When gambling for resurrection is too risky MFM... · • Divide sample into threeperiods ... reduction in equity/assets ratio or equity issuance during crisis (2008-2010). ... 630

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


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


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Insurers hit hard took less credit risk


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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


YTM

Duration


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)


Issues Issuers

Y Y

Iss,M0.75509277

Y Y

Iss,M0.63977417

Y Y

Iss,M0.57

1,452541


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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


YTM

Duration


-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)


Issues Issuers

Y Y

Iss,M0.88630448

Y Y

Iss,M0.59

1,784674

Y Y

Iss,M0.53

2,622873


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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


YTM

NAIC 2

Structured

Duration


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)


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



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)


Y N

I,Q 0.09

Y Y

I,Q 0.83

Y N

I,Q 0.09

Y Y

I,Q 0.86


1,70150

1,70150

1,70150

1,70150



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Insurance: credit risk or duration?Insurers hit hard bought lower yielding bonds, not shorter-term bonds


Credit risk Duration of purchases

YTM YTM Duration Duration

Crisis Hit Flag



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


0.631,701

50

0.791,701

50

0.121,701

50

0.461,701

50



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Insurance: 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



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)



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



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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



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.


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Banks hit hard reduced credit provision

Credit growth (RE loans) based on crisis experience for large banks


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Banks hit hard took on less interest-rate risk

Asset maturities (Call reports) based on crisis experience for large banks


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Documents

When gambling for resurrection is too risky MFM... · • Divide sample into threeperiods ... reduction in equity/assets ratio or equity issuance during crisis (2008-2010). ... 630