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Activity Number: 508 - Forecasting and Modeling Financial Volatility
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #305150
Title: Inference for Volatility Functionals of Ito Semimartingales Observed with Noise
Author(s): Richard Chen*
Companies: University of Chicago
Keywords: high-frequency data; Ito semimartingale; volatility functionals; nonparametrics; central limit theorem; optimal rate
Abstract:

This paper presents nonparametric inference for nonlinear volatility functionals of general multivariate Ito semimartingales, in high-frequency and noisy setting. The estimator achieves the optimal convergence rate by local moving averages, jump truncation, spatial localization and high-order nonlinearity bias correction. A stable central limit theorem is attained with estimable asymptotic covariance matrix, and forms a basis for infill asymptotic results of, for example, the realized Laplace transform, the realized principal component analysis, the linear continuous-time regression, and the generalized method of integrated moments, hence helps to extend the application scopes to more frequently sampled noisy data.


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