Trimmed Lévy Processes and their Extremal Components
We analyse a trimmed stochastic process of the form ${}^{(r)}X_t= X_t - \sum_{i=1}^r Δ_t^{(i)}$, where $(X_t)_{t \geq 0}$ is a driftless subordinator on $\mathbb{R}$ with its jumps on $[0,t]$ ordered as $ Δ_t^{(1)}\ge Δ_t^{(2)} \cdots$. When $r\to\infty$, both ${}^{(r)}X_t \to 0$ and $Δ_t^{(r)} \to 0$ a.s. for each $t>0$, and it is interesting to study the weak limiting behaviour of $\bigl({}^{(r)}X_t, Δ_t^{(r)}\bigr)$ in this case. We term this "large-trimming" behaviour. Concentrating on the case $t=1$, we study joint convergence of $\bigl({}^{(r)}X_1, Δ_1^{(r)}\bigr)$ under linear normalization, assuming extreme value-related conditions on the Lévy measure of $X$ which guarantee that $Δ_1^{(r)}$ has a limit distribution with linear normalization. Allowing ${}^{(r)}X_1$ to have random centering and scaling in a natural way, we show that $\bigl({}^{(r)}X_1, Δ_1^{(r)}\bigr)$ has a bivariate normal limiting distribution, as $r\to\infty$; but replacing the random normalizations with natural deterministic ones produces non-normal limits which we can specify.