Limited liability, or how to prevent slavery in contract theory

Transcription

1 Limited liability, or how to prevent slavery in contract theory Université Paris Dauphine, France Joint work with A. Révaillac (INSA Toulouse) and S. Villeneuve (TSE) Advances in Financial Mathematics, Paris, France, January 11, 2017

2 Outline 1 2 Back to

3 Motivation B. Salanié, The economics of contracts Customers know more about their tastes than firms, firms know more about their costs than the government and all agents take actions that are at least partly unobservable. Vast economic literature revisiting general equilibrium theory by incorporating incitations and asymmetry of information. Moral hazard: situation where an Agent can benefit from an action (inobservable), whose cost is incurred by others. How to design optimal contracts?

4 Motivation B. Salanié, The economics of contracts Customers know more about their tastes than firms, firms know more about their costs than the government and all agents take actions that are at least partly unobservable. Vast economic literature revisiting general equilibrium theory by incorporating incitations and asymmetry of information. Moral hazard: situation where an Agent can benefit from an action (inobservable), whose cost is incurred by others. How to design optimal contracts?

5 Motivation B. Salanié, The economics of contracts Customers know more about their tastes than firms, firms know more about their costs than the government and all agents take actions that are at least partly unobservable. Vast economic literature revisiting general equilibrium theory by incorporating incitations and asymmetry of information. Moral hazard: situation where an Agent can benefit from an action (inobservable), whose cost is incurred by others. How to design optimal contracts?

6 Modelisation Contract between an Agent and a Principal, between 0 and T. Agent chooses his action (or effort): process α. Choice of Agent impacts the distribution of another process X t X t = X 0 + α s ds + σws α, t [0, T ], 0 where W α is a P α Brownian motion. Profit of Principal depends on X, which he observes. But α is inacessible!

7 Modelisation Contract between an Agent and a Principal, between 0 and T. Agent chooses his action (or effort): process α. Choice of Agent impacts the distribution of another process X t X t = X 0 + α s ds + σws α, t [0, T ], 0 where W α is a P α Brownian motion. Profit of Principal depends on X, which he observes. But α is inacessible!

8 Modelisation Contract between an Agent and a Principal, between 0 and T. Agent chooses his action (or effort): process α. Choice of Agent impacts the distribution of another process X t X t = X 0 + α s ds + σws α, t [0, T ], 0 where W α is a P α Brownian motion. Profit of Principal depends on X, which he observes. But α is inacessible!

9 Modelisation Contract between an Agent and a Principal, between 0 and T. Agent chooses his action (or effort): process α. Choice of Agent impacts the distribution of another process X t X t = X 0 + α s ds + σws α, t [0, T ], 0 where W α is a P α Brownian motion. Profit of Principal depends on X, which he observes. But α is inacessible!

10 Principal proposes a contract to Agent at 0. This corresponds to a salary/price/premium ξ received at T, contingent on X. Agent then solves V A 0 (ξ) := sup α E Pα [ U }{{} A Utility with U A (x) := exp( γ A x). ( ξ(x ) }{{} Salary T c 0 2 α t 2 }{{} Cost )] dt. Dependence of ξ in the whole trajectory of X is, in general, crucial. Agent faces a non Markovian stochastic control problem.

11 Principal proposes a contract to Agent at 0. This corresponds to a salary/price/premium ξ received at T, contingent on X. Agent then solves V A 0 (ξ) := sup α E Pα [ U }{{} A Utility with U A (x) := exp( γ A x). ( ξ(x ) }{{} Salary T c 0 2 α t 2 }{{} Cost )] dt. Dependence of ξ in the whole trajectory of X is, in general, crucial. Agent faces a non Markovian stochastic control problem.

12 Principal proposes a contract to Agent at 0. This corresponds to a salary/price/premium ξ received at T, contingent on X. Agent then solves V A 0 (ξ) := sup α E Pα [ U }{{} A Utility with U A (x) := exp( γ A x). ( ξ(x ) }{{} Salary T c 0 2 α t 2 }{{} Cost )] dt. Dependence of ξ in the whole trajectory of X is, in general, crucial. Agent faces a non Markovian stochastic control problem.

13 Principal proposes a contract to Agent at 0. This corresponds to a salary/price/premium ξ received at T, contingent on X. Agent then solves V A 0 (ξ) := sup α E Pα [ U }{{} A Utility with U A (x) := exp( γ A x). ( ξ(x ) }{{} Salary T c 0 2 α t 2 }{{} Cost )] dt. Dependence of ξ in the whole trajectory of X is, in general, crucial. Agent faces a non Markovian stochastic control problem.

