Anti-Robust and Tonsured Statistics

TL;DR

This paper introduces 'tonsuring,' a novel statistical method for financial data analysis that removes inlier data points to better understand market behavior during large moves.

Contribution

It presents a new approach called 'tonsuring' that differs from traditional outlier rejection by focusing on inlier removal to analyze market dynamics.

Findings

Tonsuring reveals clearer market change patterns during large moves.

The method improves understanding of tail behavior in financial data.

It offers an alternative to outlier rejection for exploratory analysis.

Abstract

This describes a statistical technique called "tonsuring" for exploratory data analysis in finance. Instead of rejecting "outlier" data that conflicts with the model, this strips out "inlier" data to get a clearer picture of how the market changes for larger moves.