Predictive Inference for Integrated Volatility

• 成果类型: Article
• 署名作者: Corradi, Valentina; Distaso, Walter; Swanson, Norman R.
• 署名单位: University of Warwick; Imperial College London; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2011.tm10012
• 发表日期: 2011
• 页码: 1496-1512
• 关键词: Errors-in-variables; Nonparametric Regression; deconvolution; diffusion; CONVERGENCE; estimators; forecasts; sample; rates; jump

摘要

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.