6. Volatility Forecasting: An Empirical Example using G@RCH Ox

  1. Evdokia Xekalaki and
  2. Stavros Degiannakis

Published Online: 31 MAR 2010

DOI: 10.1002/9780470688014.ch6

ARCH Models for Financial Applications

ARCH Models for Financial Applications

How to Cite

Xekalaki, E. and Degiannakis, S. (2010) Volatility Forecasting: An Empirical Example using G@RCH Ox, in ARCH Models for Financial Applications, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470688014.ch6

Author Information

  1. Department of Statistics Athens University of Economics and Business, Greece

Publication History

  1. Published Online: 31 MAR 2010
  2. Published Print: 16 APR 2010

ISBN Information

Print ISBN: 9780470066300

Online ISBN: 9780470688014



  • conditional mean;
  • conditional volatility frameworks;
  • conditional volatility specifications;
  • volatility forecasting


This chapter obtains volatility forecasts in an empirical framework using the G@RCH package. In particular, the 11 conditional volatility specifications with the four distributional assumptions that are provided for by the G@RCH package are estimated on the basis of an observed series of values of the FTSE100 index. The conditional mean is considered as a first-order autoregressive process and the general framework of the author's model specification is described. The next trading day's variance forecasts are estimated using the G@RCH package in the forms that include (i) conditional volatility specifications (GARCH(1,1), IGARCH(1,1), EGARCH(1,1), GJR(1,1) and APARCH(1,1)); and (ii) conditional volatility frameworks (FIGARCH(1,d,1), FIGARCHC(1,d,1), FIEGARCH(1,d,1), FIAPARCH(1,d,1), FIAPARCHC(1,d,1) and HYGARCH(1,d,1)).

Controlled Vocabulary Terms

conditional mean; Forecast accuracy