Large VARX Model With Network Regularization

27 Pages Posted: 15 Feb 2018

See all articles by Weilong Guo

Weilong Guo

Cornell University

Andreea Minca

Cornell University

Date Written: June 15, 2017


The vector autoregression model (VAR) has long been used for portfolio analysis, with recent extensions that incorporate exogenous factors (VARX). Despite its increased forecasting precision, the applicability of the VARX model is deeply hampered in the absence of sparsity, as the amount of coefficients to be estimated grows quadratically with the number of series. We introduce a novel regularization method for VARX model in the context of portfolios, where weighted links between portfolios are used to construct a penalty function for the autoregressive parameter matrices. To test the prediction performance of the new method, we first cluster the time series into several groups using wavelet decomposition and hierarchical clustering, after which we construct datasets of different homogeneity. Our method is meritorious from two perspectives: the computation time for the model is significantly reduced and the forecasting precision of the model is enhanced by fifty percent compared to existing regularization methods.

Keywords: Varx Model, Networks, Group Lasso, High-Dimensional Time Series, Forecasting, Time Series Clustering

Suggested Citation

Guo, Weilong and Minca, Andreea, Large VARX Model With Network Regularization (June 15, 2017). Available at SSRN: or

Weilong Guo

Cornell University ( email )

Ithaca, NY 14853
United States

Andreea Minca (Contact Author)

Cornell University ( email )

222 Rhodes Hall
Ithaca, NY NY 14853
United States


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