Moments convergence of powered normal extremes
Tingting Li, Zuoxiang Peng
TL;DR
This paper investigates how the moments of powered normal extremes converge under optimal normalization, revealing that convergence rates depend on the power index but this dependence vanishes in higher-order moment expansions.
Contribution
It provides new insights into the convergence behavior of powered normal extremes and identifies the influence of the power index on convergence rates.
Findings
Convergence rates depend on the power index.
Dependence on the power index disappears in higher-order expansions.
Optimal normalizing constants are identified.
Abstract
In this paper, convergence for moments of powered normal extremes is considered under an optimal choice of normalizing constants. It is shown that the rates of convergence for normalized powered normal extremes depend on the power index. However, the dependence disappears for higher-order expansions of moments.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFinancial Risk and Volatility Modeling · Stochastic processes and financial applications · Meteorological Phenomena and Simulations