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When gambling for resurrection is too risky
Divya Kirti IMF
MFM Winter MeetingMarch 2017
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The views expressed herein are my own and should not be attributed to the IMF, its Executive Board, or its management.
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What drives risk taking by financial institutions?
• Workhorse corporate finance model: risk shifting• Particularly applied to finance, regulation
• Jensen Meckling (1976), Stiglitz Weiss (1981), Rajan (2006), Acharya Viswanathan (2011), Helmann Murdock Stiglitz (2000), Rochet (2008),Plantin Rochet (2007)
• What happens when really in trouble?• Risk shifting framework makes clear prediction: gamble for
resurrection
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Can gambling for resurrection be too risky?
• Study risk taking by US financial institutions during the crisis• Compare life insurers hit hard by crisis to those hit less hard • Instead of doubling down, insurers hit hard pulled back
• Reduced credit risk• Reduced interest-rate risk
• Maybe franchise value can make gambling for resurrection too risky
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What about regulation?
• Not just effectively tighter capital requirements: risk reduction within assets with identical regulatory treatment
• State level US insurance regulation: coordinated moral suasion unlikely
• Focus on insurance helps sharpen results and interpretation• Results are broader than insurance: same approach yields similar
results for banks
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Literature
• Risk shifting• In general and in finance: Jensen Meckling (1976), Stiglitz Weiss (1981),
Rajan (2006), Acharya Viswanathan (2011), Helmann Murdock Stiglitz (2000), Rochet (2008), Plantin Rochet (2007)
• Empirical literature: Becker Ivashina (2015), IMF (2016), Foley-FisherNarajabad Verani (2016), Dell’Ariccia Laeven Suarez (2017), Plosser Santos (2014), Hong (2017)
• Insurance• Risks, strategy: Domanski Shin Sushko (2015), Koijen Yogo (2016),
Chodorow-Reich Ghent Haddad (2016)• Regulation, response: Becker Ivashina (2015), Koijen Yogo (2016), Ellul
Jotikasthira Lundblad Wang (2015), Becker Opp (2014), Merrill Nadauld Strahan (2014), Merrill Nadauld Stulz Sherland (2014), Chodorow-Reich (2014)
• Context: Berends McMenamin Plestis Rosen (2013), IMF (2016), S&P(2014), Poterba (1997), Foley-Fisher Narajabad Verani (2015), Briys de Varenne (1997), Paulson Plestis Rosen McMenamin Mohey-Deen (2014), NAIC (2013)
• Other connections: derivatives, crisis frameworks, credit supply
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Approachand results
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Data
• Use data for 2005Q1-2014Q4• Rich data on life insurers’ assets
• Quarterly position-level data on all bond holdings• Daily position-level data on all bond transactions• Contract-level data on all interest-rate swap positions/transactions
• Caveats• Detail is for general accounts (backing term/life rather than VA)• Less detail on liabilities, use simple duration assumption
• Look at pro-forma consolidated entities
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Approach
• Focus on large insurers• Top two asset deciles by quarter• Large insurers account for 90% by general account bonds
• Measures of risk taking on two dimensions• Interest-rate risk: gap between liability DV01 and asset DV01 (bonds
and derivatives), as a fraction of liability DV01, in percentage points• Credit risk: average YTM of all bonds purchased in quarter, in basis
points• Divide sample into three periods
• Pre-crisis: 2005Q1-2007Q2 (cutoff following Becker Ivashina 2015)• Crisis: 2007Q3-2010Q4• Post-crisis: 2011Q1-2014Q4
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Identify insurers hit hard at parent level
• Measure crisis experience and level of financial health during crisis at parent level
• Look at dividends, equity/assets and equity issuance and construct insurer-level flag
• Flag as ‘hit hard’ if dividend or equity growth below 10th percentile in 2008-2010, or issued equity in 2008-2010
• This flags 24 out of 50 large insurers with matched parents
Insurers hit hard pulled back
Risk taking based on crisis experience for large insurers with matched parent
Interest-rate risk Credit Risk
Net DV01 Gap Net DV01 Gap YTM YTM
Crisis Hit Flag 6.85(1.06)
-3.22 -2.94
13.64(1.15)
-28.28 -29.00(-2.63) (-1.26) (-2.45) (-2.40)
-8.96 -8.07 -16.19 -14.29
Crisis Hit Flag × 2007Q3-2010Q4
Crisis Hit Flag × 2011Q1-2014Q4
Log(Assets)
(-2.74)
0.29(0.12)
(-2.20)
-0.31(-0.05)
(-1.08)
-19.26(-2.85)
(-0.89)
-9.61(-0.36)
Quarter FE Insurer FESE clustered byR2
Insurer-Quarters Insurers
Y N
I,Q 0.09
1,70150
Y Y
I,Q 0.83
1,70150
Y N
I,Q 0.63
1,70150
Y Y
I,Q 0.79
1,70150
Crisis hit flag: insurer-level dummy for severe dividend cuts, reduction in equity/assets ratio or equity issuance during crisis (2008-2010). SE double clustered, t-stats in parentheses.
