1. What is an ARCH Process?

  1. Evdokia Xekalaki and
  2. Stavros Degiannakis

Published Online: 31 MAR 2010

DOI: 10.1002/9780470688014.ch1

ARCH Models for Financial Applications

ARCH Models for Financial Applications

How to Cite

Xekalaki, E. and Degiannakis, S. (2010) What is an ARCH Process?, in ARCH Models for Financial Applications, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470688014.ch1

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:

  • autoregressive conditional heteroscedasticity (ARCH) process;
  • conditional mean;
  • conditional variance;
  • estimation method;
  • non-synchronous trading effect;
  • non-trading period effect

Summary

Autoregressive conditional heteroscedasticity (ARCH) models have been widely used in financial time series analysis and particularly in analysing the risk of holding an asset, evaluating the price of an option, forecasting time-varying confidence intervals and obtaining more efficient estimators under the existence of heteroscedasticity. Financial markets appear to be affected by the accumulation of information during non-trading periods, as reflected in the prices when the markets reopen following a close. As a result, the variance of returns displays a tendency to increase. Non-synchronous trading in the stocks making up an index induces autocorrelation in the return series, primarily when high-frequency data are used. This chapter also discusses the relationship between conditional variance and conditional mean.

Controlled Vocabulary Terms

autoregressive conditional heteroskedasticity; conditional mean; conditional variance; estimation