Lecture 2Numerical Solution of HJB Equations

Diffusion Processes, Kolmogorov Forward Equation

Benjamin MollPrinceton

EABCN Training SchoolJune 4-6, 2018

Outline

1. Numerical solution of HJB equations

2. Diffusion processes

3. Stochastic HJB equations

4. Kolmogorov Forward equations

1

Numerical Solution of HJB EquationsCodes: http://www.princeton.edu/~moll/HACTproject.htm

2

One-Slide Summary of Numerical Method• Consider general HJB equation:

ρv(x) = maxαr(x, α) + v ′(x) · f (x, α)

• Will discretize and solve using finite difference method• Discretization⇒ system of non-linear equations

ρv = r(v) + A(v)v

where A is a sparse (tri-diagonal) transition matrix

nz = 1360 10 20 30 40 50 60 70

0

10

20

30

40

50

60

70 3

Barles-Souganidis

• There is a well-developed theory for numerical solution of HJBequation using finite difference methods

• Key paper: Barles and Souganidis (1991), “Convergence ofapproximation schemes for fully nonlinear second order equationshttps://www.dropbox.com/s/vhw5qqrczw3dvw3/barles-souganidis.pdf?dl=0

• Result: finite difference scheme “converges” to unique viscositysolution under three conditions

1. monotonicity2. consistency3. stability

• Good reference: Tourin (2013), “An Introduction to Finite DifferenceMethods for PDEs in Finance.”

4

Problem we will work with: neoclassical growth model

• Explain using neoclassical growth model, easily generalized toother applications

ρv(k) = maxcu(c) + v ′(k)(F (k)− δk − c)

• Functional forms

u(c) =c1−σ

1− σ , F (k) = kα

• Use finite difference method

• Two MATLAB codeshttp://www.princeton.edu/~moll/HACTproject/HJB_NGM.m

http://www.princeton.edu/~moll/HACTproject/HJB_NGM_implicit.m

5

Finite Difference Approximations to v ′(ki)

• Approximate v(k) at I discrete points in the state space,ki , i = 1, ..., I. Denote distance between grid points by ∆k .

• Shorthand notationvi = v(ki)

• Need to approximate v ′(ki).• Three different possibilities:

v ′(ki) ≈vi − vi−1∆k

= v ′i ,B backward difference

v ′(ki) ≈vi+1 − vi∆k

= v ′i ,F forward difference

v ′(ki) ≈vi+1 − vi−12∆k

= v ′i ,C central difference

6

Finite Difference Approximations to v ′(ki)

!

"# $ # #%$

!# !#%$

!# $

Central

Backward

Forward

7

Finite Difference Approximation

FD approximation to HJB is

ρvi = u(ci) + v′i × (F (ki)− δki − ci) (∗)

where ci = (u′)−1(v ′i ), and v ′i is one of backward, forward, central FDapproximations.Two complications:

1. which FD approximation to use? “Upwind scheme”2. (∗) is extremely non-linear, need to solve iteratively:

“explicit” vs. “implicit method”

My strategy for next few slides:• what works• supplementary slides: why it works (Barles-Souganidis)

8

Which FD Approximation?• Which of these you use is extremely important• Best solution: use so-called “upwind scheme.” Rough idea:

• forward difference whenever drift of state variable positive• backward difference whenever drift of state variable negative

• In our example: definesi ,F = F (ki)− δki − (u′)−1(v ′i ,F ), si ,B = F (ki)− δki − (u′)−1(v ′i ,B)

• Approximate derivative as followsv ′i = v

′i ,F1{si ,F>0} + v

′i ,B1{si ,B<0} + v̄

′i 1{si ,F<0<si ,B}

where 1{·} is indicator function, and v̄ ′i = u′(F (ki)− δki).• Where does v̄ ′i term come from? Answer:

• since v is concave, v ′i ,F < v ′i ,B (see figure)⇒ si ,F < si ,B• if s ′i ,F < 0 < s ′i ,B, set si = 0⇒ v ′(ki) = u′(F (ki)− δki), i.e.

