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Tests for Variance Shift at an Unknown Time Point

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  • D. A. Hsu

Abstract

Two tests for variance shift in a sequence of independent normal random variables, when the initial level of variance is unknown, are investigated in this article. The first is a locally most powerful test, and the second is a test based upon cusums of X2 values. Distribution functions of the two test statistics are approximated through the use of Edgeworth expansions and/or the beta distribution by matching the first few moments. Critical points of both test statistics are tabulated for various sample sizes. Powers of the two tests are compared using a Monte Carlo example. An illustration of the application of the tests to stock market price analysis is provided.

Suggested Citation

  • D. A. Hsu, 1977. "Tests for Variance Shift at an Unknown Time Point," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(3), pages 279-284, November.
    • Handle: RePEc:bla:jorssc:v:26:y:1977:i:3:p:279-284
    DOI: 10.2307/2346968
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    Cited by:

    1. Alessandro De Gregorio & Stefano M. Iacus, 2007. "Change point estimation for the telegraph process observed at discrete times," Papers 0705.0503, arXiv.org.
    2. Kucharczyk, Daniel & Wyłomańska, Agnieszka & Sikora, Grzegorz, 2018. "Variance change point detection for fractional Brownian motion based on the likelihood ratio test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 439-450.
    3. Cheng, Tsung-Lin, 2009. "An efficient algorithm for estimating a change-point," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 559-565, March.
    4. Michael Messer, 2022. "Bivariate change point detection: Joint detection of changes in expectation and variance," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 886-916, June.
    5. Max Wornowizki & Roland Fried & Simos G. Meintanis, 2017. "Fourier methods for analyzing piecewise constant volatilities," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(3), pages 289-308, July.
    6. Minya Xu & Ping-Shou Zhong & Wei Wang, 2016. "Detecting Variance Change-Points for Blocked Time Series and Dependent Panel Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 213-226, April.
    7. Bo Zhao & Joseph Glaz, 2016. "Scan Statistics for Detecting a Local Change in Variance for Normal Data with Known Variance," Methodology and Computing in Applied Probability, Springer, vol. 18(2), pages 563-573, June.
    8. Xuwen Zhu & Yana Melnykov, 2022. "On Finite Mixture Modeling of Change-point Processes," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 3-22, March.
    9. Shota Gugushvili & Frank van der Meulen & Moritz Schauer & Peter Spreij, 2018. "Nonparametric Bayesian volatility estimation," Papers 1801.09956, arXiv.org, revised Mar 2019.
    10. Kim, Tae-Hwan & Leybourne, Stephen & Newbold, Paul, 2002. "Unit root tests with a break in innovation variance," Journal of Econometrics, Elsevier, vol. 109(2), pages 365-387, August.
    11. Cook, Steven, 2006. "Testing for cointegration in the presence of mis-specified structural change," Statistics & Probability Letters, Elsevier, vol. 76(13), pages 1380-1384, July.
    12. Loredana Ureche-Rangau & Franck Speeg, 2011. "A simple method for variance shift detection at unknown time points," Economics Bulletin, AccessEcon, vol. 31(3), pages 2204-2218.
    13. Winston T. Lin, 1999. "Dynamic and Stochastic Instability and the Unbiased Forward Rate Hypothesis: A Variable Mean Response Approach," Multinational Finance Journal, Multinational Finance Journal, vol. 3(3), pages 173-221, September.
    14. Galeano, Pedro, 2004. "Variance changes detection in multivariate time series," DES - Working Papers. Statistics and Econometrics. WS ws041305, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Radu T. Pruna & Maria Polukarov & Nicholas R. Jennings, 2016. "A new structural stochastic volatility model of asset pricing and its stylized facts," Papers 1604.08824, arXiv.org.
    16. Stefan Albert & Michael Messer & Julia Schiemann & Jochen Roeper & Gaby Schneider, 2017. "Multi-Scale Detection of Variance Changes in Renewal Processes in the Presence of Rate Change Points," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 1028-1052, November.
    17. de Pooter, M.D. & van Dijk, D.J.C., 2004. "Testing for changes in volatility in heteroskedastic time series - a further examination," Econometric Institute Research Papers EI 2004-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Jandhyala, Venkata K. & Fotopoulos, Stergios B. & Hawkins, Douglas M., 2002. "Detection and estimation of abrupt changes in the variability of a process," Computational Statistics & Data Analysis, Elsevier, vol. 40(1), pages 1-19, July.

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