Univariate GARCH Modeling

  • Eric Zivot
  • Jiahui Wang
Chapter

Abstract

Previous chapters have concentrated on modeling and predicting the conditional mean, or the first order moment, of a univariate time series, and are rarely concerned with the conditional variance, or the second order moment, of a time series. However, it is well known that in financial markets large changes tend to be followed by large changes, and small changes tend to be followed by small changes. In other words, the financial markets are sometimes more volatile, and sometimes less active.

Keywords

Conditional Variance Standardize Residual GARCH Model Financial Time Series Arch Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Eric Zivot
    • 1
  • Jiahui Wang
    • 2
  1. 1.Department of EconomicsUniversity of WashingtonSeattleUSA
  2. 2.Ronin Capital LLCChicagoUSA

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