we’re at a steady state.9

Sparsity

• Recall discretized HJB equationρvi = u(ci) + v

′i × (F (ki)− δki − ci), i = 1, ..., I

• This can be written as

ρvi = u(ci) +vi+1 − vi∆k

s+i ,F +vi − vi−1∆k

s−i ,B, i = 1, ..., I

Notation: for any x , x+ = max{x, 0} and x− = min{x, 0}

• Can write this in matrix notation

ρvi = u(ci) +

[–s−i ,B∆k

s−i ,B∆k

–s+i ,F∆k

s+i ,F∆k

]vi−1vivi+1

and hence

ρv = u+ Av

where A is I × I (I= no of grid points) and looks like...10

Visualization of A (output of spy(A) in Matlab)

nz = 1360 10 20 30 40 50 60 70

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The matrix A

• FD method approximates process for k with discrete Poissonprocess, A summarizes Poisson intensities

• entries in row i :

−s−i ,B∆k︸ ︷︷ ︸inflowi−1≥0

s−i ,B∆k−s+i ,F∆k︸ ︷︷ ︸

outflowi≤0

s+i ,F∆k︸︷︷︸

inflowi+1≥0

vi−1

vi

vi+1

• negative diagonals, positive off-diagonals, rows sum to zero:• tridiagonal matrix, very sparse

• A (and u) depend on v (nonlinear problem)ρv = u(v) + A(v)v

• Next: iterative method...12

Iterative Method

• Idea: Solve FOC for given vn, update vn+1 according tovn+1i − vni∆

+ ρvni = u(cni ) + (v

n)′(ki)(F (ki)− δki − cni ) (∗)

• Algorithm: Guess v0i , i = 1, ..., I and for n = 0, 1, 2, ... follow1. Compute (vn)′(ki) using FD approx. on previous slide.2. Compute cn from cni = (u′)−1[(vn)′(ki)]3. Find vn+1 from (∗).4. If vn+1 is close enough to vn: stop. Otherwise, go to step 1.

• See http://www.princeton.edu/~moll/HACTproject/HJB_NGM.m

• Important parameter: ∆ = step size, cannot be too large (“CFLcondition”).

• Pretty inefficient: I need 5,990 iterations (though quite fast)13

Efficiency: Implicit Method

• Efficiency can be improved by using an “implicit method”vn+1i − vni∆

+ ρvn+1i = u(cni ) + (vn+1i )′(ki)[F (ki)− δki − cni ]

• Each step n involves solving a linear system of the form1

∆(vn+1 − vn) + ρvn+1 = u+ Anvn+1((ρ+ 1

∆)I− An)vn+1 = u+ 1

∆vn

• but An is super sparse⇒ super fast• See http://www.princeton.edu/~moll/HACTproject/HJB_NGM_implicit.m

• In general: implicit method preferable over explicit method1. stable regardless of step size ∆2. need much fewer iterations3. can handle many more grid points 14

Implicit Method: Practical Consideration

• In Matlab, need to explicitly construct A as sparse to takeadvantage of speed gains

• Code has part that looks as followsX = -min(mub,0)/dk;Y = -max(muf,0)/dk + min(mub,0)/dk;Z = max(muf,0)/dk;

• Constructing full matrix – slowfor i=2:I-1

A(i,i-1) = X(i);A(i,i) = Y(i);A(i,i+1) = Z(i);

endA(1,1)=Y(1); A(1,2) = Z(1);A(I,I)=Y(I); A(I,I-1) = X(I);

• Constructing sparse matrix – fastA =spdiags(Y,0,I,I)+spdiags(X(2:I),-1,I,I)+spdiags([0;Z(1:I-1)],1,I,I);

15

Just so you remember: one-slide summary again• Consider general HJB equation:

ρv(x) = maxαr(x, α) + v ′(x) · f (x, α)

