An easy test for two stationary long processes being uncorrelated via AR approximations
This paper proposes an easy test for two stationary autoregressive fractionally integrated moving average (ARFIMA) processes being uncorrelated via AR approximations. We prove that an ARFIMA process can be approximated well by an autoregressive (AR) model and establish the theoretical foundation of Haugh's (1976) statistics to test two ARFIMA processes being uncorrelated. Using AIC or Mallow's Cp criterion as a guide, we demonstrate through Monte Carlo studies that a lower order AR(k) model is sufficient to prewhiten an ARFIMA process and the Haugh test statistics perform very well in finite sample. We illustrate the methodology by investigating the independence between the volatility of two daily nominal dollar exchange rates-Euro and Japanese Yen and find that there exists "strongly simultaneous correlation" between the volatilities of Euro and Yen within 25 days.
|Date of creation:||01 Aug 2008|
|Date of revision:|
|Contact details of provider:|| Postal: |
Fax: +32 10474304
Web page: http://www.uclouvain.be/core
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:cor:louvco:2008047. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alain GILLIS)
If references are entirely missing, you can add them using this form.