High-Dimensional Copula-Based Distributions with Mixed Frequency Data

FEDS Working Paper No. 2015-050

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

54 Pages Posted: 26 Jul 2015

See all articles by Dong Hwan Oh

Dong Hwan Oh

Board of Governors of the Federal Reserve System

Andrew J. Patton

Duke University - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: May 19, 2015

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

Suggested Citation

Oh, Dong Hwan and Patton, Andrew J., High-Dimensional Copula-Based Distributions with Mixed Frequency Data (May 19, 2015). FEDS Working Paper No. 2015-050, http://dx.doi.org/10.17016/FEDS.2015.050, Available at SSRN: https://ssrn.com/abstract=2631691 or http://dx.doi.org/10.2139/ssrn.2631691

Dong Hwan Oh (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Andrew J. Patton

Duke University - Department of Economics ( email )

213 Social Sciences Building
Box 90097
Durham, NC 27708-0204
United States

HOME PAGE: http://econ.duke.edu/~ap172/

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