• Discretization⇒ system of non-linear equationsρv = r(v) + A(v)v

where A is a sparse (tri-diagonal) transition matrix

nz = 1360 10 20 30 40 50 60 70

0

10

20

30

40

50

60

70

16

Diffusion Processes

17

Diffusion Processes• A diffusion is simply a continuous-time Markov process (with

continuous sample paths, i.e. no jumps)• for jumps, use Poisson process: very intuitive, briefly later

• Simplest possible diffusion: standard Brownian motion (sometimesalso called “Wiener process”)

• Definition: a standard Brownian motion is a stochastic process Wwhich satisfiesW (t + ∆t)−W (t) = εt

√∆t, εt ∼ N (0, 1), W (0) = 0

• Not hard to seeW (t) ∼ N (0, t)

• Continuous time analogue of a discrete time random walk:Wt+1 = Wt + εt , εt ∼ N (0, 1)

18

Standard Brownian Motion

• Note: mean zero, E(W (t)) = 0...• ... but blows up Var(W (t)) = t

19

Brownian Motion• Can be generalized

x(t) = x(0) + µt + σW (t)

• Since E(W (t)) = 0 and Var(W (t)) = tE[x(t)− x(0)] = µt, Var[x(t)− x(0)] = σ2t

• This is called a Brownian motion with drift µ and variance σ2

• Often useful to write this in differential form• recall ∆W (t) := W (t + ∆t)−W (t) = εt

√∆t, εt ∼ N (0, 1)

• use notation dW (t) := εt√dt, with εt ∼ N (0, 1) and write

dx(t) = µdt + σdW (t)

• This is called a stochastic differential equation• Analogue of stochastic difference equation:

xt+1 = µ+ xt + σεt , εt ∼ N (0, 1)20

21

Further Generalizations: Diffusion Processes

• Can be generalized further (suppressing dependence of x , W on t )

dx = µ(x)dt + σ(x)dW

where µ and σ are any non-linear etc etc functions.

• This is called a “diffusion process”

• µ(·) is called the drift and σ(·) the diffusion.

• all results can be extended to the case where they depend on t,µ(x, t), σ(x, t) but abstract from this for now.

• The amazing thing about diffusion processes: by choosingfunctions µ and σ, you can get pretty much any stochastic processyou want (except jumps)

22

Example 1: Ornstein-Uhlenbeck Process

• Brownian motion dx = µdt + σdW is not stationary (randomwalk). But the following process is

dx = θ(x̄ − x)dt + σdW

• Analogue of AR(1) process, autocorrelation e−θ ≈ 1− θ

xt+1 = θx̄ + (1− θ)xt + σεt

• That is, we just choose

µ(x) = θ(x̄ − x)

and we get a nice stationary process!

• This is called an “Ornstein-Uhlenbeck process”23

Ornstein-Uhlenbeck Process

• Can show: stationary distribution is N(x̄ , σ

2

2θ

)24

Example 2: “Moll Process”

• Design a process that stays in the interval [0, 1] and mean-revertsaround 1/2

µ(x) = θ (1/2− x) , σ(x) = σx(1− x)

• That isdx = θ (1/2− x) dt + σx(1− x)dW

• Note: diffusion goes to zero at boundaries σ(0) = σ(1) = 0 &mean-reverts⇒ always stay in [0, 1]

25

Other Examples

• Geometric Brownian motion:dx = µxdt + σxdW

x ∈ [0,∞), no stationary distribution:log x(t) ∼ N ((µ− σ2/2)t, σ2t).

• Feller square root process (finance: “Cox-Ingersoll-Ross”)dx = θ(x̄ − x)dt + σ

√xdW

x ∈ [0,∞), stationary distribution is Gamma(γ, 1/β), i.e.g∞(x) ∝ e−βxxγ−1, β = 2θx̄/σ2, γ = 2θx̄/σ2

• Other processes in Wong (1964), “The Construction of a Class ofStationary Markoff Processes”

