchase agreements (“repos”) Essentially a run on the shadow banking system
“Too Big to Fail” & “Too Interconnected to Fail” Institutions that pose significant systemic risk are viewed as “Too Big to Fail” -- e.g., failure would cause ripple effects throughout the economy due to the sheer size of the enterprises “Too Interconnected to Fail” -- Firms with multiple counterparty relationships could trigger a cascading chain of failures – “domino effect”
Primary Indicators: Lack of Substitutability Substitutability – extent to which other firms or segments of the financial system can provide the same services provided by the failed institutions For substitutability to be a problem, the services must be critical to the functioning of other institutions or the financial system Payment system – operational arrangement that enables individuals and institutions to transfer funds Settlement system – enables transfer of securities and cash to settle trades Liquidity system – inter-bank lending, repos, etc. Could Unavailability of Insurance Cause a Financial Crisis?
Contributing Factors: Enhance Vulnerability to Systemic Events Leverage Measured conventionally as debt to equity Off-balance sheet positions, options exposure, and “marginability” of positions also creates leverage Leverage and “loss spirals” Declines in asset values erode net worth much faster than the asset declines themselves (“leverage”) ● E.g., at 10-to-1 assets/equity ratio, 5% decline in assets means 50% decline in equity If many institutions are affected at same time, selling assets puts additional pressure on prices generating a loss spiral
Contributing Factors: Liquidity Risk and Maturity Mismatches Liquidity risk – vulnerability to shocks is increased to the extent the institution holds illiquid assets Difficulties in obtaining financing may trigger need to sell assets – problematic for illiquid assets Especially serious if other institutions also illiquid Asset-liability maturity mismatch raises liquidity risk In 2007-2009 Financial Crisis, Shadow banks used short- term commercial paper, overnight lending, and repos to finance longer-term assets Disappearance of short-term financing triggered need to sell illiquid longer-term assets
What Are Repos? Repos – abbreviation for “sale and repurchase agreement” Defined: Sale of securities together with an agreement for the seller to buy back the securities at a later date Repurchase price > original sale price, the difference representing interest, the “repo rate” Seller is a borrower, using securities as collateral for a cash loan at specified interest rate The buyer acts as a lender During financial crisis, repo buyers refused to extend credit to firms such as Lehman
Contributing Factors: Complexity Enhances Vulnerability to Shocks Dimensions of complexity Complexity of organization or group structure – firms offering banking, insurance, and investment products more complex than single industry firms Geographical complexity – multi-national firms face variety of local and regional risk factors Product complexity exposes firms to risks that may not be fully understood ● E.g., AIG Financial Products CDS transactions Complexity aggravated by opacity AIG’s positions were opaque, preventing market adjustment for over-exposure
Contributing Factors: Government Policy and Regulation Government policy and regulation can contribute to financial system fragility Deposit insurance and guaranty funds create moral hazard that may lead to crises – buyers with government guarantees have no incentive to monitor “Too Big To Fail” policies also create moral hazard Complexity of AIG created regulatory blind spot that led to AIG’s near collapse (nominally regulated by Office of Thrift Supervision) US government policy permitted investment banks to become over-leveraged, helping to precipitate crisis Lack of regulatory oversight contributed to the housing bubble and mortgage backed securities crisis
Size Risk: The Macro-Economic Importance of Insurers
Total Assets: US Banks and Insurers Assets: Banks $14.6 trillion, insurers $6.8 trillion. Source: Federal Reserve Flow of Funds accounts.
Total US Life and P-C Premiums: % of GDP Source: A.M. Best Company, American Council of Life Insurance, St. Louis Federal Reserve Bank.
US GDP From Financial Services (Value Added) Source: US Department of Commerce, Bureau of Economic Analysis.
Insurance Companies: Share of Total Assets Source: Federal Reserve Flow of Funds Accounts.
Conclusions: How Big Are Insurers? Insurers have $6.8 trillion in assets About 50% as large as commercial banks Only about 8% of total US financial assets Insurers do not have large share of any asset market Insurer insolvencies resolved gradually so even large insolvency would not lead to liquidity problems Insurers not very important source of GDP (< 3%) Therefore, as a sector insurers do not pose systemic risk due to their size alone
Interconnectedness Risk: Could Insurers Cause A “Run on the Bank”?
