A new fluctuation test for constant variances with applications to finance

DOI10.1007/s00184-011-0371-7zbMath1254.62097OpenAlexW2061259821MaRDI QIDQ1928381

Nicolai Bissantz, Matthias Arnold, Daniel Ziggel, Dominik Wied

Publication date: 3 January 2013

Published in: Metrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00184-011-0371-7

zbMATH Keywords

portfolio optimizationstructural breakseconometric modeling

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Parametric hypothesis testing (62F03) Hypothesis testing in multivariate analysis (62H15) Non-Markovian processes: hypothesis testing (62M07) Portfolio theory (91G10)

Related Items (12)

Quantifying the data-dredging bias in structural break tests ⋮ Fourier methods for analyzing piecewise constant volatilities ⋮ On- and offline detection of structural breaks in thermal spraying processes ⋮ A residual-based multivariate constant correlation test ⋮ On the application of new tests for structural changes on global minimum-variance portfolios ⋮ Jump robust daily covariance estimation by disentangling variance and correlation components ⋮ Testing for a change in covariance operator ⋮ Estimating stochastic volatility models using realized measures ⋮ Testing constant cross-sectional dependence with time-varying marginal distributions in parametric models ⋮ Tests for Scale Changes Based on Pairwise Differences ⋮ An asymptotic test for constancy of the variance under short-range dependence ⋮ Inference for the Lagged Cross‐Covariance Operator Between Functional Time Series

Cites Work

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• Monitoring disruptions in financial markets
• Break detection in the covariance structure of multivariate time series models
• A new test for structural stability in the linear regression model
• GARCH (1,1) processes are near epoch dependent
• Change-point estimation in ARCH models
• Range vs. maximum in the OLS-based version of the CUSUM test
• Covariance changes detection in multivariate time series
• TESTING FOR DISTRIBUTIONAL CHANGE IN TIME SERIES
• TESTING FOR A CHANGE IN CORRELATION AT AN UNKNOWN POINT IN TIME USING AN EXTENDED FUNCTIONAL DELTA METHOD
• The Cusum Test with Ols Residuals
• A Conditional Kolmogorov Test
• Testing and Locating Variance Changepoints with Application to Stock Prices
• Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality
• METHODS OF STATISTICAL ANALYSIS APPROPRIATE FOR k SAMPLES OF TWO VARIABLES
• MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS
• Detecting parameter shift in garch models
• Monitoring Structural Change
• An Asymptotic | chi 2 Test for the Equality of Two Correlation Matrices

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