Question

The expression for $u(x)$ in (12.4) appears to break down when $\mu=\frac{1}{2} \sigma^2$. By letting $\mu=\frac{1}{2} \sigma^2+\epsilon$ and considering the limit $\epsilon \rightarrow 0$, show that a sensible mean exit time formula can be established in this special case.

   The expression for $u(x)$ in (12.4) appears to break down when $\mu=\frac{1}{2} \sigma^2$. By letting $\mu=\frac{1}{2} \sigma^2+\epsilon$ and considering the limit $\epsilon \rightarrow 0$, show that a sensible mean exit time formula can be established in this special case.
 
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An Introduction to the Numerical Simulation of Stochastic Differential Equations
An Introduction to the Numerical Simulation of Stochastic Differential Equations
Desmond J. Higham,… 1st Edition
Chapter 12, Problem 6 ↓

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## Step-by-step solution for the mean exit time formula when μ = σ²/2  Show more…

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The expression for $u(x)$ in (12.4) appears to break down when $\mu=\frac{1}{2} \sigma^2$. By letting $\mu=\frac{1}{2} \sigma^2+\epsilon$ and considering the limit $\epsilon \rightarrow 0$, show that a sensible mean exit time formula can be established in this special case.
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Key Concepts

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Mean Exit Time
The mean exit time represents the expected time until a stochastic process leaves a specified domain. It is a fundamental quantity in the study of stochastic processes and is obtained by solving a boundary value problem associated with the generator of the process.
Stochastic Differential Equations
Stochastic differential equations (SDEs) describe systems influenced by random noise, typically represented with drift and diffusion terms. In this context, the parameters ? and ?^2 define the drift and diffusion coefficients, and special cases of these parameters can lead to unique behaviours in the solutions.
Perturbation Methods
Perturbation methods involve introducing a small parameter (such as ?) to study the behavior of a system near a problematic or critical point. By considering ? = (1/2)?^2 + ? and analyzing the limit as ? approaches 0, one can resolve apparent singularities in the expression for the mean exit time.
Asymptotic Analysis
Asymptotic analysis is used to examine the behavior of functions as a parameter approaches a particular limit. In this problem, it helps in determining the limiting behaviour of the mean exit time expression as the drift parameter approaches a critical value, yielding a sensible result despite the apparent singularity.
Removable Singularity
A removable singularity occurs when an expression appears to be undefined or divergent at a particular parameter value, but through appropriate limiting procedures, a well-defined finite result is obtained. This concept demonstrates that the breakdown in the original expression is only superficial and can be resolved through careful analysis.

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