Interconnectedness: Insurers and Other Financial Firms I Do US insurers invest heavily in other financial institutions? Investment in Banks: ● 5.6% of insurer assets are in bank bonds ● 1% of insurer assets and in bank equities Investment in Securities firms: ● 1.6% of insurer assets in securities firm bonds ● 1% of insurers assets in securities firm equities Conclusion: Insurers are not vulnerable to stock and bond declines from financial firms
Interconnectedness: Insurers and Other Financial Firms II Are insurers a significant source of funds for other financial institutions? Life insurers supply: ● 9.4% of outstanding bonds for banks ● 14.1% of outstanding bonds for securities firms However, bonds account for only 10% of financing for banks and securities firms Conclusion: Banks and securities firms are not dependent on insurers to finance their activities
Interconnectedness Within Insurance Industry Reinsurance is the primary source of intra- industry interconnectedness Reinsurance creates risk of counterparty default Reinsurer failure to pay claims can trigger insurer insolvencies Insolvent insurers then default on their reinsurance counterparties Result: A Reinsurance spiral Reinsurance counterparty risk present for both Reinsurance transactions with affiliates Non-affiliated reinsurance transactions
Measures of Reinsurance Interconnectedness Reinsurance premiums ceded Insurance in force ceded (life) Reinsurance receivables – funds owed by reinsurers to ceding company Write-down of liabilities due to reinsurance: Reserve credit taken (life) Net amount recoverable from reinsurance (P-C) If reinsurers fail: Default on receivables and ceded premiums Liability write-downs canceled, increasing leverage
Extent of Reinsurance Interconnectedness Reinsurance premiums ceded to affiliates & non- affiliates P-C premiums ceded = 17.2% of surplus Life premiums ceded = 38.0% of surplus Insurance in force ceded (life) = 55.3% of surplus Reinsurance recoverables – For 25% of P-C insurers, recoverables > 40% of surplus For 25% of life insurers, recoverables > 100% of surplus Write-down of liabilities due to reinsurance: Reserve credit taken (life) averages 149% of surplus Recoverables (P-C) average 39% of surplus
Have Reinsurance Failures Been a Significant Source of Insurer Insolvency?
P/C Impairments: Triggering Events Source: A.M. Best: 1969-2010 Impairment Review, Special Report, May 2, 2011. Deficient loss reserves, inadequate pricing, and rapid growth are the leading triggers. Investment, catastrophe, and reinsurance losses play a much smaller role.
L-H Impairments: Triggering Events Source: A.M. Best: U.S. Life/Health – 1976-2010 Impairment Review, Special Report, May 23, 2011. Inadequate pricing, affiliate problems, rapid growth, and investments are primary causes of L/H insolvencies. Life insurers more susceptible to affiliate problems.
Reinsurance Interconnectedness: Conclusions Reinsurer failure traditionally not a major source of insurer insolvency Many insurers do have large reinsurance counterparty exposure relative to surplus Therefore, reinsurance failure could threaten solvency of individual insurers and create industry-wide crisis However, it is unlikely that even a major intra-industry crisis would spill over into broader financial markets Therefore, reinsurance causes intra-industry vulnerability but not systemic risk
Interconnectedness and Non-Core Activities Insurer non-core (“banking”) activities can create interconnectedness and systemic risk Example: AIG Financial Products Non-core activities that may be systemic Credit derivatives transactions Asset lending programs Financial guarantees and other off-balance sheet commitments Reliance by insurers on short-term financing Subsidiaries with high exposures relative to capital Improved regulation needed to prevent crises
Substitutability Risk: Could Unavailability of Insurance Cause a Financial Crisis?
