Predictive Inference for Integrated Volatility

Valentina Corradi, Norman Swanson () and Walter Distaso ()
Additional contact information
Norman Swanson: Rutgers University
Walter Distaso: Imperial College

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 densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned quantities. The kernel functions used in our analysis are based on different realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions under which the estimators are asymptotically equivalent to their unfeasible counterparts, based on the unobservable volatility process, is provided. Asymptotic normality is also established. The efficacy of the estimators is examined via Monte Carlo experimentation, and an empirical illustration based upon data from the New York Stock Exchange is provided.

Keywords: conditional confidence intervals; Diffusions; integrated volatility; kernels; microstructure noise; realized volatility measures (search for similar items in EconPapers)
JEL-codes: C14 C22 C53 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2006-09-22
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Related works:
Working Paper: Predictive Inference for Integrated Volatility (2011)
Working Paper: Predictive Inference for Integrated Volatility (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:200616

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