AI RESEARCH
On Stability and Decomposition of Sample Quantiles under Heavy-Tailed Distributions
arXiv CS.LG
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ArXi:2605.18370v1 Announce Type: cross We study sample quantiles of distributions indexed by estimated parameters, with a on Value-at-Risk related to linear projections of financial returns that whose underlying probability law is heavy-tailed. In this setting, the projection direction and the empirical quantile threshold are estimated from the data, so the standard Bahadur representation under a fixed distribution does not separate the distinct sources of instability.