2012/11/01 by Mathias Vetter, Holger Dette
Economics, Econometrics and Finance · Engineering · Mathematics · #Applied mathematics #Computer science #Econometrics #Engineering #Financial Risk and Volatility Modeling #Finite element method #Gaussian #Mathematical optimization #Mathematics #Parametric statistics #Realized variance #Statistical Methods and Inference #Statistics #Stochastic processes and financial applications #Stochastic volatility #Volatility (finance) #Weak convergence #math.ST #stat.TH #von Mises yield criterion
paper · pdf · doi:10.3150/11-bej384
published as Bernoulli 2012, Vol. 18, No. 4, 1421-1447 · Published in at http://dx.doi.org/10.3150/11-BEJ384 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
openalex publication_date 2012/11/01 · arxiv created 2012/11/23 · arxiv updated 2012/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We consider the problem of testing the parametric form of the volatility for high frequency data. It is demonstrated that in the presence of microstructure noise commonly used tests do not keep the preassigned level and are inconsistent. The concept of preaveraging is used to construct new tests, which do not suffer from these drawbacks. These tests are based on a Kolmogorov–Smirnov or Cramér–von-Mises functional of an integrated stochastic process, for which weak convergence to a (conditional) Gaussian process is established. The finite sample properties of a bootstrap version of the test are illustrated by means of a simulation study.