Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.
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The Statistical Cost of Adaptation in Multi-Source Transfer Learning
Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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Factor multivariate stochastic volatility models of high dimension
Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.