Scaling conditional tail probability and quantile estimators

TL;DR

This paper introduces a new method for scaling high-frequency tail probability and quantile estimates of the conditional return distribution, improving risk assessment in financial markets.

Contribution

The paper proposes a novel procedure specifically designed for scaling high-frequency tail estimates in conditional return distributions.

Findings

Enhanced accuracy in tail probability estimation

Improved quantile scaling for risk management

Applicable to high-frequency financial data

Abstract

We present a novel procedure for scaling relatively high frequency tail probability and quantile estimates for the conditional distribution of returns.