Lack of Substitutes and Crises For lack of substitutability to cause a crisis both of the following must be true: The product must be unavailable and have no substitutes or alternative suppliers The product must be essential for the functioning of other institutions or the financial system
Quantitative Measures of Substitutability For lack of substitutability to cause a crisis: Concentration (market share of top firms) – highly concentrated markets more likely to trigger crisis due to lack of substitutes Ease of entry into the market If entry barriers exist, new entrants prevented form providing vital products or financial services Ease of entry can mitigate concerns about lack substitutability
Concentration and Regulation in Insurance Concentration in insurance: US Top 4 (10) non-life groups have 29% (50%) of market Top 4 (10) life groups have 24% (45%) of market Entry of new insurers relatively easy (both on shore and off-shore, e.g., Bermuda) Nationally significant insurers reviewed quarterly by the NAIC – Financial Analysis Working Group Therefore, widespread insolvencies causing insurance unavailability are extremely unlikely Survivors or new entrants would provide coverage
Do Insurance Products Have Substitutes? Life Insurance Mostly asset accumulation products rather than mortality/longevity risk bearing Many non-insurance substitutes for asset accumulation and investment products ● Banks, mutual funds, securities firms, etc. Many insurers available to fill coverage gaps resulting from insolvency of one or a few firms Therefore, lack of substitutes not a problem for life insurance
Do Insurance Products Have Substitutes? Property-Casualty (PC) Insurance Mainly provide risk management and risk-bearing No real substitutes for individual buyers (auto insurance) and small commercial customers ● But many insurers are available to fill coverage gaps resulting from one or a few insolvencies Large corporate buyers have substitutes – self insurance, captives, securitization Therefore, lack of substitutes not a problem for P-C insurance
Is Insurance Critical to Functioning of Economy? Insurance clearly enables the economy to function more smoothly by enabling individuals and businesses to take more risk However, it is difficult to argue that insurance is as important as banking, the payments system, or the settlement system Various insurance markets regularly experience availability crises without significantly affecting real economic activity Therefore, unavailability of insurance unlikely to create a systemic crisis
Contributing Factors: How Risky Are Insurers?
Contributing Factors: Leverage and Insolvency Rates
Equity Capital-to-Assets Ratios Source: Federal Reserve Flow of Funds accounts, American Council of Life Insurance, FDIC.
Premiums-to-Surplus Ratios: US Insurers Insurance leverage ratios have been improving over time.
Failure Rates: US Banks & Insurers Bank failure rate was more strongly affected by the crisis.
P/C Insurer Impairments: 1969-2011 Source: A.M. Best; Insurance Information Institute Financial Crisis Did Not Trigger Many Impairments.
Why Did PC Impairments Increase in 2011? Lingering effects of financial crisis Crisis appears to have had delayed effect on P-C insurers Near record catastrophe losses Insured losses of $116 billion 2 nd largest year for catastrophes in recorded history (largest was 2005 when Hurricanes Katrina, Rita, and Wilma and other events caused losses of $123 billion) “Soft market” phase of underwriting cycle PC insurance supply increases and prices decrease
PC Insurer Impairments & Combined Ratio Impairment rates highly correlated with underwriting performance. Source: A.M. Best; Insurance Information Institute Correlation with combined ratio = 64%
Life/Health Insurer Impairments:1976-2011 Life/health impairments less cyclical than P/C Source: A.M. Best.
LH Impairment Frequency & Profits Source: A.M. Best. LH less correlated with profits than PC, Corr = -20%
PC Guaranty Fund Assessments: 1978-2010 Source: A.M. Best Company, National Conference of Insurance Guaranty Funds. Correlation = 81%.
LH Guaranty Fund Assessments: 1988-2010 Source: A.M. Best, National Organization of Life and Health Insurance Guaranty Associations.
US Insurance Stock Indices vs. S&P 500 PC insurers beat the S&P during crisis, life insurers did not.
