A new spatial-sign max-type test for high-dimensional alpha is asymptotically independent of an existing sum-type test, allowing a Cauchy-combined adaptive procedure with robust power against sparse and dense alternatives.
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Neural generative model learns continuous term structures of risk-neutral moments from standard normals, enforces no-arbitrage, and extracts densities that price options more accurately and stably than three parametric plus nine stochastic baselines.
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Robust Spatial-Sign-Based Testing of High-Dimensional Alpha in Conditional Factor Models
A new spatial-sign max-type test for high-dimensional alpha is asymptotically independent of an existing sum-type test, allowing a Cauchy-combined adaptive procedure with robust power against sparse and dense alternatives.
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Risk-Neutral Generative Networks
Neural generative model learns continuous term structures of risk-neutral moments from standard normals, enforces no-arbitrage, and extracts densities that price options more accurately and stably than three parametric plus nine stochastic baselines.