Peer Monitoring via Loss Mutualization

Transcription

1 Peer Monitoring via Loss Mutualization Francesco Palazzo Bank of Italy November 19, 2015 Systemic Risk Center, LSE

2 Motivation Extensive bailout plans in response to the financial crisis... US Treasury disbursed $313 bn to financial industry through TARP. Euro Area governments incurred net cost of e 178 bn in asset relief programs, recapitalizations, guarantees, etc....pushed governments to pass legislation aimed at reducing future bailout costs on taxpayers. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

3 Policy response Financial sector should bear a higher share of losses: Bank resolution funds (Dodd Frank Title II, BRRD) Mandatory clearing via CCPs (Dodd Frank Title VII, EMIR) European Deposit Insurance Scheme? (under discussion) Current loss mutualization schemes share an atomistic perspective: Contributions to loss sharing funds proportional to bank riskiness. Different mix of prefunded and ex post contributions. Focus exclusively on loss absorption capacity in case of default. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

4 Idea Main idea: loss mutualization may be used as a tool to allocate losses in a way that fosters peer discipline among banks. Main model ingredients: Banks subject to moral hazard. Banks have superior skills to assess other banks credit risk and they trade in an interbank market. Each bank knows the identity of its counter parties in the interbank market (OTC market). Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

5 Main results Q&A on optimal loss sharing design to enhance peer discipline: How shall we distribute losses (beyond the defaulter s contributions) among surviving banks? Allocate losses only to banks exposed to the defaulter. How large should be optimal contributions? Reduce bank shareholders payoff to zero. Less effective when banks face less credit risk from their exposures? Irrelevant under optimal scheme. Otherwise, higher contagion risk favours peer discipline. Role of costly prefunded resources ( skin in the game )? They substitute and reinforce peer discipline. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

6 Literature Peer monitoring: Stiglitz (1990), Varian (1990), Ghatak (2000). Interbank discipline: Rochet & Tirole (1996), De Young et al. (1998), Peek et al. (1999), Furfine (2002). CCPs: Biais et al. (2012b), Antinolfi et al. (2014), Zawadowski (2013). Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

7 Model Players: N (even) identical banks & a competitive sector of investors. Universal risk neutrality. Timing: t = 0, 1, 2, 3. Investment Technology Pay I > 0 at t = 0 and receive 0 (with prob d i ) or R > 0 at t = 3. d i is realized at t = 2 and depends on the effort choice at t = 1: Effort costs c > 0 and it leads to d i =d with prob. α 0 or d i =0. Without effort d i =d. where d G ( ) is common to all banks and has expected value m. If k N banks exert effort, the probability that l are safe is given by a correlated binomial pmf P k (l) with k P k (l) k l k = α. l=0 Effort decisions are not observable. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

8 Model Investors contract at t = 0. Bank i offers a contract (p i, k i ): p i is the amount to reimburse at t = 3. k i is a pre-payment at t = 0 and it costs µ > 1 per unit. Final payoff at t = 3: π i = R p i + k i Interbank market at t = 2 At t = 2 all banks observe d = (d 1,..., d N ) and simultaneously decide to match with another bank. Banks can only enter a bilateral transaction with another bank: Trading avoids a loss L > but increases default risk: d i + (1 d i )d j γ Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

9 Timing bank i exerts unobservable effort e i Contracts enforced unless default 0 bank i offers (p i, k i ). Investors accept or reject 1 2 d = (d 1,..d N ) realize. banks match and and decide to trade or incur a loss L 3 t I restrict attention to symmetric subgame perfect equilibria. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

10 Interbank market Common knowledge of (d 1,..., d N ). Final payoff π i determined at t = 0. Bank i payoff displays strong monotonicity with respect to d i, d j : (1 d i )(1 d j γ)π i 1. Threat of ostracism Bank i accepts to trade with bank j only if: (1 d i )(1 d j γ)π i (1 d i )π i L L d j (1 d i )γπ i For simplicity, I consider parameters s.t. two risky banks trade for all d. 2. Endogenous self-selection and positive assortative matching Suppose l banks are safe and N l are risky. In a stable matching: If l is even, all pairs include banks of identical credit risk. If l is odd, all pairs include banks of identical credit risk except one. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

11 Effort Choice - Matching Probabilities If all N banks exert effort, the probability p j at t = 1 (effort decision) is: [ I {l even} + p ss = N l=0 P N (l) l N p rr = N P N (l) N l l=0 N p rs = p sr = 1 N [ N P N (l)i {l odd} l=0 ( 1 1 l ) I {l odd} ] =1 α 1 N I {l even} + N l 1 ] I N l {l odd} Focus on N case, hence p rs = p sr = 0. = α 1 N N P N (l)i {l odd} l=0 N P N (l)i {l odd} l=0 Let q rs be the probability that, after shirking, a risky bank matches with a safe bank, assuming the other N 1 banks exerted effort. q rs = N 1 l=0 1 P N 1 (l) N l I {l odd} Perfect correlation: q rs = 1 α Independence: q rs = 0. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

