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We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns.

Y et, under the strongly-aligned regime, KM suffers the saturation effect, while TKM can be continuously improved as the alignment becomes stronger. This further implies that TKM has a strong ability to capture the strong alignment and provide a theoretically guaranteed solution to eliminate the phenomena of saturation effect.

Conformal prediction is atechnique for constructing prediction intervals that attainvalidcoverage infinite samples, without making distributional assumptions. Despite this appeal, existing conformal methods can be unnecessarily conservativebecause theyform intervals ofconstant orweakly varying length across the input space.

Empirical risk minimization (ERM) is a crucial framework that offers a general approach to handling a broad range of machine learning tasks.

Distributed learning has attracted significant attention recently with the increasing popularity of distributed systems and distributed data.