The Factor-Spline-GARCH Model for High- and Low-Frequency Correlations

49 Pages Posted: 7 Feb 2011 Last revised: 15 Nov 2013

See all articles by Jose Gonzalo Rangel

Jose Gonzalo Rangel

Banorte Financial Group

Robert F. Engle

New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER); New York University (NYU) - Volatility and Risk Institute

Date Written: January 31, 2011

Abstract

We propose a new approach to model high- and low-frequency components of equity correlations. Our framework combines a factor asset pricing structure with other specifications capturing dynamic properties of volatilities and covariances between a single common factor and idiosyncratic returns. High-frequency correlations mean revert to slowly varying functions that characterize long-term correlation patterns. We associate such term behavior with low-frequency economic variables, including determinants of market and idiosyncratic volatilities. Flexibility in the time-varying level of mean reversion improves both the empirical fit of equity correlations in the U.S. and correlation forecasts at long horizons.

Keywords: Factor models, Dynamic correlation, Spline-GARCH, DCC, Idiosyncratic volatility, Time-varying betas, Long-term forecast evaluation.

JEL Classification: C32, C51, C53, G11, G12

Suggested Citation

Rangel, Jose Gonzalo and Engle, Robert F., The Factor-Spline-GARCH Model for High- and Low-Frequency Correlations (January 31, 2011). Journal of Business and Economic Statistics, Vol. 30, No. 1, pp. 109-124, 2012, Available at SSRN: https://ssrn.com/abstract=1754785 or http://dx.doi.org/10.2139/ssrn.1754785

Jose Gonzalo Rangel (Contact Author)

Banorte Financial Group

Prol. Reforma 1230
Mexico City, CDMX 05349
Mexico

Robert F. Engle

New York University (NYU) - Department of Finance

Stern School of Business
44 West 4th Street
New York, NY 10012-1126
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

New York University (NYU) - Volatility and Risk Institute ( email )

44 West 4th Street
New York, NY 10012
United States

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