A k- factor GIGARCH process : estimation and application to electricity market spot prices
Some crucial time series of market data, such as electricity spot prices, exhibit long memory, in the sense of slowly-decaying correlations combined with heteroscedasticity. To e able to model such a behaviour, we consider the k-factor GIGARCH process and we propose two methods to address the related parameter estimation problem. For each method, we develop the asymptotic theory for this estimation.
|Date of creation:||Jul 2004|
|Publication status:||Published in Probabilistic methods applied to power systems, Jul 2004, United States. pp.1 - 7, 2004|
|Note:||View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00188533|
|Contact details of provider:|| Web page: https://hal.archives-ouvertes.fr/|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Cheung, Yin-Wong & Diebold, Francis X., 1994.
"On maximum likelihood estimation of the differencing parameter of fractionally-integrated noise with unknown mean,"
Journal of Econometrics,
Elsevier, vol. 62(2), pages 301-316, June.
- Yin-Wong Cheung & Francis X. Diebold, 1993. "On maximum-likelihood estimation of the differencing parameter of fractionally integrated noise with unknown mean," Working Papers 93-5, Federal Reserve Bank of Philadelphia.
- Yin-Wong Cheung & Francis X. Diebold, 1990. "On maximum-likelihood estimation of the differencing parameter of fractionally integrated noise with unknown mean," Discussion Paper / Institute for Empirical Macroeconomics 34, Federal Reserve Bank of Minneapolis.
When requesting a correction, please mention this item's handle: RePEc:hal:journl:halshs-00188533. 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: (CCSD)
If references are entirely missing, you can add them using this form.