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Diffusion Hitting Times from the Generator Equation

Article Quant Q&A · Author: Victor Felipe

Summary

The document introduces a one-dimensional diffusion process and its infinitesimal generator, a differential operator built from the process’s drift and volatility. It describes a familiar boundary-hitting probability: the chance of reaching a lower level before an upper level can be found by solving a generator equation with boundary values. The question then asks how discounted expectations of hitting times can likewise be represented using solutions to an eigenvalue equation, where the generator applied to a function equals the discount rate times that function.

The cited formulas express discounted probabilities of reaching a specified boundary in terms of increasing or decreasing solutions, subject to boundary conditions. This setup connects stochastic process expectations with ordinary differential equations and points toward boundary value methods and Feynman–Kac reasoning. However, the document poses the problem rather than supplying a derivation or answer. It also assumes regularity and boundary behavior without detailing the conditions needed for the formulas, so it serves as a conceptual question rather than a complete computational recipe.

Key ideas

  • A diffusion’s infinitesimal generator is a second-order differential operator determined by its drift and volatility.
  • A boundary-hitting probability can be characterized by a generator equation with boundary conditions.
  • Discounted hitting-time expectations are related to solutions of the equation where the generator equals the discount rate times the function.
  • Monotonicity and boundary conditions select the relevant increasing or decreasing solution.
  • The document raises the connection but does not provide a derivation or full regularity conditions.

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Full text
# Infinitesimal Generators and Expectation of First Hitting Time as Solution of Differential Equation


# Infinitesimal Generators and Expectation of First Hitting Time as Solution of Differential Equation












I've been learning about Linear Diffusions and how their infinitesimal generators can be used to relate expectations and deterministic differential equations.

Let $X$ be an one-dimensional diffusion with the following dynamics:

$dX_t = \mu(X_t)dt + \sigma(X_t)dW_t$

Its infinitesimal generator $\mathcal{G}$ takes the form of the following second-order differential operator:

$\mathcal{G}f(x) = \frac{1}{2}\sigma^2(x)\frac{d^2f}{dx^2}(x) + \mu(x)\frac{df}{dx}$

I've seen, for example, that when one defines: $u(x) = Pr( \tau_a < \tau_b | X_0 = x)$, with $a<b$ and $\tau_a$ and $\tau_b$ being their respective hitting times. With some regularity hypothesis under $u$ and by proceeding analogously to a "first step analysis" for discrete Markov Chains, one can show that $u$ is a solution of: $\mathcal{G}u(x) = 0$, with $u(a) = 1$ and $u(b) = 0$ as boundary conditions.

I know this approach is much more general than this simple example and can be used also in the opposite direction. I think it is actually what is done for the Dirichlet's Problem and the Feynman-Kac Formula.

In the article http://users.iems.northwestern.edu/~linetsky/cev.pdf, the authors claim that:

For such a $S$ having the following local volatility dynamics:

$dS_t = \mu S_tdt + \sigma(S_t)S_tdW_t$

The following is true:

$\mathbb{E}[e^{-r\tau_a}\mathbb{1}_{\{\tau_a < \infty\}} | S_0 = x] = \frac{\phi_r(x)}{\phi_r(a)}$, for $ x \geq a$

and

$\mathbb{E}[e^{-r\tau_b}\mathbb{1}_{\{\tau_b < \tau_0 \}} | S_0 = x] = \frac{\psi_r(x)}{\psi_r(b)}$, for $ x \leq b$

where: $\phi_r$ and $\psi_r$ are solutions of: $\mathcal{G}v = rv$.

$\psi_r$ is required to be an increasing function and $\phi_r$ is required to be a decreasing function such that: $\psi(0+) = (0)$, if $0$ is a regular boundary point (we force 0 to be a killing boundary).

I have no idea how those expectations can be expressed in terms of the solutions of this "eigenvector equation". Could someone please explain to me why? Please, I ask you to be as didactic as possible, once I'm kinda new to the subject.

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