4. Volatility Forecasting: An Empirical Example using Eviews 6

  1. Evdokia Xekalaki and
  2. Stavros Degiannakis

Published Online: 31 MAR 2010

DOI: 10.1002/9780470688014.ch4

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 Eviews 6, in ARCH Models for Financial Applications, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470688014.ch4

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

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Keywords:

  • ARCH models;
  • estimation parameters;
  • EViews;
  • one-step-ahead volatility forecasting;
  • ten-step-ahead volatility forecasting

Summary

This chapter obtains volatility forecasts in terms of four ARCH specifications on the basis of the data set on the S&P500 equity index for the period from 4 April 1988 to 5 April 2005. It looks into generating one-step-ahead and more-than-one-step-ahead volatility forecasts using EViews. The package provides the estimation of ARCH models with GARCH(p, q), IGARCH(p, q), EGARCH(p, q), APARCH(p, q), GRJ(p, q), CGARCH(1,1) and ACGARCH(1,1) volatility specifications. The chapter obtains more-than-one-step-ahead forecasts of volatility of the S&P500 index on the basis of a parameter vector estimated at a given point in time t. For the sake of illustration, it discusses the ten-day-ahead case. More-than-one-step-ahead forecasts can be computed by repeated substitution.

Controlled Vocabulary Terms

autoregressive conditional heteroskedasticity; estimation; forecast accuracy