High-dimensional copula-based distributions with mixed frequency data

Dong Hwan Oh and Andrew Patton

Journal of Econometrics, 2016, vol. 193, issue 2, 349-366

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: High frequency data; Forecasting; Composite likelihood; Nonlinear dependence (search for similar items in EconPapers)
JEL-codes: C32 C51 C58 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Working Paper: High-Dimensional Copula-Based Distributions with Mixed Frequency Data (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:193:y:2016:i:2:p:349-366

DOI: 10.1016/j.jeconom.2016.04.011

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