Convergence of the adapted empirical measure for mixing observations
Ruslan Mirmominov, Johannes Wiesel

TL;DR
This paper proves the convergence and concentration properties of an adapted empirical measure under mixing conditions, extending theoretical tools for stochastic process analysis and providing numerical validation.
Contribution
It establishes $ ext{AW}$-convergence and concentration inequalities for the adapted empirical measure under generalized mixing conditions, extending previous results beyond i.i.d. observations.
Findings
Proves $ ext{AW}$-convergence of the adapted empirical measure.
Derives moment bounds and sub-exponential concentration inequalities.
Extends bounded differences inequality to uncountable spaces.
Abstract
The adapted Wasserstein distance is a modification of the classical Wasserstein metric, that provides robust and dynamically consistent comparisons of laws of stochastic processes, and has proved particularly useful in the analysis of stochastic control problems, model uncertainty, and mathematical finance. In applications, the law of a stochastic process is not directly observed, and has to be inferred from a finite number of samples. As the empirical measure is not -consistent, Backhoff, Bartl, Beiglb\"ock and Wiesel introduced the adapted empirical measure , a suitable modification, and proved its -consistency when observations are i.i.d. In this paper we study -convergence of the adapted empirical measure to the population distribution , for observations satisfying a…
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Taxonomy
TopicsStochastic processes and financial applications · Risk and Portfolio Optimization · Geometric Analysis and Curvature Flows
