Working Paper

High-Dimensional Copula-Based Distributions with Mixed Frequency Data


Abstract: 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.

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

JEL Classification: C32; C51; C58;

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File(s): File format is application/pdf http://www.federalreserve.gov/econresdata/feds/2015/files/2015050pap.pdf
Description: Full text

File(s): File format is application/pdf http://dx.doi.org/10.17016/FEDS.2015.050
Description: http://dx.doi.org/10.17016/FEDS.2015.050

Authors

Bibliographic Information

Provider: Board of Governors of the Federal Reserve System (U.S.)

Part of Series: Finance and Economics Discussion Series

Publication Date: 2015-05-19

Number: 2015-50