12 Effort Choice Exert effort: [ ] 1 E ei =1[u i π] = p ss π i + p rr (1 x)(1 γx)g(x) dx π i c 0 Shirk: ( )] L E ei =0[u i π] = (1 m)π i q rs [1 1 G L π i γ(1 q rs ) x(1 x)g(x) dx γπ j 0 Incentive compatibility constraint: ( )] c q rs [1 G L γπj L π i m(1 α) + γ(1 α q rs ) 1 x(1 x)g(x) dx := ξ(π j ) (IC) 0 If involuntary credit risk depends exclusively on: Macro shock: q rs = 1 α Threat of ostracism Idiosyncratic factors: q rs = 0 Endogenous self-selection Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

13 Incentive Compatible Contract First best max investment I = sr c (s prob. of surviving) Incentive compatible contract. max p i,k i s(r p i + k i ) µk i c s.t. R p i + k i ξ(π j ) (IC) sp i + (1 s)k i I (IR) IC equilibrium: 0 I c I k I k i = 0 k i > 0 No investment Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

14 Loss Mutualization Scheme All N banks participate to the loss sharing scheme. I exclude the possibility to reward a bank. In case of a bank s default, its investors may receive payments from other banks at t = 3. Investors are risk-neutral and transfers only serve for incentives. Loss sharing contributions can be interpreted as penalties. τ 0 : penalty if a bank did not trade with any bank. τ 1 : penalty if bank traded with a defaulter. No penalties on banks which traded with a non-defaulting peer. Otherwise, more stingent IC constraint but no welfare improvement. Positive assortative matching continues to hold. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

15 Loss Mutualization Scheme Focus on N + case. Ex-ante probability to trade is one. ( )] L c q rs [1 G L τ γπ j +(1 γ)τ 1 τ 0 1 (1 q rs α)(1 γ) 1 x(1 x)g(x) dx 0 π i m(1 α) + γ(1 q rs α) 1 x(1 x)g(x) dx 0 Transfers affect the IC constraint via both peer discipline mechanisms: ( )] L Threat of ostracism: q rs [1 G L γπ j +(1 γ)τ 1 τ 0 Endogenous self-selection: τ 1 (1 q rs α)(1 γ) 1 x(1 x)g(x) dx 0 Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

16 Loss Mutualization Scheme Let s and z be the prob. to survive and to pay τ 1. Max program: max p i,k i s(r p i + k i ) µk i zτ 1 c s.t. R p i + k i ξ(π j, τ 0, τ 1 ) (IC) sp i + (1 s)k i + zτ 1 I For a given (τ 0, τ 1 ) the max investment levels are: I c = I sξ(r I zτ 1 s, τ 0, τ 1 )+ c+ zτ 1 I k = I µ 1 µ [sξ(π τ, τ 0, τ 1 )+ c+zτ 1 ] where π τ solves π = ξ(π, τ 0, τ 1 ). (IR) Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

17 Proposition Optimal Loss Mutualization Scheme The optimal loss contributions are τ 0 = 0, τ 1 = π c, where π c is the solution to π = ξ(π, 0, π). Impose the highest penalty on shareholders only if a bank has previously traded with a defaulter. Importance of punishing informed counter parties. In bilateral interbank market it occurs via direct losses. Under the optimal scheme the IC constraint is γ independent: ( )] c q rs [1 G Lπ L π m(1 α) + (1 α q rs ) 1 x(1 x)g(x) dx 0 Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

18 Extension I Information acquisition Immediately after exerting effort banks have to decide whether to pay a cost c d > 0 to observe other banks default proababilities. Set up a plausible microfoundation of the matching process. Out-of-equilibrium, a bank with no information on others credit risk has to match with informed counter parties. IC constraint for information acquisition is: c d (1 α)(1 E[n s d i = 0])m(γπ i + (1 γ)τ 1 ) Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

19 Extension II Interbank collateral Extend model with a loss distribution and the possibility to post costly collateral to other banks at t = 2. Interbank collateral reduces the threat of ostracism. A risky bank uses collateral to bribe a safe bank and reduce its loss given default. Crucial difference between collateral posted to investors before effort choice, and to other banks once a bank becomes risky. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20

20 Limitations A more realistic framework should include many interbank counter parties, different bank sizes, and multiple financial contracts available. With multiple bank relationships, how should we measure a closer bank relationship? Difficult to punish bank shareholders as much as possible. In a dynamic context a very high punishment may create future incentives for misconduct. Risk-sharing considerations may call for loss sharing contributions also from banks with no trading relationships with the defaulter. However, an unequal distribution should still apply to foster peer discipline incentives. Francesco Palazzo (Bank of Italy) Peer Monitoring via Loss Mutualization November 19, / 20