A0744
Title: Large volatility matrix prediction using tensor factor structure
Authors: Sung Hoon Choi - University of Connecticut (United States) [presenting]
Donggyu Kim - UC Riverside (United States)
Abstract: Several approaches for predicting large volatility matrices have been developed based on high-dimensional factor-based It processes. These methods often impose restrictions to reduce the model complexity, such as constant eigenvectors or factor loadings over time. However, several studies indicate that eigenvector processes are also time-varying. To address this feature, the factor structure is generalized by representing the integrated volatility matrix process as a cubic (order-3 tensor) form, which is decomposed into low-rank tensor and idiosyncratic tensor components. To predict conditional expected large volatility matrices, the projected tensor principal orthogonal component thresholding (PT-POET) procedure is proposed, and its asymptotic properties are established. The advantages of PT-POET are validated through a simulation study and demonstrated in an application to minimum variance portfolio allocation using high-frequency trading data.
