Review of Quantitative Finance and Accounting

, Volume 19, Issue 2, pp 111–129 | Cite as

An Assessment of Empirical Model Performance When Financial Market Transactions are Observed at Different Data Frequencies: An Application to East Asian Exchange Rates

  • Kai-Li Wang
  • Chris Fawson
  • Christopher B. Barrett

Abstract

This paper compares the performance of alternative models of east Asian exchange rates at different data frequencies. Selected models employ different specifications of the conditional variance and the conditional error distribution. Conditional variance specifications include: homoscedasticity, GARCH, LGARCH, and EGARCH. Conditional error distribution specifications include normal and Student t. The best exchange rate model specification is clearly conditional on data frequency. Higher frequency (daily, weekly) data commonly exhibit characteristics that demand more sophisticated estimation methods than analysts commonly employ. These characteristics generally vanish at lower (monthly, quarterly) frequencies. Overall we find significant benefit from accommodating heteroscedasticity and leptokurtic properties of the conditional distribution as data frequency increases. Using a likelihood ratio test we compare the relative gain from addressing heteroscedasticity (through use of GARCH models) versus accommodation of leptokurtosis. This comparison suggests that the gains from correct specification of the conditional distribution dominate those obtained from addressing problems of heteroscedasticity.

exchange rates data frequency GARCH distributions leptokurtosis 

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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Kai-Li Wang
    • 1
  • Chris Fawson
    • 2
  • Christopher B. Barrett
    • 3
  1. 1.Tunghai UniversityTaiwan
  2. 2.Utah State UniversityUSA
  3. 3.Cornell UniversityUSA

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