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Insurers hit hard reduced net interest-rate exposure
Risk taking based on crisis experience for large insurers with matched parent
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Insurers hit hard took less credit risk
Risk taking based on crisis experience for large insurers with matched parent
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Franchise value, not risk shifting?Results suggest a type of ‘anti-Jensen Meckling’ effect
• Risk shifting framework predicts that insurers hit hard should have increased risk taking
• Insurers hit hard pulled back, relative to insurers hit less hard• Consistent with value from ensuring survival
• Financial institutions depend on trust, which is unlikely to survivebankruptcy
• Chodorow-Reich Ghent Haddad (2016): maybe franchise value relates to existing assets
• Other versions: private benefits from management/employment
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What about capital requirements?
• Perhaps capital requirements became effectively tighter for insurers hit hard
• Key advantage of insurance data: can examine risk taking within regulatory buckets
• Insurers hit hard reduced risk within assets with identical regulatory treatment
Risk reduction within regulatory bucketsApproach: follow Becker Ivashina (2015) within large insurers
Share of newly issued bonds bought by insurers hit hard: NAIC 1 (AAA-A) corporate bonds
2005-2007H1 2007H2-2010 2011-2014
Hit hard fraction Hit hard fraction Hit hard fraction
YTM
Duration
Tot insurer purchases
6.79(0.79)
-0.84(-0.77)
1.44(0.29)
-8.79(-2.54)
2.42(2.86)
5.75(2.49)
-11.21(-3.34)
2.20(2.86)
7.36(5.87)
Month FE Issuer FESE clustered byR2
Issues Issuers
Y Y
Iss,M0.76349173
Y Y
Iss,M0.60807276
Y Y
Iss,M0.51
1,170364
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What about moral suasion?
• US insurers are regulated at the state level• Coordination on moral suasion is unlikely• Top 10 states only cover three quarters of assets• Working Group for “nationally significant insurers” can apply “peer
pressure” on lead regulator (NAIC 2013)• Literature suggests moral suasion is unlikely
• Koijen Yogo (2016) document regulatory arbitrage across states• Ellul et al (2015) show differences in regulatory implementation
across states• Capital requirements were substantially lowered for MBS (Becker
Opp 2014, Merrill Nadauld Stulz Sherland 2014)
Additional results
• Insurers hit hard took more risk ex-ante Details
• Bond level analysis shows risk reduction within:• NAIC 2 (BBB) corporate bonds Details
• High yield corporate bonds Details
• All privately issued investment grade bonds Details
• Role of interest-rate derivatives Details
• Credit risk vs. duration Details
• All large insurers Details
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Results are broader than insuranceSame approach yields similar results for banks
• Look at dividends, equity/assets and construct bank-level flag• Large banks hit hard pulled back relative to banks hit less hard
• Lower credit growth• Less interest rate risk post-crisis
Regressions and figures
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• Helpful to compare insurers and banks• Different exposure to interest rates: really about risk• Maybe insurers’ bailout probability not high enough?• Franchise value about new business or existing assets?
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Discussion
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Franchise value can make gambling for resurrection risky
• Insurers hit hard by crisis pulled back• Reduced net interest-rate exposure• Took less credit risk
• Insurance setting addresses concerns about regulation• Risk reduction within regulatory categories• State level regulation makes moral suasion unlikely
• Results are broader, apply to banks as well
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What do we learn about risk shifting?