14 Solving the Dynamic version of Agent s utility at time t is V A [ t (ξ) := esssupjt α, Jt α = E Pa U A (ξ(x ) α T t ) ] c 2 α s 2 ds F t Introduce the certainty equivalent Y := log(v A (ξ))/r A Itô s formula + classical arguments imply that Y A solves the BSDE T T Yt A = ξ + f (Z s )ds Z s σdw s, t t with f : z γ A 2 σ 2 z 2 + sup a R {az c 2 a2 } = 1 2 ( 1 c γ Aσ 2 )z 2. Optimal effort α := a (Z s ) := Zs c

15 Principal looks for Stackelberg equilibrium in two steps. (i) Compute best reaction of Agent to a contrat ξ α (ξ) P (ξ). (ii) Optimisation feedback on the contracts V P 0 [ := sup E P (ξ) U(X ξ(x ))], ξ Ξ R U: utility function of Principal, U(x) := exp( γ P x). Ξ R : contrats such that V A (ξ) R (participation constraint). Direct computation lead to linear optimal contract ξ := C + z X T, with constant effort given by z /c where z := γ P + 1 cσ 2 γ A + γ P + 1 cσ 2.

16 Principal looks for Stackelberg equilibrium in two steps. (i) Compute best reaction of Agent to a contrat ξ α (ξ) P (ξ). (ii) Optimisation feedback on the contracts V P 0 [ := sup E P (ξ) U(X ξ(x ))], ξ Ξ R U: utility function of Principal, U(x) := exp( γ P x). Ξ R : contrats such that V A (ξ) R (participation constraint). Direct computation lead to linear optimal contract ξ := C + z X T, with constant effort given by z /c where z := γ P + 1 cσ 2 γ A + γ P + 1 cσ 2.

17 z is deterministic. Contract is linear, Markovian, explicit: life is great. However, under P, X T is a drifted BM = P (X T < 0) > 0. Life is not so great for Agent...

18 z is deterministic. Contract is linear, Markovian, explicit: life is great. However, under P, X T is a drifted BM = P (X T < 0) > 0. Life is not so great for Agent...

19 z is deterministic. Contract is linear, Markovian, explicit: life is great. However, under P, X T is a drifted BM = P (X T < 0) > 0. Life is not so great for Agent...

20 z is deterministic. Contract is linear, Markovian, explicit: life is great. However, under P, X T is a drifted BM = P (X T < 0) > 0. Life is not so great for Agent...

21 Outline Back to 1 2 Back to

22 The model Back to Two more ingredients compared to before: Agent can only be paid non-negative salary. To make Principal happy, we allow him to fire Agent Therefore, contracts are now described by a pair ((ξ t ) t [0,T ], τ) salary and firing time Limited liability extension of Holmström and Milgrom s model, or finite horizon version of Sannikov s model.

23 The model Back to Two more ingredients compared to before: Agent can only be paid non-negative salary. To make Principal happy, we allow him to fire Agent Therefore, contracts are now described by a pair ((ξ t ) t [0,T ], τ) salary and firing time Limited liability extension of Holmström and Milgrom s model, or finite horizon version of Sannikov s model.

24 The model Back to Two more ingredients compared to before: Agent can only be paid non-negative salary. To make Principal happy, we allow him to fire Agent Therefore, contracts are now described by a pair ((ξ t ) t [0,T ], τ) salary and firing time Limited liability extension of Holmström and Milgrom s model, or finite horizon version of Sannikov s model.

25 The model Back to Two more ingredients compared to before: Agent can only be paid non-negative salary. To make Principal happy, we allow him to fire Agent Therefore, contracts are now described by a pair ((ξ t ) t [0,T ], τ) salary and firing time Limited liability extension of Holmström and Milgrom s model, or finite horizon version of Sannikov s model.

26 Back to Exactly as before, the certainty equivalent of Agent verifies Y A t = ξ τ + τ t f (Z s )ds τ t Z s σdw s, Clearly, utility of Agent is higher than if he were paid 0 = comparison theorem. Since f (0) = 0, certainty equivalent of Agent paid 0 IS 0 (extends to general setup as soon as c(0) = 0). Therefore Y A t 0, t [0, τ].

27 Back to Exactly as before, the certainty equivalent of Agent verifies Y A t = ξ τ + τ t f (Z s )ds τ t Z s σdw s, Clearly, utility of Agent is higher than if he were paid 0 = comparison theorem. Since f (0) = 0, certainty equivalent of Agent paid 0 IS 0 (extends to general setup as soon as c(0) = 0). Therefore Y A t 0, t [0, τ].