Insurer Leverage & Solvency: Conclusions US regulated insurance companies are highly solvent Insolvency rates are low Guaranty fund costs are low Financial crisis had little impact on insurer insolvencies Life insurers give some cause for concern More highly leveraged than PC but about same as banks More interconnected than PC insurers (susceptibility to affiliates) LH stocks harder hit by crisis than PC stocks Inter-connectedness does not pose serious solvency threat for PC insurers based on past experience Monolines (insurers of bonds) are a different story Not traditional insurance
Contributing Factors: Liquidity Risk and Asset-Liability Mismatches
Liquidity Risk Danger signals for life insurance industry ABS/MBS = 194% of surplus (only 27% of surplus for PC insurers) Privately placed bonds = 204% of surplus (only 10% of surplus for PC insurers) But, life insurers have significant cash from operations 43.8% of surplus 28.1% of benefit payments Conclusion: Liquidity risk exists from mortgage- backed securities and private placements for life but not PC insurers – partly offset by cash flow
Maturity Mismatches Asset and liability maturities tend to be long-term for insurers (in absolute terms and relative to banks) Property-casualty liabilities not “putable” Must experience a loss and file a claim to collect Most life insurance long-term and not putable Exceptions: cash value life insurance, variable life, and variable annuities However, usually a penalty for early surrender Conclusion: Maturity mismatch not a problem but some putability risk for life insurance
Contributing Factors: Complexity
Complexity AIG prime example of complexity Complicated group structure Geographically dispersed Complex, new financial products Large multi-national insurers common in insurance industry Life insurance more complex than PC Most life products have embedded derivatives Conclusion: Complexity is a problem for the large, multi-product, multi-national insurers
Contributing Factors: Government Policy and Regulation
Do Guaranty Funds Create Moral Hazard? In theory, mis-priced guaranty fund coverage provides incentives for excessive risk-taking In practice, guaranty funds do not seem to be a problem No solvency crisis for US regulated insurance companies – now or during Financial Crisis Guaranty fund assessments have been very low Possible rationale: Risk-based capital (introduced in 1994) blunts insurer incentives for excessive risk-taking GF protection is incomplete (low maximums, etc.)
Regulation of Complex Multi-Nationals Generally, complex multi-national financial service firms lead to gaps in regulation No one regulator has responsibility for entire firm ● Different national regulators have responsibility for nationally domiciled subsidiaries ● Banking and insurance subs may be regulated by different organizations At least in US, regulation of insurers mostly at the individual insurer rather than the group level Conclusion: Better supervision needed for insurance groups and multi-national financial services firms
Conclusions: Does Insurance Pose Systemic Risk?
P-C Insurance May Not Create Systemic Risk “Runs” are not possible To obtain funds, it is necessary to have a claim Unlike bank deposits, which are instantaneously “putable” Insurance not involved in liquidity creation, payments system, or business/consumer lending Insurers hold only small proportion of total invested assets in the economy Insurance claim payments are not a major financial asset for any economic sector However, intra-sector reinsurance exposure could cause “reinsurance spiral” spreading across the P-C industry Not clear if this would be a true systemic event, i.e., not likely to affect other financial institutions or the real economy
Does Life Insurance Pose Systemic Risk? Why LI may be systemically risky Life insurance investment products are susceptible to “runs” (withdrawals and/or suspension of premium payments/annuity considerations) Life insurers are thinly capitalized in comparison with P-C insurers, but similar to banks Life insurers hold large amounts of ABS/MBS and private placements relative to surplus Insurance guaranty fund system probably not adequate for a major run or liquidity crisis Life insurers owned by banks (and vice versa) could add to fragility of banking system
Does Life Insurance Pose Systemic Risk? Why LI may NOT be systemically risky Life insurance sector not involved in payments system, liquidity creation, credit creation, etc. Life insurers own only small proportion of stocks and bonds in the economy (about 6%) Life insurance is a small proportion of household financial assets (about 3%) Many substitutes exist for life insurance policies Life insurers not major employers (< 2% of non-farm civilian labor force) Disappearance of the entire sector would be tragic but sustainable
Systemic Risk In Insurance: Non-Core Activities As AIG debacle shows, the main systemic risk posed by the insurance industry comes from insurer participation in “banking” activities, e.g., credit default swaps (CDS) and other derivatives Swiss Re data shows that insurers and reinsurers accounted for 33% of CDS market in early 2000s As with AIG, most insurers are not adequately capitalized to sustain large CDS meltdown Insurance groups should required to increase transparency of CDS operations Tighter regulation of leverage at non-insurance subs
Overall Regulatory Implications Regulators need to improve capabilities in group supervision Regulation of non-insurance subsidiaries to head off future AIG-type crises Improved measures of group level solvency risk Regulators need to improve international coordination of insurance supervision for multi- national insurers Coordinate national regulators & the International Association of Insurance Supervisors
Systemic Risk and the Interconnectedness Between Banks and Insurers: An Econometric Analysis Hua Chen, J. David Cummins, Krupa Viswanathan, and Mary A. Weiss Presented at: 2013 CICIRM Kunming, China July 18, 2013 Copyright J. David Cummins, 2013, all rights reserved. Not to be reproduced without author’s permission.