• Literature shows more risk taking in response to some shocks• e.g. Becker Ivashina (2015), IMF (2016), Foley-Fisher Narajabad Verani
(2016), Dell’Ariccia Laeven Suarez (2017), Plosser Santos (2014),Hong (2017)
• How should this be reconciled with an ‘anti-Jensen Meckling’ effect?• Two possibilities
• Workhorse corporate finance model fails locally (in the neighborhood of what happened in crisis)
• Something else explains increased risk documented by literature (e.g. fixed return targets, earnings and dividend smoothing)
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Implications for macro-prudential policy
• Macro-prudential perspective: might want to loosen capital requirements in a crisis
• What if capital requirements aren’t the binding constraint?• If franchise value matters, want to make survival clear: stress tests,
sufficient recapitalization
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Appendix
Insurers hit hard bought more MBS in 2005...
Net MBS purchases as a fraction of total net purchases from 2005-2014 for insurers hit hard
Notes: Net MBS purchases adjusted to exclude prepayments.Back to main presentation
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... than insurers not hit hard
Net MBS purchases as a fraction of total net purchases from 2005-2014 for insurers not hit hard
Notes: Net MBS purchases adjusted to exclude prepayments.Back to main presentation
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MBS bought in 2005 were subsequently downgradedCorporate bonds bought in 2005 were much less likely to be downgraded
Private structured bonds bought by insurers hit hard in 2005 by rating
Notes: NAIC 1 is omitted category. Restricted to private structured bonds bought in 2005, weighted by purchases in 2005.
Back to main presentation
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Bond level analysis: NAIC 2 corporate bonds
Share of newly issued bonds bought by insurers hit hard: NAIC 2 (BBB) corporate bonds
2011-20142005-2007H1 2007H2-2010
Hit hard fraction Hit hard fraction Hit hard fraction
YTM
Duration
Tot insurer purchases
3.53(0.46)
0.11(0.10)
-0.37(-0.09)
2.66(1.14)
0.85(1.21)
3.96(2.41)
-6.50(-2.66)
1.46(2.41)
6.88(5.46)
Month FE Issuer FESE clustered byR2
Issues Issuers
Y Y
Iss,M0.75509277
Y Y
Iss,M0.63977417
Y Y
Iss,M0.57
1,452541
Back to main presentation
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Bond level analysis: HY corporate bonds
Share of newly issued bonds bought by insurers hit hard: High yield (not NAIC 1 or 2) corporate bonds
2005-2007H1 2007H2-2010 2011-2014
Hit hard fraction Hit hard fraction Hit hard fraction
YTM
Duration
Tot insurer purchases
-1.28(-0.21)
-1.24(-0.54)
5.50(1.15)
-0.75(-0.37)
1.19(2.02)
4.55(3.77)
-7.11(-4.00)
1.55(3.39)
6.77(7.49)
Month FE Issuer FESE clustered byR2
Issues Issuers
Y Y
Iss,M0.88630448
Y Y
Iss,M0.59
1,784674
Y Y
Iss,M0.53
2,622873
Back to main presentation
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Bond level analysis: all private IG bondsShare of newly issued bonds bought by insurers hit hard: all privately issued investment grade bonds
2005-2007H1 2007H2-2010 2011-2014
Hit hard fraction Hit hard fraction Hit hard fraction
YTM
NAIC 2
Structured
Duration
Tot insurer purchases
0.37(0.08)
6.90(0.79)
-2.81(-0.16)
-0.51(-0.91)
5.53(4.16)
-0.83(-0.59)
3.88(0.68)
-20.46(-0.85)
1.10(2.36)
4.70(5.15)
-3.92(-2.65)
2.94(0.80)
-18.71(-1.30)
0.59(1.59)
7.13(8.70)
Month FE Issuer FESE clustered byR2
Issues Issuers
Y Y
Iss,M0.63
1,796801
Y Y
Iss,M0.59
2,6861,055
Y Y
Iss,M0.52
4,3591,484
Back to main presentation29/ 35
Insurance: role for derivatives in managing risk
Risk taking based on crisis experience for all large insurers
Including derivatives Excluding derivatives
DV01 Gap DV01 Gap DV01 Gap DV01 Gap
Crisis Hit Flag
Crisis Hit Flag × 2007Q3-2010Q4
Crisis Hit Flag × 2011Q1-2014Q4
Log(Assets)
6.85(1.06)
-3.22(-2.63)
-8.96(-2.74)
0.29(0.12)
-2.94(-1.26)
-8.07(-2.20)
-0.31(-0.05)
6.22(0.96)
-0.86(-2.39)
-4.37(-1.41)
0.98(0.39)
-0.53(-0.26)
-3.29(-0.95)
-0.27(-0.04)
Quarter FE Insurer FESE clustered byR2
Y N
I,Q 0.09
Y Y
I,Q 0.83
Y N
I,Q 0.09
Y Y
I,Q 0.86
Insurer-Quarters Insurers
1,70150
1,70150
1,70150
1,70150
Crisis hit flag: insurer-level dummy for severe dividend cuts, reduction in equity/assets ratio or equity issuance during crisis (2008-2010). SE double clustered, t-stats in parentheses.
