Robust estimation of transition matrices in high dimensional heavy-tailed vector autoregressive processes

Huitong Qiu, Sheng Xu, Fang Han, Han Liu, Brian S Caffo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Gaussian vector autoregressive (VAR) processes have been extensively studied in the literature. However, Gaussian assumptions are stringent for heavy-tailed time series that frequently arises in finance and economics. In this paper, we develop a unified framework for modeling and estimating heavy-tailed VAR processes. In particular, we generalize the Gaussian VAR model by an elliptical VAR model that naturally accommodates heavy-tailed time series. Under this model, we develop a quantile-based robust estimator for the transition matrix of the VAR process. We show that the proposed estimator achieves parametric rates of convergence in high dimensions. This is the first work in analyzing heavy-tailed high dimensional VAR processes. As an application of the proposed framework, we investigate Granger causality in the elliptical VAR process, and show that the robust transition matrix estimator induces sign-consistent estimators of Granger causality. The empirical performance of the proposed methodology is demonstrated by both synthetic and real data. We show that the proposed estimator is robust to heavy tails, and exhibit superior performance in stock price prediction.

Original languageEnglish (US)
Title of host publication32nd International Conference on Machine Learning, ICML 2015
PublisherInternational Machine Learning Society (IMLS)
Pages1843-1851
Number of pages9
Volume3
ISBN (Print)9781510810587
StatePublished - 2015
Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
Duration: Jul 6 2015Jul 11 2015

Other

Other32nd International Conference on Machine Learning, ICML 2015
CountryFrance
CityLile
Period7/6/157/11/15

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ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Science Applications

Cite this

Qiu, H., Xu, S., Han, F., Liu, H., & Caffo, B. S. (2015). Robust estimation of transition matrices in high dimensional heavy-tailed vector autoregressive processes. In 32nd International Conference on Machine Learning, ICML 2015 (Vol. 3, pp. 1843-1851). International Machine Learning Society (IMLS).