Prior Literature: Insurers & Banks Two Prior Papers measure systemic risk in banking and insurance using market data Billio et al. (2012) – monthly stock returns ● Hedge funds, brokers, banks, and insurance companies ● Principal components and linear Granger causality test ● Conclusion: All four sectors have become highly interrelated in the past decade, increasing the level of systemic risk in the banking and insurance industries Acharya et al. (2010) – daily stock data ● Systemic expected shortfall (SES) – propensity to be undercapitalized when the system as a whole is undercapitalized ● Conclusion: 9 insurers among the top 50 systemic financial institutions
Purpose of Our Paper Develop and implement a robust systemic risk measure for insurance Investigate interconnectedness between banking and insurance during financial crisis We use CDS quotes and intra-day equity returns to estimate systemic risk in the insurance and banking industries “Are insurers instigators or victims of systemic risk?”
Purpose II Our systemic risk measure relies on Daily-frequency market price data for CDS (Markit) Intra-day trading data on stock prices (TAQ) Systemic risk measure is risk-neutral, forward- looking and economically intuitive Direction of interconnectedness investigated Linear and non-linear Granger causality Correcting for heteroskedasticity
Contribution to Literature First paper to use data on CDS spreads and intra- day stock prices to study systemic risk for the insurance industry Different econometric methodology than Billio et al. (2012) and Acharya et al. (2010) New evidence on whether insurers are victims or sources of systemic risk
Measuring Systemic Risk Two major components that determine risk profile of sample firms: Probability of default of each insurer (based on CDS premiums – Markit.com) Default correlation (estimated indirectly from underlying equity return correlation -- TAQ) Measure of systemic risk uses portfolio credit risk methodology (developed by Huang et al., 2009)
Distress Insurance Premium (DIP)
A Measure of Systemic Risk Estimate forward-looking, risk-neutral indicator of systemic risk of insurance industry: “ price of insurance against financial distress (DIP)” Define financial distress by choosing a threshold (e.g., 15%) such that the ratio of portfolio credit losses to total liabilities of the insurance sector is equal to or above threshold
A Measure of Systemic Risk II Construct a hypothetical portfolio Consists of debt instruments issued by the sample banks/insurers, weighted by the liability size of each firm. Conduct Monte Carlo simulation (Tarashev and Zhu 2008) Probability of joint default (PD) Loss given default (LGD) Systemic risk measure: the price of insurance against financial distress
A Measure of Systemic Risk III Systemic risk measure is calculated as risk-neutral expectation of portfolio credit losses that reach at least a minimum share (15%) of sector’s total liability = systemic risk measure for insurance industry L t = portfolio credit losses TL t = total liability of insurance sector at time t
A Measure of Systemic Risk IV Similar estimation procedure is performed for firms in banking sector to obtain used to analyze degree of interconnectedness between insurance and banking industries.
A Measure of Systemic Risk V Advantage of method is that does not require large sample of firms Huang et al. (2009) – 12 banks Conclusions apply to relatively large firms since they have traded CDS Large insurers have lower default probabilities than smaller insurers so results apply more strongly to small insurers
Granger Causality Tests Testing Granger causality involves using F-tests to determine whether lagged information on a variable X provides any significant information about a variable Y in the presence of lagged Y. If not, then X does not Granger-cause Y Linear Granger causality tests conducted first Then do nonlinear Granger causality tests Nonlinear Granger causality test uses the residuals from the linear causality test Then do Hiemstra-Jones (HJ) Test on residuals
Data and Systemic Risk Measures Sample Selection Sample of banks and insurers (Markit, SIC code) Check whether the firm is publicly traded on a US exchange (TAQ) Focus on 5-year, Senior, No Restructuring CDS Quotes on Friday Fill in missing values ● Use other quotes on the same day for conversion ● Trace back one (two,…, five) day(s) before ● Interpolation Determine a common time period Our sample: 11 insurers and 22 banks with CDS quotes over the period Feb 2002 to May 2008
Average Probability of Default Default probability begins to spike in 3 rd quarter of 2007.
