Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
High-Dimensional Copula-Based Distributions with Mixed Frequency Data
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.
Cite this item
Dong Hwan Oh & Andrew J. Patton, High-Dimensional Copula-Based Distributions with Mixed Frequency Data, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2015-50, 19 May 2015.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
Keywords: Composite likelihood; forecasting; high frequency data; nonlinear dependence
This item with handle RePEc:fip:fedgfe:2015-50
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