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Activity Number: 326
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #317444
Title: Network Modeling of High-Dimensional Time Series in the Presence of Factors
Author(s): Sumanta Basu* and George Michailidis
Companies: UC Berkeley/Lawrence Berkeley National Laboratory and University of Florida
Keywords: high-dimensional statistics ; time serie ; factor models ; graphical models ; lasso
Abstract:

Estimating network connectivity among multiple time series is an important problem in many macroeconomic and financial applications. Examples include portfolio optimization in asset pricing and contagion analysis in finance. Sparsity-inducing regularizers commonly used in high-dimensional statistics do not account for the presence of pervasive factors influencing the underlying dynamics. In this work, we address the problem of estimating the network of contemporaneous connectivity among multiple time series in the presence of market-driven factor processes. For models with observable factor processes, we propose a regularized maximum likelihood procedure to simultaneously estimate the factor loadings and the conditional independence structure among the idiosyncratic components. We investigate the theoretical properties of the proposed method under high-dimensional scaling, assess its performance via extensive numerical studies and demonstrate its advantage on a motivating example from financial econometrics. In the presence of latent factors, we propose a low-rank and sparse modeling strategy to estimate the network after accounting for the underlying common factors.


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