Systemic Risk Measure After measuring probabilities of default and asset return correlations, the systemic risk measure can be computed for each week Systemic risk measure represents a weekly price of insurance against distressed losses over the following three months. To make comparisons, unit price of insurance = ratio of nominal price to total liabilities of sample firms in each sector
Linear Granger Causality Tests Distress Insurance Premiums are not stationary Differenced time series are stationary Linear causality tests are performed on the differenced series
Linear Granger Causality Tests The results imply that systemic risk of insurers Granger-causes systemic risk of banks Also, systemic risk of banks Granger-causes systemic risk of insurers However, results of BDS tests indicate that nonlinearities are present in the univariate systemic risk measures for both banks and insurers Therefore, we must conduct nonlinear Granger causality tests BDS = Brock-Dechert-Scheinkman.
Control for Conditional Heteroscedasticity If conditional heteroskedasticity exists, causality test results can be biased Residuals from Granger-causality tests reveal Little autocorrelation Conditional heteroskedasticity exists Therefore, use GARCH (1,1) model to assess whether the bi-directional causality changes Re-do linear and nonlinear Granger tests using GARCH
Non-Linear GARCH Models: Main Results Nonlinear effect of insurers on banks is highly significant at 1 lag Significance fades after 3 lags Banks in contrast have persistent predictive power on insurers up to 5 lags Systemic risk of banks has longer duration of impact on insurers Impact is also stronger than insurer effect on banks
Interconnectedness: Stress Testing Stress testing conducted to study the impact of systemic risk movements in the banking sector on the insurance sector and then vice versa A hypothetical shock in systemic risk of banking sector is fed into GARCH regression to generate future dynamic movements of systemic risk in the banking and insurance sectors Shocks of 5%, 10%, 15% and 20% are applied Systemic risk of insurance sector fed into GARCH model as well to generate future dynamic movements of systemic risk
Inter-Sector Impact of 20% Systemic Shock
Non-Linear Tests: Conclusions Banks create economically significant systemic risk for insurers but not vice versa Based on linear and non-linear Granger causality tests correcting for heteroskedasticity Therefore, insurers seem to be victims of systemic risk rather than instigators Banks are instigators of systemic risk
Systemic Risk: Policy Implications Regulators should focus on banks to prevent/ameliorate systemic shocks from banks Regulators should focus on non-core rather than insurance activities of large insurers Insurance regulators should focus on mitigating effect of shocks from banks (e.g., investment restrictions and tighter capital requirements for life insurers)
Further Information American International Group, 2009, AIG: Is the Risk Systemic? Powerpoint presentation (New York). De Bandt, Olivier and Philipp Hartmann, 2000, Systemic Risk: A Survey (Frankfurt, Germany: European Central Bank). Geneva Association, 2010, Systemic Risk in Insurance: An Analysis of Insurance and Financial Stability (Geneva, Switzerland). Group of 10, 2001, Report on Consolidation in the Financial Sector Harrington, Scott E., 2009, “The Financial Crisis, Systemic Risk, and the Future of Insurance Regulation,” Journal of Risk and Insurance 76: 785-819. Kaufman, George G., 1996, “Bank Failures, Systemic Risk, and Bank Regulation,” The CATO Journal 16: 17-45. Kaufman, George G., 2000, “Banking and Currency Crises and Systemic Risk: Lessons from Recent Events,” Federal Reserve Bank of Chicago Economic Perspectives 24: 9-28. Swiss Re, 2003, Reinsurance – A Systemic Risk, Sigma No. 5/2003 (Zurich, Switzerland). World Economic Forum, 2009, Global Risks 2009 (Geneva, Switzerland). Cummins, J. David and Mary A. Weiss, 2012, “Systemic Risk and the U.S. Insurance Sector,” working paper, Temple University, Philadelphia.
