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Predictive density estimators for daily volatility based on the use of realized measures
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Corradi, Valentina, Distaso, Walter and Swanson, Norman R. (2009) Predictive density estimators for daily volatility based on the use of realized measures. In: 1st Symposium on Econometric Theory and Applications (SETA), Acad Sinica, Taipei, Taiwan, May 18-20, 2005. Published in: Journal of Econometrics, Vol.150 (No.2). pp. 119-138. ISSN 0304-4076. doi:10.1016/j.jeconom.2008.12.015
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Official URL: http://dx.doi.org/10.1016/j.jeconom.2008.12.015
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 a]. [Andersen, T.G., Bollerslev,T., Diebold, FX, 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. (c) 2009 Elsevier B.V. All rights reserved.
| Item Type: | Conference Item (Paper) | ||||
|---|---|---|---|---|---|
| Subjects: | H Social Sciences > HC Economic History and Conditions Q Science > QA Mathematics H Social Sciences |
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| Divisions: | Faculty of Social Sciences > Economics | ||||
| Journal or Publication Title: | Journal of Econometrics | ||||
| Publisher: | Elsevier BV * North Holland | ||||
| ISSN: | 0304-4076 | ||||
| Official Date: | June 2009 | ||||
| Dates: |
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| Volume: | Vol.150 | ||||
| Number: | No.2 | ||||
| Number of Pages: | 20 | ||||
| Page Range: | pp. 119-138 | ||||
| DOI: | 10.1016/j.jeconom.2008.12.015 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Conference Paper Type: | Paper | ||||
| Title of Event: | 1st Symposium on Econometric Theory and Applications (SETA) | ||||
| Type of Event: | Conference | ||||
| Location of Event: | Acad Sinica, Taipei, Taiwan | ||||
| Date(s) of Event: | May 18-20, 2005 |
Data sourced from Thomson Reuters' Web of Knowledge
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