[en] In this article, two popular tests for structural breaks are considered for return volatilities: the ICSS algorithm employing the AIT test, and the least-squares (LS) estimator. We show that the AIT test is sensitive to many features of the time series, and the use of asymptotic critical values is not always justified. The LS method was found to detect breaks more accurately, especially if there are many, in comparative simulations. Real data analysis revealed that LS estimation yields results in better accordance with general economic intuition, although its results are somewhat sensitive to the sample length. In general, we recommend the LS estimator for practical purposes.
Disciplines :
Méthodes quantitatives en économie & gestion
Auteur, co-auteur :
KOSTYRKA, Andreï ; University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Department of Economics and Management (DEM)
Malakhov, Dmitry; National research university ‘Higher school of Economics’
Co-auteurs externes :
yes
Langue du document :
Russe
Titre :
A byl li sdvig: empiricheskij analiz testov na strukturnye sdvigi v volatil’nosti dohodnostej
Titre traduit :
[en] Was there ever a shift: Empirical analysis of structural-shift tests for return volatility
Date de publication/diffusion :
2021
Titre du périodique :
Applied Econometrics
Maison d'édition :
Russian Presidential Academy of National Economy and Public Administration (RANEPA), Russie
Skrobotov A. A. (2020). Survey on structural breaks and unit root tests. Applied Econometrics, 58, 96–141 (in Russian).
Andreou E., Ghysels E. (2002). Detecting multiple breaks in financial market volatility dynamics. Journal of Applied Econometrics, 17 (5), 579–600.
Andrews D. W. K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59 (3), 817–858.
Bai J., Perron P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18 (1), 1–22.
Bollerslev T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31 (3), 307–327.
Fernández C., Steel M. F. J. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93 (441), 359–371.
Galeano P., Tsay R. S. (2010). Shifts in individual parameters of a GARCH model. Journal of Financial Econometrics, 8 (1), 122–153.
Ghalanos A. (2020). Rugarch: Univariate GARCH models. [R-пакет версии 1.4-2]. https://cran.r-project.org/web/packages/rugarch/rugarch.pdf.
Glosten L. R., Jagannathan R., Runkle D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48 (5), 1779–1801.
Heberle J., Sattarhoff C. (2017). A fast algorithm for the computation of HAC covariance matrix esti- mators. Econometrics, 5 (1), 9.
Hillebrand E. (2005). Neglecting parameter changes in GARCH models. Journal of Econometrics, 129 (1–2), 121–138.
Inclán C., Tiao G. C. (1994). Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association, 89 (427), 913–923.
Kokoszka P., Leipus R. (1999). Testing for parameter changes in ARCH models. Lithuanian Mathematical Journal, 39 (2), 182–195.
Kwiatkowski D., Phillips P. C. B., Schmidt P., Shin Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54 (1–3), 159–178.
Lamoureux C. G., Lastrapes W. D. (1990). Heteroskedasticity in stock return data: Volume versus GARCH effects. Journal of Finance, 45 (1), 221–229.
Lavielle M., Moulines E. (2000). Least-squares estimation of an unknown number of shifts in a time se- ries. Journal of Time Series Analysis, 21 (1), 33–59.
Ling S., McAleer M. (2002). Stationarity and the existence of moments of a family of GARCH pro- cesses. Journal of Econometrics, 106 (1), 109–117.
Liu J., Wu S., Zidek J. V. (1997). On segmented multivariate regression. Statistica Sinica, 497–525.
Lumley T., Heagerty P. (1999). Weighted empirical adaptive variance estimators for correlated data re- gression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61 (2), 459–477.
McAleer M., Hafner C. M. (2014). A one-line derivation of EGARCH. Econometrics, 2 (2), 92–97.
Mikosch T., Stărică C. (2004). Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects. Review of Economics and Statistics, 86 (1), 378–390.
Nelson D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 347–370.
Newey W. K., West K. D. (1994). Automatic lag selection in covariance matrix estimation. The Review of Economic Studies, 61 (4), 631–653.
Perron P., Yamamoto Y., Zhou J. (2020). Testing jointly for structural changes in the error variance and coefficients of a linear regression model. Quantitative Economics, 11 (3), 1019–1057.
Rapach D. E., Strauss J. K., Wohar M. E. (2008). Chapter 10: Forecasting stock return volatility in the presence of structural breaks. In: Rapach D. E., Wohar M. E. (Eds.) Forecasting in the Presence of Structural Breaks and Model Uncertainty (Frontiers of Economics and Globalization, Vol. 3), 381–416.
Wu J., Xiao Z. (2018). Testing for changing volatility. The Econometrics Journal, 21 (2), 192–217.
Xu K. L. (2013a). Powerful tests for structural changes in volatility. Journal of Econometrics, 173 (1), 126–142.
Xu K. L. (2013b). Power monotonicity in detecting volatility levels change. Economics Letters, 121 (1), 64–69.
Yao Y.-C. (1988). Estimating the number of change-points via Schwarz’ criterion. Statistics and Probability Letters, 6 (3), 181–189.
Zambrano-Bigiarini M., Rojas R. (2013). A model-independent particle swarm optimization software for model calibration. Environmental Modelling and Software, 43, 5–25.
