A robust α-stable central limit theorem under sublinear expectation without integrability condition

Abstract

This article relaxes the integrability condition imposed in the literature for the robust α-stable central limit theorem under sublinear expectation. Specifically, for α ∈(0,1], we prove that the normalized sums of i.i.d. non-integrable random variables \n-1αΣi=1nZi \n=1∞ converge in distribution to ζ1, where (ζt)t∈ 0,1] is a multidimensional nonlinear symmetric α-stable process with a jump uncertainty set L. The limiting α -stable process is further characterized by a fully nonlinear partial integro-differential equation (PIDE) \[ \ array [c]l ∂tu(t,x)- Fμ∈ L \ ∫Rdδλαu(t,x)Fμ(dλ) \ =0,\\ u(0,x)=φ(x),\ \ \ ∀(t,x)∈ 0,1]× Rd, array . \] where \[ δλα u(t,x):= \ array [c]l u(t,x+λ)-u(t,x)- Dxu(t,x),λ 1\|λ |≤ 1\,\ α=1,\\ u(t,x+λ)-u(t,x),\ α ∈(0,1). array . \] The main tools are a weak convergence approach to obtain the limiting process, a L\'evy-Khintchine representation of the nonlinear α-stable process and a truncation technique to estimate the corresponding α-stable L\'evy measures. As a byproduct, the article also provides a probabilistic approach to prove the existence of the above fully nonlinear PIDE.

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