arXiv:2605.18370v2 Announce Type: replace-cross Abstract: 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. A canonical starting point is Bahadur's representation, which expresses the sample quantile through the emp
This research provides deeper mathematical understanding of critical financial stability measures like Value-at-Risk in complex heavy-tailed scenarios, essential for more robust financial modeling.
Sophisticated financial models and risk management rely on accurate quantile estimation, especially for assets with heavy-tailed distributions characteristic of financial returns, impacting stability and regulatory frameworks.
The research refines the understanding of 'instability' in quantile estimation when underlying distribution parameters are also estimated, leading to more robust statistical methods.
- · Quantitative finance researchers
- · Risk management departments
- · Financial regulators
- · Overly simplistic financial models
Improved accuracy in financial risk assessments, especially for high-volatility assets.
More reliable regulatory stress tests and capital requirements based on enhanced risk modeling.
Potentially more stable financial markets due to better-understood and managed tail risks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG