Using high frequency stock market index data to calculate, model and forecast realized return variance
Download full text from publisher
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Giot, Pierre & Laurent, Sebastien, 2004.
"Modelling daily Value-at-Risk using realized volatility and ARCH type models,"
Journal of Empirical Finance,
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 014, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- GIOT, Pierre & LAURENT, Sébastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," CORE Discussion Papers RP 1708, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Pierre Giot & Sébastien Laurent, 2002. "Modelling Daily Value-at-Risk Using Realized Volatility and ARCH Type Models," Computing in Economics and Finance 2002 52, Society for Computational Economics.
- Degiannakis, Stavros & Filis, George, 2016. "Forecasting oil price realized volatility: A new approach," MPRA Paper 69105, University Library of Munich, Germany.
- Adam Clements & Yin Liao, 2014. "The role in index jumps and cojumps in forecasting stock index volatility: Evidence from the Dow Jones index," NCER Working Paper Series 101, National Centre for Econometric Research.
- Halbleib Roxana & Voev Valeri, 2011.
"Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors,"
Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik),
De Gruyter, vol. 231(1), pages 134-152, February.
- Roxana Halbleib & Valerie Voev, 2010. "Forecasting Multivariate Volatility Using the VARFIMA Model on Realized Covariance Cholesky Factors," Working Papers ECARES ECARES 2010-041, ULB -- Universite Libre de Bruxelles.
- Roxana Halbleib & Valeri V. Voev, 2011. "Forecasting multivariate volatility using the VARFIMA model on realized covariance cholesky factors," ULB Institutional Repository 2013/195065, ULB -- Universite Libre de Bruxelles.
- Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
- Degiannakis, Stavros & Livada, Alexandra, 2013.
"Realized volatility or price range: Evidence from a discrete simulation of the continuous time diffusion process,"
Elsevier, vol. 30(C), pages 212-216.
- Degiannakis, Stavros & Livada, Alexandra, 2013. "Realized Volatility or Price Range: Evidence from a discrete simulation of the continuous time diffusion process," MPRA Paper 80489, University Library of Munich, Germany.
- Degiannakis, Stavros & Livada, Alexandra, 2013. "Realized Volatility or Price Range: Evidence from a discrete simulation of the continuous time diffusion process," MPRA Paper 80449, University Library of Munich, Germany.
- Roxana Chiriac & Valeri Voev, 2011.
"Modelling and forecasting multivariate realized volatility,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 26(6), pages 922-947, September.
- Roxana Chiriac & Valeri Voev, 2008. "Modelling and Forecasting Multivariate Realized Volatility," CREATES Research Papers 2008-39, Department of Economics and Business Economics, Aarhus University.
- Eugenie Hol & Siem Jan Koopman, 2002. "Stock Index Volatility Forecasting with High Frequency Data," Tinbergen Institute Discussion Papers 02-068/4, Tinbergen Institute.
- Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
- Bucci, Andrea, 2017. "Forecasting realized volatility: a review," MPRA Paper 83232, University Library of Munich, Germany.
- Adam E Clements & Yin Liao, 2013. "Modeling and forecasting realized volatility: getting the most out of the jump component," NCER Working Paper Series 93, National Centre for Econometric Research.
More about this item
StatisticsAccess and download statistics
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eui:euiwps:eco2001/06. 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: (Julia Valerio). General contact details of provider: http://edirc.repec.org/data/deiueit.html .
We have no references for this item. You can help adding them by using this form .