July 2015

High-Dimensional Copula-Based Distributions with Mixed Frequency Data

Dong Hwan Oh and Andrew J. Patton


This paper proposes a new model for high-dimensional distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, enabling the use of high frequency data to accurately forecast linear dependence, and a new class of copulas designed to capture nonlinear dependence among the resulting uncorrelated, low frequency, residuals. Estimation of the new class of copulas is conducted using composite likelihood, facilitating applications involving hundreds of variables. In- and out-of-sample tests confirm the superiority of the proposed models applied to daily returns on constituents of the S&P 100 index.

Accessible materials (.zip)

Keywords: Composite likelihood, forecasting, high frequency data, nonlinear dependence

DOI: http://dx.doi.org/10.17016/FEDS.2015.050

PDF: Full Paper

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Last Update: June 19, 2020