Law of large numbers and central limit theorem under nonlinear expectations

By Shige Peng. Probability, Uncertainty and Quantitative Risk, 2007

103 citations recorded by the source corpus.

The main achievement of this paper is the finding and proof of Central Limit Theorem (CLT, see Theorem 12) under the framework of sublinear expectation. Roughly speaking under some reasonable assumption, the random sequence {1/n(X1+⋯+Xn)}i=1∞$\{1/\sqrt {n}(X_{1}+\cdots +X_{n})\}_{i=1}^{\infty }$ converges in law to a nonlinear normal distribution, called G-normal distribution, where {Xi}i=1∞$\{X_{i}\}_{i=1}^{\infty }$ is an i.i.d. sequence under the sublinear expectation. It’s known that the framework of sublinear expectation provides a important role in situations that the probability measure itself has non-negligible uncertainties. Under such situation, this new CLT plays a similar role as the one of classical CLT. The classical CLT can be also directly obtained from this new CLT, since a linear expectation is a special case of sublinear expectations. A deep regularity estimate of 2nd order fully nonlinear parabolic PDE is applied to the proof of the CLT. This paper is originally exhibited in arXiv.(math.PR/0702358v1).

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ea28b911c4dfd976db5a3b31f93b8bd16093f72d

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Citations in the corpus

Strong laws of large numbers for capacities (2010, background): “Recently, motivated by the risk measures, super-hedge pricing and modelling uncertainty in finance, Peng [11,12,13,14,15] initiated the notion of independent and identically distributed (IID) random variables under sub-linear expectations.”
Limit theorems with rate of convergence under sublinear expectations (2017, methodology): “The theorem then follows from the error bound by Song (2017) for Peng (2007)’s CLT. 4.2 Representation of G-normal distribution Roughly speaking, the continuous-time form of Eq.”