28 Back to Exactly as before, the certainty equivalent of Agent verifies Y A t = ξ τ + τ t f (Z s )ds τ t Z s σdw s, Clearly, utility of Agent is higher than if he were paid 0 = comparison theorem. Since f (0) = 0, certainty equivalent of Agent paid 0 IS 0 (extends to general setup as soon as c(0) = 0). Therefore Y A t 0, t [0, τ].

29 Back to State constraint reinterpretation Certainty equivalent Y A of Agent paid non-negative salary verifies t t Z s.t. Yt A = Y0 A f (Z s )ds + Z s σdw s, and Yt A Converse is true! Any non-negative payment ξ τ is the terminal value Yτ Z of a controlled diffusion as above, constrained to stay positive.

30 Back to State constraint reinterpretation Certainty equivalent Y A of Agent paid non-negative salary verifies t t Z s.t. Yt A = Y0 A f (Z s )ds + Z s σdw s, and Yt A Converse is true! Any non-negative payment ξ τ is the terminal value Yτ Z of a controlled diffusion as above, constrained to stay positive.

31 Back to Principal now solves a mixed optimal control/stopping problem with state constraints V P = sup E P (Z) [U(X T YT Z )]. (τ,z) Easy to solve the problem on the boundary y = 0 immediate stopping is optimal (otherwise, optimal stopping problem). HJB equation: u(t, x, y) =: exp( γ P (x f (t, y)), with { max f t γ Pσ 2 2 f + (1 + γ P σ 2 f y ) 2 } 2((γ A σ 2 + 1)f y + σ 2 (f yy + γ P fy 2 )) +, f y = 0, f (t, 0) = 0, f (T, y) = y,

32 Back to Principal now solves a mixed optimal control/stopping problem with state constraints V P = sup E P (Z) [U(X T YT Z )]. (τ,z) Easy to solve the problem on the boundary y = 0 immediate stopping is optimal (otherwise, optimal stopping problem). HJB equation: u(t, x, y) =: exp( γ P (x f (t, y)), with { max f t γ Pσ 2 2 f + (1 + γ P σ 2 f y ) 2 } 2((γ A σ 2 + 1)f y + σ 2 (f yy + γ P fy 2 )) +, f y = 0, f (t, 0) = 0, f (T, y) = y,

33 Back to Principal now solves a mixed optimal control/stopping problem with state constraints V P = sup E P (Z) [U(X T YT Z )]. (τ,z) Easy to solve the problem on the boundary y = 0 immediate stopping is optimal (otherwise, optimal stopping problem). HJB equation: u(t, x, y) =: exp( γ P (x f (t, y)), with { max f t γ Pσ 2 2 f + (1 + γ P σ 2 f y ) 2 } 2((γ A σ 2 + 1)f y + σ 2 (f yy + γ P fy 2 )) +, f y = 0, f (t, 0) = 0, f (T, y) = y,

34 Interpretation Back to Main findings of Sannikov Agent is not necessarily held to his reservation utility. Agent is fired in two cases: his certainty equivalent reaches 0 (bankruptcy), or it becomes to high (golden parachute) In our model, a necessary condition for golden parachutes to happen is γ P σ 2 (γ A σ 2 1) 1 Sannikov s result seems to depend heavily on the choice of utility functions. Is it due to exponential utility?

35 Interpretation Back to Main findings of Sannikov Agent is not necessarily held to his reservation utility. Agent is fired in two cases: his certainty equivalent reaches 0 (bankruptcy), or it becomes to high (golden parachute) In our model, a necessary condition for golden parachutes to happen is γ P σ 2 (γ A σ 2 1) 1 Sannikov s result seems to depend heavily on the choice of utility functions. Is it due to exponential utility?

36 Interpretation Back to Main findings of Sannikov Agent is not necessarily held to his reservation utility. Agent is fired in two cases: his certainty equivalent reaches 0 (bankruptcy), or it becomes to high (golden parachute) In our model, a necessary condition for golden parachutes to happen is γ P σ 2 (γ A σ 2 1) 1 Sannikov s result seems to depend heavily on the choice of utility functions. Is it due to exponential utility?

37 Interpretation Back to Main findings of Sannikov Agent is not necessarily held to his reservation utility. Agent is fired in two cases: his certainty equivalent reaches 0 (bankruptcy), or it becomes to high (golden parachute) In our model, a necessary condition for golden parachutes to happen is γ P σ 2 (γ A σ 2 1) 1 Sannikov s result seems to depend heavily on the choice of utility functions. Is it due to exponential utility?

38 Back to Thank you for your attention!