MDS screens assets using Fréchet variation dependence on weighted point-curve objects of returns and intraday risk, then applies standard allocation, with claimed consistency guarantees and better out-of-sample performance on Chinese high-frequency stock data.
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A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
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Large-Scale Asset Selection via Metric Dependence with Enriched High Frequency Information
MDS screens assets using Fréchet variation dependence on weighted point-curve objects of returns and intraday risk, then applies standard allocation, with claimed consistency guarantees and better out-of-sample performance on Chinese high-frequency stock data.
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A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.