26

Stochastic HJB Equations

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Stochastic Optimal Control

• Generic problem:

v (x0) = max{α(t)}t≥0

E0∫ ∞0

e−ρtr (x(t), α(t)) dt

subject to the law of motion for the state

dx(t) = f (x(t), α(t)) dt + σ(x(t))dW (t)

and α (t) ∈ A, for t ≥ 0, x(0) = x0 given

• σ could depend on α as well – easy extension

• Deterministic problem: special case σ(x) ≡ 0

• In general x ∈ RN , α ∈ RM . For now do scalar case.28

Stochastic HJB Equation: Scalar Case

• Claim: the HJB equation is

ρv(x) = maxα∈A

r(x, α) + v ′(x)f (x, α) +1

2v ′′(x)σ2(x)

• Here: on purpose no derivation (“cookbook”)

• In case you care, see any textbook, e.g. chapter 2 in Stokey (2008)

29

Just for Completeness: Multivariate Case

• Let x ∈ RN , α ∈ RM

• For fixed x , define the N × N covariance matrixσ2(x) = σ(x)σ(x)′

(this is a function σ2 : RN → RN × RN )• The HJB equation is

ρv(x) = maxα∈A

r(x, α) +

N∑i=1

∂v(x)

∂xifi(x, α) +

1

2

N∑i=1

N∑j=1

∂2v(x)

∂xi∂xjσ2i j(x)

• In vector notation

ρv(x) = maxα∈A

r(x, α) +∇xv(x) · f (x, α) +1

2tr(∆xv(x)σ

2(x))

• ∇xv(x): gradient of v (dimension N × 1)• ∆xv(x): Hessian of v (dimension N × N)

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HJB Equation: Endogenous and Exogenous State

• Lots of problems have the form x = (x1, x2)• x1: endogenous state• x2: exogenous state

dx1 = f̃ (x1, x2, α)dt

dx2 = µ̃(x2)dt + σ̃(x2)dW

• Special case with

f (x, α) =

[f̃ (x1, x2, α)

µ̃(x2)

], σ(x) =

[0

σ̃(x2)

]

• Claim: the HJB equation isρv(x1, x2) =max

α∈Ar(x1, x2, α) + v1(x1, x2)f̃ (x1, x2, α)

+v2(x1, x2)µ̃(x2) +1

2v22(x1, x2)σ̃

2(x2)31

Example: Real Business Cycle Model

v (k0, z0) = max{c(t)}t≥0

E0∫ ∞0

e−ρtu(c(t))dt

subject todk = (zF (k)− δk − c)dt

dz = µ̃(z)dt + σ̃(z)dW

for t ≥ 0, k(0) = k0, z(0) = z0 given

Here:• x1 = k, x2 = z, α = c• r(x, α) = u(α)

• f (x, α) =[x2F (x1)− δx1 − α

µ̃(x2)

], σ(x) =

[0

σ̃(x2)

]32

Example: Real Business Cycle Model

• HJB equation is

ρv(k, z) =maxcu(c) + vk(k, z)[zF (k)− δk − c]

+ vz(k, z)µ(z) +1

2vzz(k, z)σ

2(z)

33

Example: Real Business Cycle Model

• Special Case 1: z is a geometric Brownian motiondz = µzdt + σzdW

ρv(k, z) =maxcu(c) + vk(k, z)[zF (k)− δk − c]

+ vz(k, z)µz +1

2vzz(k, z)σ

2z2

See Merton (1975) for an analysis of this case

• Special Case 2: z is a Feller square root processdz = θ(z̄ − z)dt + σ

√zdW

ρv(k, z) =maxcu(c) + vk(k, z)[zF (k)− δk − c]

+ vz(k, z)θ(z̄ − z) +1

2vzz(k, z)σ

2z

34

Aside: Poisson Uncertainty

• Simplest way of modeling uncertainty in continuos time:two-state Poisson process

• zt ∈ {z1, z2} Poisson with intensities λ1, λ2

• Result: HJB equation is

ρvi(k) = maxcu(c) + v ′i (k)(ziF (k)− δk − c) + λi(vj(k)− vi(k))

for i = 1, 2, j ̸= i

35

Special Case: Stochastic AK Model with log Utility

• Preferences: u(c) = log c

• Technology: zF (k) = zk (so maybe “zk model”?)

