Volatility Processes and Volatility Forecast with Long Memory

26 Pages Posted: 15 Apr 2002

See all articles by Gilles O. Zumbach

Gilles O. Zumbach

Edgelab; Consulting in Financial Engineering

Date Written: February 16, 2002


We introduce a new family of processes that include the long memory (power law) in the volatility correlation. This is achieved by measuring the historical volatility on a set of increasing time horizons and by computing the resulting effective volatility by a sum with power law weights. In the limit where only one component is included, the models are equivalent to GARCH(1,1) and I-GARCH(1). The models have 2 parameters (integrated processes) or 4 parameters (mean reverting processes). Volatility forecast in the context of quadratic processes is discussed, in particular as a mean to estimate process parameters. Using hourly data, the empirical properties of the new models are compared to existing models (GARCH, FIGARCH, ...), in particular log-likelihood estimates and volatility forecast errors. These studies show the advantage of the long memory processes as they give a good description of the empirical data from 1 hour to 1 month, with the same parameters.

Keywords: long memory volatility processes, ARCH model, volatility forecast

JEL Classification: C22

Suggested Citation

Zumbach, Gilles, Volatility Processes and Volatility Forecast with Long Memory (February 16, 2002). Available at SSRN: https://ssrn.com/abstract=306000 or http://dx.doi.org/10.2139/ssrn.306000

Gilles Zumbach (Contact Author)

Edgelab ( email )

Avenue de la Rasude 5
Lausanne, 1006
+41764351267 (Phone)

Consulting in Financial Engineering ( email )

Ch. Charles Baudouin 8
Saconnex d'Arve, 1228
+41 76 4351267 (Phone)