Testing Linearity in an AR Errors-in-variables Model with Application to Stochastic Volatility

D. Feldmann, et al. "Testing Linearity in an AR Errors-in-variables Model with Application to Stochastic Volatility." Applicationes Mathematicae 30.4 (2003): 389-412. <http://eudml.org/doc/279004>.

@article{D2003,
abstract = {Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given data set, a statistical test is required. In this paper, we develop such a test of a linear hypothesis versus a general composite nonparametric alternative using the state space representation of the SV model as an errors-in-variables AR(1) model. The power of the test is analyzed. We provide a simulation study and apply the test to the HFDF96 data set. Our results confirm a linear AR(1) structure in log-volatility for the analyzed stock indices S&P500, Dow Jones Industrial Average and for the exchange rate DEM/USD.},
author = {D. Feldmann, W. Härdle, C. Hafner, M. Hoffmann, O. Lepski, A. Tsybakov},
journal = {Applicationes Mathematicae},
keywords = {autoregression with errors in variables; stochastic volatility; testing parametric versus nonparametric fit; minimax tests},
language = {eng},
number = {4},
pages = {389-412},
title = {Testing Linearity in an AR Errors-in-variables Model with Application to Stochastic Volatility},
url = {http://eudml.org/doc/279004},
volume = {30},
year = {2003},
}

TY - JOUR
AU - D. Feldmann
AU - W. Härdle
AU - C. Hafner
AU - M. Hoffmann
AU - O. Lepski
AU - A. Tsybakov
TI - Testing Linearity in an AR Errors-in-variables Model with Application to Stochastic Volatility
JO - Applicationes Mathematicae
PY - 2003
VL - 30
IS - 4
SP - 389
EP - 412
AB - Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given data set, a statistical test is required. In this paper, we develop such a test of a linear hypothesis versus a general composite nonparametric alternative using the state space representation of the SV model as an errors-in-variables AR(1) model. The power of the test is analyzed. We provide a simulation study and apply the test to the HFDF96 data set. Our results confirm a linear AR(1) structure in log-volatility for the analyzed stock indices S&P500, Dow Jones Industrial Average and for the exchange rate DEM/USD.
LA - eng
KW - autoregression with errors in variables; stochastic volatility; testing parametric versus nonparametric fit; minimax tests
UR - http://eudml.org/doc/279004
ER -