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Deriving the Diffusion HJB Equation from the Generator

Article Quant Q&A · Author: Pcw.

Summary

The document derives the Hamilton–Jacobi–Bellman equation for an infinite-horizon discounted payoff from a one-dimensional diffusion. It splits the value into near-term utility and continuation value, applies Itô’s formula, and takes a small-time limit. The key step is to take the conditional expectation of the stochastic integral: its Brownian component has zero expectation, while the drift component contributes the drift times the derivative of the value function. The resulting equation equates discounted value to current utility plus the diffusion generator applied to value.

The argument assumes sufficient regularity of the value function and suitable conditions on the diffusion for the expectations and limit to be valid. It illustrates how the generator appears in a basic no-control setting; it does not develop the optimization step or boundary conditions needed for more general HJB problems.

Key ideas

  • The conditional expectation of an Itô integral against Brownian motion is zero under suitable integrability conditions.
  • The drift part of the state process contributes the drift multiplied by the derivative of the value function.
  • The second-order term in the diffusion generator comes from quadratic variation.
  • The discounted value equation balances current utility against the generator and discount rate.

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Full text
# Derivation of HJB equation


# Derivation of HJB equation












I am trying to derive the HJB equation in a stochastic setting. Let me exemplify my problem with the simplest case where there is no control, just one state variable. Assume the payoff is given by $$ V(X_{t})\equiv E_{t}\left\{ \int_{t}^{\infty}e^{-\rho(s-t)}u(X_{s})ds\right\} $$ where $X_{t}$ is given by $$ dX_{t}=\mu(X_{t})dt+\sigma(X_{t})dZ_{t} $$ and $Z_{t}$ is the standard Brownian Motion. For any $dt>0$ we can write: $$ V(X_{t})=E_{t}\left\{ \int_{t}^{t+dt}e^{-\rho(s-t)}u(X_{s})ds+e^{-\rho dt}V(X_{t+dt})\right\} $$

$$ \left(1-e^{-\rho dt}\right)V(X_{t})=E_{t}\left\{ \int_{t}^{t+dt}e^{-\rho(s-t)}u(X_{s})ds+e^{-\rho dt}\left[V(X_{t+dt})-V(X_{t})\right]\right\} \tag{1} $$

From Ito calculus we get that (and assuming that $W(\cdot)$ is well behaved): $$ V(X_{t+dt})-V(X_{t})=\int_{t}^{t+dt}V'(X_{s})dX_{s}+\frac{1}{2}\int_{t}^{t+dt}V''(X_{s})d[X_{s}]=\int_{t}^{t+dt}V'(X_{s})dX_{s}+\frac{1}{2}\int_{t}^{t+dt}\sigma(X_{s})^2V''(X_{s})ds $$ where the last equality follows from the known properties of the quadratic variation of the process $X_{t}$. Plugging this back in (1): $$ \left(1-e^{-\rho dt}\right)V(X_{t})=E_{t}\left\{ \int_{t}^{t+dt}e^{-\rho(s-t)}u(X_{s})ds+e^{-\rho dt}\left[\int_{t}^{t+dt}V'(X_{s})dX_{s}+\frac{1}{2}\int_{t}^{t+dt}\sigma(X_{t})^2V''(X_{s})ds\right]\right\} $$

Dividing both sides by $dt$ and taking the limit $dt\rightarrow0$: $$ \rho V(X_{t})=E_{t}\left\{ u(X_{t})+\lim_{dt\rightarrow0}\frac{\int_{t}^{t+dt}V'(X_{s})dX_{s}}{dt}+\frac{1}{2}\sigma(X_{t})^2V''(X_{t})\right\} $$ where I used the fact that when dealing with the Riemann integral: $\lim_{dt\rightarrow0}\frac{\int_{t}^{t+dt}f(x_{s})ds}{dt}=f(x_{t})$ (from standard calculus).

As you can see, I am almost there. I just don't know how to deal with term $\lim_{dt\rightarrow0}\frac{\int_{t}^{t+dt}V'(X_{s})dX_{s}}{dt}$. For example, assume $\mu(X_{t})=0$ and $\sigma(X_{t})=1$, so that $X_{t}$ is simply the standard Brownian Motion $Z_{t}$. In that case, to get the HJB formula right I would need: $$ \lim_{dt\rightarrow0}\frac{\int_{t}^{t+dt}V'(Z_{s})dZ_{s}}{dt}=0 $$ But I don't know how to prove that this is true. More generally (for any $\mu(X_{t})$ and $\sigma(X_{t})$), I would need to prove: $$ \lim_{dt\rightarrow0}\frac{\int_{t}^{t+dt}V'(X_{s})dX_{s}}{dt}=\mu(X_{t})V'(X_{t}) $$ which I am also not sure how to do. Any ideas?

## Answer by AFK (score 1)

https://quant.stackexchange.com/a/17934

By using the fact that the brownian integral has expected value $0$, we find \begin{eqnarray*} & & \left(1-e^{-\rho dt}\right)v(X_{t}) \\ &=& E_{t}\left\{ \int_{t}^{t+dt}e^{-\rho(s-t)}u(X_{s})ds+e^{-\rho dt}\left[\int_{t}^{t+dt}v'(X_{s})dX_{s}+\frac{1}{2}\int_{t}^{t+dt}\sigma(X_{s},s)^2 v''(X_{s})ds\right]\right\} \\ &= & E_{t}\left\{ \int_{t}^{t+dt}e^{-\rho(s-t)}u(X_{s}) +e^{-\rho dt}\left(v'(X_{s})\mu(s,X_s) +\frac{1}{2}\sigma(X_{s},s)^2 v''(X_{s})\right)ds\right\} \end{eqnarray*} So the PDE is \begin{eqnarray*} \rho v(x) &=& u(x) + \left(\mu(t,x) v'(x) +\frac{1}{2}\sigma(t,x)^2 v''(x)\right) \end{eqnarray*} or \begin{eqnarray*} (- \mathcal{A}_t + \rho) v(x) &=& u(x) \end{eqnarray*} where $\mathcal{A}_t = \mu(t,x)\partial_x + \frac{1}{2}\sigma(t,x)^2 \partial_x^2$ is the generator of the diffusion.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.