Chapter 7: Problem 3
Prove that the probability of at least one zero of \(Y(t)\) in the interval \(\left(t_{0}, t_{1}\right)\) is \((2 / \pi) \arccos \sqrt{t_{0} / t_{1}}\).
Chapter 7: Problem 3
Prove that the probability of at least one zero of \(Y(t)\) in the interval \(\left(t_{0}, t_{1}\right)\) is \((2 / \pi) \arccos \sqrt{t_{0} / t_{1}}\).
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Get started for freeShow that the probability of the event \(\left|X\left(t_{1}\right)-X\left(t_{0}\right)\right|>\xi\), given that \(X(t)\) takes on an extreme value \([X(t)\) has two extreme values] over the interval \(\left(t_{0}, t_{1}\right)\) at either \(t_{0}\) or \(t_{1}\), is \(\exp \left(-\xi^{2} / 2\left(t_{1}-t_{0}\right)\right), t_{0}>0\)
Prove Kolmogorov's inequality for Brownian motion: $$ \operatorname{Pr}\left\\{\sup _{0 \leq u \leq t}|X(u)|>\varepsilon\right\\} \leq t / \varepsilon^{2}, \quad \varepsilon>0 . $$
Let \(\\{X(t) ; t \geq 0\\}\) be a Brownian motion process. By formally differentiating the martingale $$ \mathscr{Z}_{\theta}(t)=\exp \left\\{0 X(t)-(1 / 2) \theta^{2} t\right\\} $$ with respect to \(\theta\), show that, for each \(n, H_{n}(X(t), t)\) is a martingale, where $$ \begin{aligned} &H_{0}(x, t) \equiv 1 \\ &H_{1}(x, t)=x \end{aligned} $$ and $$ H_{n}(x, t)=x H_{n-1}(x, t)-(n-1) t H_{n-2}(x, t) $$ An alternative approach is to show that (5.2) applies.
Establish the identity $$ E\left[\exp \left\\{\lambda \int_{0}^{\prime} f(s) X(s) d s\right)\right]=\exp \left\\{\lambda^{2} \int_{0}^{\prime} f(v)\left[\int_{0}^{0} u f(u) d u\right] d v\right\\}, \quad-\infty<\lambda<\infty $$ for any continuous function \(f(s), \quad 0 \leq s<\infty\).
\(\left\\{f_{\theta}(X(t), t)\right\\}\) is a martingale for any real parameter \(\theta\), where \(f_{\theta}(x, t)=\) \(\exp \left\\{\theta x-\frac{1}{2} \theta^{2} t\right\\} .\) Use the martingale \(f_{\theta}(X(t), t)+f_{-\theta}(X(t), t)\), where \(\theta=\sqrt{2 \lambda}\) to show $$ E\left[e^{-\lambda T}\right]=\frac{1}{\cosh (\sqrt{2 \lambda} a)} $$ where \(T=\min \\{t: X(t)=+a\) or \(X(t)=-a\\}\).
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