Do Long-Memory Models Have Long Memory?
AbstractThis paper examines the predictability memory of fractionally integrated ARMA processes. Very long memory is found for positively fractionally integrated processes with large positive AR parameters. However, negative AR parameters absorb, to a great extent, the memory generated by a positive fractional difference. An MA parameter may also reduce the predictability memory substantially, even if the parameter alone provides hardly any memory.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 227.
Length: 4 pages
Date of creation: 27 Feb 1998
Date of revision: 16 Mar 2000
Publication status: Published in International Journal of Forecasting, 2000, pages 121-124.
Contact details of provider:
Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden
Phone: +46-(0)8-736 90 00
Fax: +46-(0)8-31 01 57
Web page: http://www.hhs.se/
More information through EDIRC
ARMA; Fractional integration; Prediction horizon;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- GIOT, Pierre & LAURENT, Sébastien, .
"Modelling daily Value-at-Risk using realized volatility and ARCH type models,"
CORE Discussion Papers RP, UniversitÃ© catholique de Louvain, Center for Operations Research and Econometrics (CORE)
-1708, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, Elsevier, vol. 11(3), pages 379-398, June.
- Giot,Pierre & Laurent,Sebastien, 2001. "Modelling daily value-at-risk using realized volatility and arch type models," Research Memorandum, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR) 014, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Pierre Giot & Sébastien Laurent, 2002. "Modelling Daily Value-at-Risk Using Realized Volatility and ARCH Type Models," Computing in Economics and Finance 2002, Society for Computational Economics 52, Society for Computational Economics.
- Man, K. S., 2003. "Long memory time series and short term forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 19(3), pages 477-491.
- Andersson, Michael K. & Karlsson, Gustav & Svensson, Josef, 2007. "The Riksbank’s Forecasting Performance," Working Paper Series 218, Sveriges Riksbank (Central Bank of Sweden).
- repec:dgr:uvatin:2005068 is not listed on IDEAS
- Souza, Leonardo R. & Smith, Jeremy, 2002. "Bias in the memory parameter for different sampling rates," International Journal of Forecasting, Elsevier, Elsevier, vol. 18(2), pages 299-313.
- Leonardo Souza & Jeremy Smith & Reinaldo Souza, 2006.
"Convex combinations of long memory estimates from different sampling rates,"
Computational Statistics, Springer,
Springer, vol. 21(3), pages 399-413, December.
- Souza, Leonardo Rocha & Smith, Jeremy & Souza, Reinaldo Castro de, 2003. "Convex Combinations of Long Memory Estimates from Different Sampling Rates," Economics Working Papers (Ensaios Economicos da EPGE) 489, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, Elsevier, vol. 22(3), pages 443-473.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Helena Lundin).
If references are entirely missing, you can add them using this form.