Back to main presentation
30/ 35
Insurance: credit risk or duration?Insurers hit hard bought lower yielding bonds, not shorter-term bonds
Risk taking based on crisis experience for all large insurers
Credit risk Duration of purchases
YTM YTM Duration Duration
Crisis Hit Flag
Crisis Hit Flag × 2007Q3-2010Q4
Crisis Hit Flag × 2011Q1-2014Q4
Log(Assets)
13.64(1.15)
-28.28(-2.45)
-16.19(-1.08)
-19.26(-2.85)
-29.00(-2.40)
-14.29(-0.89)
-9.61(-0.36)
0.52(1.21)
0.69(1.90)
-0.15(-0.30)
0.02(0.15)
0.60(1.59)
-0.14(-0.27)
0.81(1.27)
Quarter FE Insurer FESE clustered by
Y N
I,Q
Y Y
I,Q
Y N
I,Q
Y Y
I,QR2
Insurer-Quarters Insurers
0.631,701
50
0.791,701
50
0.121,701
50
0.461,701
50
Crisis hit flag: insurer-level dummy for severe dividend cuts, reduction in equity/assets ratio or equity issuance during crisis (2008-2010). SE double clustered, t-stats in parentheses.
Back to main presentation
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Insurance: all large insurers
Risk taking based on crisis experience for all large insurers
Interest-rate risk
Net DV01 Gap Net DV01 Gap
Credit risk
YTM YTM
Crisis Hit Flag 1.47(0.30)
-1.89 -1.73
9.92(1.01)
-20.77 -20.92(-1.82) (-0.86) (-1.81) (-1.76)
-7.73 -7.35 -17.22 -15.74
Crisis Hit Flag × 2007Q3-2010Q4
Crisis Hit Flag × 2011Q1-2014Q4
Log(Assets)(-2.64)
2.61(1.29)
(-2.28)
1.78(0.31)
(-1.26)
-13.21(-2.17)
(-1.12)
-3.75(-0.16)
Quarter FE Insurer FESE clustered byR2
Insurer-Quarters Insurers
Y N
I,Q 0.10
2,13664
Y Y
I,Q 0.83
2,13664
Y N
I,Q 0.60
2,13664
Y Y
I,Q 0.79
2,13664
Crisis hit flag: insurer-level dummy for severe dividend cuts, reduction in equity/assets ratio or equity issuance during crisis (2008-2010). SE double clustered, t-stats in parentheses.
Back to main presentation
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Banks hit hard pulled back
Credit growth and risk taking based on crisis experience for large banks
Credit risk Interest rate risk
∆log × 100 ∆log × 100 Loans >1Yr Loans >1Yr
Crisis Hit Flag
Crisis Hit Flag × 2007Q3-2010Q4
Crisis Hit Flag × 2011Q1-2014Q4
Log(Assets)
0.31(0.87)
-1.02(-2.47)
-0.75(-1.56)
-0.31(-3.45)
-1.04(-2.41)
-0.61(-1.24)
1.17(2.11)
2.23(0.57)
-0.27(-0.17)
-6.83(-1.93)
-2.53(-2.23)
0.37(0.17)
-6.39(-1.69)
-8.73(-2.38)
Quarter FE BHC FESE clustered byR2
BHC-Quarters BHCs
Y N
B,Q 0.22
2,14454
Y Y
B,Q 0.37
2,14454
Y N
B,Q 0.07
2,14454
Y Y
B,Q 0.82
2,14454
Crisis hit flag: bank-level dummy for severe dividend cuts, reduction in equity/assets ratio during crisis (2008-2010). SE double clustered, t-stats in parentheses.
Back to main presentation
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Banks hit hard reduced credit provision
Credit growth (RE loans) based on crisis experience for large banks
Back to main presentation
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Banks hit hard took on less interest-rate risk
Asset maturities (Call reports) based on crisis experience for large banks
Back to main presentation
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