Export to your Reference Manger

Please Copy & Paste
Bibtex-Export
Endnote-Export

       

Predictive density estimators for daily volatility based on the use of realized measures

[journal article]

Corradi, Valentina; Distaso, Walter; Swanson, Norman R.

fulltextDownloadDownload full text

(1269 KByte)

Citation Suggestion

Please use the following Persistent Identifier (PID) to cite this document:http://nbn-resolving.de/urn:nbn:de:0168-ssoar-212358

Further Details
Abstract The main objective of this paper is to propose a feasible, model free estimator of the predictive density of integrated volatility. In this sense, we extend recent papers by Andersen et al. [Andersen, T.G., Bollerslev, T., Diebold, F.X., Labys, P., 2003. Modelling and forecasting realized volatility. Econometrica 71, 579–626], and by Andersen et al. [Andersen, T.G., Bollerslev, T., Meddahi, N., 2004. Analytic evaluation of volatility forecasts. International Economic Review 45, 1079–1110; Andersen, T.G., Bollerslev, T., Meddahi, N., 2005. Correcting the errors: Volatility forecast evaluation using high frequency data and realized volatilities. Econometrica 73, 279–296], who address the issue of pointwise prediction of volatility via ARMA models, based on the use of realized volatility. Our approach is to use a realized volatility measure to construct a non parametric (kernel) estimator of the predictive density of daily volatility. We show that, by choosing an appropriate realized measure, one can achieve consistent estimation, even in the presence of jumps and microstructure noise in prices. More precisely, we establish that four well known realized measures, i.e. realized volatility, bipower variation, and two measures robust to microstructure noise, satisfy the conditions required for the uniform consistency of our estimator. Furthermore, we outline an alternative simulation based approach to predictive density construction. Finally, we carry out a simulation experiment in order to assess the accuracy of our estimators, and provide an empirical illustration that underscores the importance of using microstructure robust measures when using high frequency data.
Classification Basic Research, General Concepts and History of Economics; Economic Statistics, Econometrics, Business Informatics
Free Keywords C22; C53; C14; Diffusions; Integrated volatility; Realized volatility measures; Kernels; microstructure noise
Document language English
Publication Year 2009
Page/Pages p. 119-138
Journal Journal of Econometrics, 150 (2009) 2
DOI http://dx.doi.org/10.1016/j.jeconom.2008.12.015
Status Postprint; peer reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)
top