Ray-Knight theorems

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Suppose ff is a non-negative continuous function whose support [a,b][a, b] is a subset of (0,1)(0,1). Show that there is a unique solution to the ordinary differential equation F′′(x)=2F(x)f(x)F^{\prime \prime }(x)=2 F(x) f(x), F(1)=1,F′(1)=0F(1)=1, F^{\prime }(1)=0, that FF is everywhere positive, and FF is bounded on [0,∞)[0, \infty ).

Hint: Since ff is zero in (b,∞)(b, \infty ), then F′′F^{\prime \prime } is zero there, and hence is of the form F(x)=Ax+BF(x)=A x+B for some AA and BB for x≥bx \geq b. Since F′(1)=0F^{\prime }(1)=0, conclude that AA is 0 .

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Let WW be a Brownian motion and LtyL_{t}^{y} the local times at level yy. Prove that local times at a fixed time tt are not a Markov process. That is, let t>0t>0 be fixed and show that (Lty,y≥0)\left(L_{t}^{y}, y \geq 0\right) is not a Markov process in the variable yy.

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Let SS be the first time two-dimensional Brownian motion exits the unit ball and let ψ(λ)=P0(S>λ)\psi (\lambda )=\mathbb {P}_{0}\left(S>\lambda \right). If WW is a one-dimensional Brownian motion with local times LtxL_{t}^{x} and T=inf⁡{t>0:Wt=1}T=\inf \left\{ t>0: W_{t}=1\right\}, find the distribution of Y=sup⁡0≤x≤1LTxY=\sup_{0 \leq x \leq 1} L_{T}^{x} in terms of ψ\psi, i.e., write P(Y≤λ)\mathbb {P}\left(Y \leq \lambda \right) in terms of the function ψ\psi.

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Suppose x∈(0,1)x \in (0,1). With WW and TT as in Exercise 26.5, find the distribution of LTxL_{T}^{x}.

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Let WW be a one-dimensional Brownian motion with local times LtxL_{t}^{x}. Let Tr=inf⁡{t>0:Lt0=r}T_{r}=\inf \left\{ t>0: L_{t}^{0}=r\right\}. The law of the process x→LTrxx \rightarrow L_{T_{r}}^{x} can be described as follows:

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(1)

The law of {LTrx,x≥0}\left\{ L_{T_{r}}^{x}, x \geq 0\right\} is the same as the law of {Xx,x≥0}\left\{ X_{x}, x \geq 0\right\} started at rr, where XX is the square of a Bessel process of order 0 .

(2)

The law of {LTr−x,x≥0}\left\{ L_{T_{r}}^{-x}, x \geq 0\right\} is also the same as the law of {Xx,x≥0}\left\{ X_{x}, x \geq 0\right\} started at rr, where XX is the square of a Bessel process of order 0 .

(3)

The processes {LTrx,x≥0}\left\{ L_{T_{r}}^{x}, x \geq 0\right\} and {LTr−x,x≥0}\left\{ L_{T_{r}}^{-x}, x \geq 0\right\} are independent of each other.

This is proved in Revuz and Yor (1999), Section XI.2, or for a challenge, try to prove (1) for yourself using the techniques of this chapter. Using this description of LTrxL_{T_{r}}^{x}, find the distribution of LTr∗=sup⁡xLTrxL_{T_{r}}^{*}=\sup_{x} L_{T_{r}}^{x}.

References

[Bas11] Bass R.F. Stochastic Processes. Cambridge University Press, 2011.