Predictive Inference for Integrated Volatility

Norman Swanson (), Valentina Corradi () and Walter Distaso ()
Additional contact information
Valentina Corradi: University of Warwick
Walter Distaso: Queen Mary

Departmental Working Papers from Rutgers University, Department of Economics

Abstract: In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive den- sities and con¯dence intervals for integrated volatility. In this paper, 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 ¯nite 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.

Keywords: Diffusions; realized volatility measures; kernels; microstructure noise; jumps (search for similar items in EconPapers)
JEL-codes: C14 C22 C53 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2011-05-15
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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http://www.sas.rutgers.edu/virtual/snde/wp/2011-09.pdf (application/pdf)

Related works:
Working Paper: Predictive Inference for Integrated Volatility (2011)
Working Paper: Predictive Inference for Integrated Volatility (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:201109

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