My Research on Systemic Risk Cummins, J. David and Mary A. Weiss, 2013, “Systemic Risk and the Insurance Industry,” forthcoming in Georges Dionne, ed., Handbook of Insurance, 2d ed. (Springer). Chen, Hua, J. David Cummins, Krupa Viswanathan, and Mary A. Weiss, 2013, “Systemic Risk and the Inter-Connectedness between Banks and Insurers: An Econometric Analysis,” forthcoming, Journal of Risk and Insurance. Chen, Hua, J. David Cummins, Krupa Viswanathan, and Mary A. Weiss, 2013, “Systemic Risk Measures in the Insurance Industry: A Copula Approach,” working paper, Temple University, Philadelphia Cummins, J. David and Mary A. Weiss, 2013, “Systemic Risk and the Regulation of the U.S. Insurance Industry,” working paper, Temple University, Philadelphia. Cummins, J. David and Mary A. Weiss, 2012, “Systemic Risk and the U.S. Insurance Sector,” working paper, Temple University, Philadelphia. To obtain the papers, please email: firstname.lastname@example.org.
Reinsurance Exposure: Recoverables from Non-Affiliates, P-C Insurers Reinsurance Recoverables/Policyholders Surplus From Non-affiliated Reinsurers Ceded paid losses Ceded unpaid losses Ceded IBNR Ceded unearned premiums Ceded commissions Minus funds held from reinsurers Normal range: 50% to 150% Un-weighted average for groups = 43% (2008)
Reinsurance Exposure: Ceded Reinsurance Leverage from Non-Affiliates Ceded Reinsurance Leverage From Non- Affiliated Reinsurers/Policyholders Surplus Reinsurance recoverables Ceded balances payable Ceded premiums written Minus funds held from reinsurers Normal range: Component of gross leverage. Gross leverage range is 5 to 7 Un-weighted average for groups = 70% (2008)
Reinsurance Recoverables/Surplus: Groups Reinsurance recoverables from non-affiliated reinsurers, including ceded paid losses, unpaid losses, IBNR losses, unearned premiums and commissions less funds held from reinsurers. Source: Best’s Key Rating Guide, 2009. Data are for 2008. For 26% of groups, non-affiliate recoverables > 50% of surplus
Reinsurance Receivables/Surplus: Industry Reinsurance recoverables from non-affiliated reinsurers, including ceded paid losses, unpaid losses, IBNR losses, unearned premiums and commissions less funds held from reinsurers. Source: Best’s Key Rating Guide, 2009. Data are for 2008. Non-affiliate receivables = 28% of industry surplus in 2008.
Reinsurance Leverage/Surplus: Groups Reinsurance recoverables, ceded balances payable, and ceded premiums written less funds held divided by policyholders surplus. Source: Best’s Key Rating Guide, 2009. Data are for 2008. For 30% of groups non-affiliate leverage > 75% of surplus.
Reinsurance Leverage/Surplus: Industry Reinsurance recoverables, ceded balances payable, and ceded premiums written less funds held divided by policyholders surplus. Source: Best’s Key Rating Guide, 2009. Non-affiliate leverage = 40% of industry surplus in 2008.
AIG: What Went Wrong?
US Life Insurers: 12 Month Change in Premiums (as of June 30, 2009) Source: A.M. Best Company.
AIG: What Went Wrong? AIG’s traditional insurance operations did not cause its meltdown AIG’s problems came from: Credit default swaps out of AIG Financial Products ● (Supposedly) regulated by Office of Thrift Supervision ● US insurance regulators had no jurisdiction Securities lending program of life subsidiaries ● Indicates need for more regulatory scrutiny in the future ● US regulators do have jurisdiction if lending is out of regulated life insurance subsidiaries
AIG Revenues Before the Crash 12 months ending 12/31/2006.
