Regression modelEconometrics / time series

Fourier DCC-GARCH Model

The Fourier DCC-GARCH model extends Engle's Dynamic Conditional Correlation GARCH framework by embedding Fourier trigonometric terms in the conditional mean or variance equations. This allows the model to approximate smooth, gradual structural shifts in volatility dynamics and inter-asset correlations without requiring knowledge of the number or timing of break points.

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Sources

  1. Engle, R. (2002). Dynamic conditional correlations: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI: 10.1198/073500102288618487
  2. Nazlioglu, S., Gormus, N. A., & Soytas, U. (2016). Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis. Energy Economics, 60, 168-175. DOI: 10.1016/j.eneco.2016.09.009

Related methods

ScholarGateFourier DCC-GARCH (Fourier Dynamic Conditional Correlation GARCH Model). Retrieved 2026-06-04 from https://scholargate.app/tr/econometrics/fourier-dcc-garch