• Productivity z follows any diffusionρv(k, z) =max

clog c + vk(k, z)(zk − δk − c)

+ vz(k, z)µ(z) +1

2vzz(k, z)σ

2(z)

• Claim: Optimal consumption is c = ρk and hence capital followsdk = (z − ρ− δ)kdt

dz = µ(z)dt + σ(z)dW

• Solution properties? Simply simulate two SDEs forward in time36

Special Case: Stochastic AK Model with log Utility

• Proof: Guess and verifyv(k, z) = ν(z) + κ log k

• FOC:u′(c) = vk(k, z) ⇔

1

c=κ

k⇔ c =

k

κ• Substitute into HJB equation

ρ[ν(z) + κ log k ] = log k − logκ+κ

k[zk − δk − k/κ]

+ ν′(z)µ(z) +1

2ν′′(z)σ2(z)

• Collect terms involving log k ⇒ κ = 1/ρ⇒ c = ρk □

• Remark: log-utility⇒ offsetting income and substitution effects offuture z ⇒ constant savings rate ρ

37

General Case: Numerical Solution with FD Method

• Want to solve:

ρv(k, z) =maxcu(c) + vk(k, z)[zF (k)− δk − c]

+ vz(k, z)µ(z) +1

2vzz(k, z)σ

2(z)

38

General Case: Numerical Solution with FD Method

It doesn’t matter whether you solve ODEs or PDEs⇒ everything generalizes

http://www.princeton.edu/~moll/HACTproject/HJB_diffusion_implicit_RBC.m

39

General Case: Numerical Solution with FD Method

• Solve on bounded grids ki , i = 1, ..., I and zj , j = 1, ..., J• Use short-hand notation vi ,j = v(ki , zj). Approximate

vk(ki , zj) ≈vi+1,j − vi ,j∆k

or vi ,j − vi−1,j∆k

vz(ki , zj) ≈vi ,j+1 − vi ,j∆z

or vi ,j − vi ,j−1∆z

vzz(ki , zj) ≈vi ,j+1 − 2vi ,j + vi ,j−1

(∆z)2

• Discretized HJB

ρvi ,j =u(ci ,j) + vk(ki , zj)(zjF (ki)− δki − ci ,j)

+ vz(ki , zj)µ(zj) +1

2vzz(ki , zj)σ

2(zj)

ci ,j =(u′)−1[vk(ki , zj)]

40

Numerical Solution: Boundary Conditions?

• Upwind method in k-dimension⇒ no boundary conditions needed

• Do need boundary conditions in z-dimension

vz(k, z1) = 0 all k ⇒ vi ,0 = vi ,1

vz(k, zJ) = 0 all k ⇒ vi ,J+1 = vi ,J

• These correspond to “reflecting barriers” at lower and upperbounds for productivity, z1 and zJ (Dixit, 1993)

41

General Case: Numerical Solution with FD Method

• Stack value function vi ,j into vector v of length I × J

• I usually stack it as “endogenous state variable first”

v = (v1,1, v2,1, ..., vI,1, v1,2, ..., vI,2, v1,3, ..., vI,J)′

• here: doesn’t really matter

• End up with system of I × J non-linear equations

ρv = u(v) + A(v)v

• Solve exactly as before

• upwind scheme• implicit method preferred to explicit method

42

Visualization of A (output of spy(A) in Matlab)

nz = 157490 500 1000 1500 2000 2500 3000 3500 4000

0

500

1000

1500

2000

2500

3000

3500

4000

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Saving Policy Function

k2 4 6 8 10 12 14

s(k,

z)

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

44

Kolmogorov Forward Equations

45

Kolmogorov Forward Equations

• Let x be a scalar diffusion

dx = µ(x)dt + σ(x)dW, x(0) = x0

• Suppose we’re interested in the evolution of the distribution of x ,g(x, t), and in particular in the stationary distribution g(x)

