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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. doi:10.1016/j.jeconom.2008.12.015 ISSN 0304-4076.

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Official URL: http://dx.doi.org/10.1016/j.jeconom.2008.12.015

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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
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:
DateEvent
June 2009Published
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

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