Corradi, Valentina (2011) Predictive Inference for Integrated Volatility. Journal of the American Statistical Association, Vol.106 (No.496). pp. 1496-1512. doi:10.1198/jasa.2011.tm10012 ISSN 0162-1459.

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Abstract

Numerous volatility-based derivative products have been engineered in recent years. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this article we propose nonparametric estimators of the aforementioned quantities, based on model-free volatility estimators. We establish consistency and asymptotic normality for the feasible estimators and study their finite-sample properties through a Monte Carlo experiment. Finally, using data from the New York Stock Exchange, we provide an empirical application to volatility directional predictability.

Item Type:	Journal Article
Subjects:	H Social Sciences > HB Economic Theory
Divisions:	Faculty of Social Sciences > Economics
Journal or Publication Title:	Journal of the American Statistical Association
Publisher:	American Statistical Association
ISSN:	0162-1459
Official Date:	2011
Dates:	Date Event 2011 Published
Volume:	Vol.106
Number:	No.496
Number of Pages:	17
Page Range:	pp. 1496-1512
DOI:	10.1198/jasa.2011.tm10012
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	ESRC
Grant number:	RES-000-23-0006, RES-062-23-0311, RES-062-23-0790

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