• Natural thing to care about especially in heterogenous agentmodels

• Example: x = wealth• µ(x) determined by savings behavior and return to

investments• σ(x) by return risk• microfound later

46

Kolmogorov Forward Equations

• Fact: Given an initial distribution g(x, 0) = g0(x), g(x, t) satisfiesthe PDE

∂g(x, t)

∂t= −

∂

∂x[µ(x)g(x, t)] +

1

2

∂2

∂x2[σ2(x)g(x, t)]

• This PDE is called the “Kolmogorov Forward Equation”

• Note: in math this often called “Fokker-Planck Equation”

• Corollary: if a stationary distribution g(x) exists, it satisfies the ODE

0 = −d

dx[µ(x)g(x)] +

1

2

d2

dx2[σ2(x)g(x)]

• Remark: as usual, stationary distribution defined as “if you startthere, you stay there”

• g(x) s.t. if g(x, t) = g(x), then g(x, τ) = g(x) for all τ ≥ t47

Just for Completeness: Multivariate Case

• Let x ∈ RN

• As before, define the N × N covariance matrix

σ2(x) = σ(x)σ(x)′

• The Kolmogorov Forward Equation is

∂g(x, t)

∂t= −

N∑i=1

∂

∂xi[µi(x)g(x, t)] +

1

2

N∑i=1

N∑j=1

∂2

∂x2[σ2i j(x)g(x, t)]

48

Application: Stationary Distribution of RBC Model

• Recall RBC Modelρv(k, z) =max

cu(c) + vk(k, z)[zF (k)− δk − c]

+ vz(k, z)µ(z) +1

2vzz(k, z)σ

2(z)

• Denote the optimal policy function bys(k, z) = zF (k)− δk − c(k, z)

• Then the distribution g(k, z, t) solves∂g(k, z, t)

∂t=−

∂

∂k[s(k, z)g(k, z, t)]

−∂

∂z[µ(z)g(k, z, t)] +

1

2

∂2

∂z2[σ2(z)g(k, z, t)]

• Numerical solution with FD method: later49

Appendix: Ito’s Lemma

50

Ito’s Lemma

• Let x be a scalar diffusiondx = µ(x)dt + σ(x)dW

• We are interested in the evolution of y(t) = f (x(t)) where f is anytwice differentiable function

• Lemma: y(t) = f (x(t)) follows

df (x) =

(µ(x)f ′(x) +

1

2σ2(x)f ′′(x)

)dt + σ(x)f ′(x)dW

• Extremely powerful because it says that any (twice differentiable)function of a diffusion is also a diffusion

• Can also be extended to vectors

• FYI: this is also where the v ′(x)µ(x) + 12v ′′(x)σ2(x) term in theHJB equation comes from (it’s E[dv(x)]dt )

51

Application: Brownian vs. Geometric Brownian Motion

• Let x be a geometric Brownian motion

dx = µxdt + σxdW

• Claim: y = log x is a Brownian motion with drift µ− σ2/2 andvariance σ2

• Derivation: f (x) = log x , f ′(x) = 1/x, f ′′(x) = −1/x2.By Ito’s Lemma

dy = df (x) =

(µx(1/x) +

1

2σ2x2(−1/x2)

)dt + σx(1/x)dW

=(µ− σ2/2

)dt + σdW

• Note: naive derivation would have used dy = dx/x and hence

dy = µdt + σdW wrong unless σ = 0!52

Just for Completeness: Multivariate Case

• Let x ∈ RN . For fixed x , define the N × N covariance matrixσ2(x) = σ(x)σ(x)′

• Ito’s Lemma:

df (x) =

N∑i=1

µi(x)∂f (x)

∂xi+1

2

N∑i=1

N∑j=1

σ2i j(x)∂2f (x)

∂xixj

dt+

N∑i=1

σi(x)∂f (x)

∂xidWi

• In vector notation

df (x) =

(∇x f (x) · µ(x) +

1

2tr(∆x f (x)σ

2(x)))dt +∇x f (x) · σ(x)dW

• ∇x f (x): gradient of f (dimension m × 1)• ∆x f (x): Hessian of f (dimension m ×m)

53