Chapter 1. Classical Time Series Models and Financial Series

  1. Christian Francq1 and
  2. Jean-Michel Zakoïan1,2

Published Online: 14 JUL 2010

DOI: 10.1002/9780470670057.ch1

GARCH Models: Structure, Statistical Inference and Financial Applications

GARCH Models: Structure, Statistical Inference and Financial Applications

How to Cite

Francq, C. and Zakoïan, J.-M. (2010) Classical Time Series Models and Financial Series, in GARCH Models: Structure, Statistical Inference and Financial Applications, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470670057.ch1

Author Information

  1. 1

    University Lille 3, Lille, France

  2. 2

    CREST, Paris, France

Publication History

  1. Published Online: 14 JUL 2010
  2. Published Print: 23 JUL 2010

ISBN Information

Print ISBN: 9780470683910

Online ISBN: 9780470670057

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Keywords:

  • notation - 1 classical time series models and financial series;
  • standard time series and stationarity, autocorrelation, white noise, innovation and autoregressive moving average (ARMA) models;
  • stationarity, playing a central part in time series analysis;
  • simplest example of second-order stationary process and white noise;
  • classical time series analysis - centered on second-order structure of processes;
  • ARMA and ARIMA models - used for second-order stationary process prediction;
  • Box–Jenkins Methodology - finding the most appropriate ARIMA(p, d, q) model and using it for forecasting;
  • modeling financial time series - a complex problem;
  • random variance models;
  • conditional heteroscedasticity

Summary

This chapter contains sections titled:

  • Stationary Processes

  • ARMA and ARIMA Models

  • Financial Series

  • Random Variance Models

  • Bibliographical Notes

  • Exercises