AIG’s Credit Default Swaps AIG sold CDS contracts, mostly to European banks (i.e., writing bond default insurance) Banks were using the swaps to reduce regulatory capital, relying on AIGs overall credit rating (regulatory arbitrage) AIG Financial Products had about $500 billion in CDS outstanding but virtually no capital AIG’s models supposedly showed that losses on the CDS portfolio were virtually impossible Losses due to model risk and managerial moral hazard
AIG’s Securities Lending Operation AIG loans securities to broker dealer or bank (e.g., to cover short selling or for diversification) Borrower posts collateral in form of cash or high quality securities AIG reinvests the collateral and earns spread between yield on invested assets and yield on underlying securities AIG had $82 billion in liabilities for securities lending as of year-end 2007, $69 billion in August 2008
AIG Securities Lending: What Went Wrong Declines in value of mortgages and other assets in 2007- 2008 reduced value of reinvested collateral in securities lending programs Many of the counterparties in the securities lending operation were the same institutions holding AIG CDS As asset values declined, borrowers terminated the securities lending arrangement to Improve liquidity Reduce exposure to AIG’s credit risk At the same time, AIG had to post additional collateral for the CDS transactions as underlying “insured” asset values declined Essentially, a “run on the bank” by AIG’s counterparties
The Bailout: Payments to AIG Counterparties CounterpartyCDS Trans.Asset LendingTotal Goldman Sachs 8.10 4.8012.90 Societe Gen.11.00 0.9011.90 Deutsche Bank 5.40 6.4011.80 Barclays 1.50 7.00 8.50 Merrill Lynch 4.90 1.90 6.80 Bank of Amer 0.70 4.50 5.20 UBS 3.30 1.70 5.00 BNP Paribus 0 4.90 Others14.7011.6026.30 Total49.6043.7093.30
US Insurer Assessments vs. AIG Total US life and P-C assessments: 1988-2010 $18.8 billion Federal assistance to AIG (as of June 30, 2009): $136 billion Not necessarily a net loss But.... Total assets of largest insurers: 2011 Met Life: $612.8 billion State Farm:$135.2 billion Source: A.M. Best Company, Harrington (2009).
Failure Costs: Met Life and State Farm (% of Life & PC Industry Assets, resp.) Source: A.M. Best Company, author’s calculations.
What Is Systemic Risk Policy Trying to Prevent? Runs on banks – traditional problem Contagion – information asymmetries Banking system collapse – Continental bank ● Correspondent bank collapse – counterparties ● Jobs would be lost Threat to settlement system Threat to payments system Fear that Infrastructure of short-term money market and OTC derivatives would not handle failure of significant counterparty Cast doubt on soundness of other counter parties (e.g., case of Bear Stearns)
Systemic Risk Policy Is Trying to Prevent II Panic due to loss of confidence Risks to system due to failure of “highly interconnected” firms “Unpredictable consequences of a failure for broader financial system” Reaction of counterparties of other firms that might come under future government control AIG was large, complex and interconnected whose failure would impose losses on counterparties and also endanger the entire world’s financial sector (Bernanke- Morehouse University)
Preview of Results After adjustment for heteroscedasticity, impact of banks on insurers found to be stronger and of longer duration than impact of insurers on banks Stress tests indicate that banks create significant systemic risk for insurers & not vice versa Insurers are primarily victims rather than instigators of systemic risk
Measuring Systemic Risk: Methodologies Huang et al. (2009): Distress Insurance Premium Acharya et al (2010): Systemic Expected Shortfall Adrian and Brunnermeier (2010): CoVAR Zhou (2010): Multivariate extreme value theory Billio et al (2012): Principal Components, Causality tests Insurers can be a source of systemic risk to some extent. 124
Nonlinear Granger Causality Tests II Diks and Panchenko (2005, 2006) HJ test could produce spurious results in the presence of conditional heteroskedasticity Ross (1989), Andersen (1996) Volatility of a time series can measure the rate of information flow. Use the GARCH model to assess whether the bi- directional causality changes.
Sample Firms II Of 11 insurers, 8 are classified as property-liability insurers Of remaining three, Lincoln National had only life-health operations MetLife group has property-liability operations Prudential divested property-liability operations, but owned these operations at the beginning of sample.