Predictive inference for integrated volatility

Valentina Corradi, Walter Distaso, Norman R. Swanson

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1496-1512
Number of pages17
JournalJournal of the American Statistical Association
Volume106
Issue number496
DOIs
StatePublished - Dec 2011

Keywords

  • Diffusion
  • Jump
  • Kernel
  • Microstructure noise
  • Prediction
  • Realized volatility measure

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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