Geodesic
Structural break detection in autoregressional conditional heteroskedasticity model: case of Student distribution
Matematičeskoe modelirovanie, Tome 35 (2023) no. 1, pp. 51-58

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We consider two methods of structural break detection in a piecewise generalized model of autoregressive conditional heteroscedasticity. The first method is based on Kolmogorov–Smirnov statistics and is called KS-method. The second one is based on the cumulative sums and is called KL-method. In this paper, we compare the KS- and KL-methods under the assumption of Student conditional distribution of random errors. The results of our Monte Carlo experiments were as follows: the KL-method lost to the KS-method both in terms of the average probability of first type error and in terms of the average power structural break detection.
Keywords: GARCH-t, Student distribution, volatility, change points, structural breaks, structural shifts, CUSUM.
Mots-clés : t-distribution, ICSS 
@article{MM_2023_35_1_a3,
     author = {D. A. Borzykh and A. A. Yazykov},
     title = {Structural break detection in autoregressional conditional heteroskedasticity model: case of {Student} distribution},
     journal = {Matemati\v{c}eskoe modelirovanie},
     pages = {51--58},
     publisher = {mathdoc},
     volume = {35},
     number = {1},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MM_2023_35_1_a3/}
}
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D. A. Borzykh; A. A. Yazykov. Structural break detection in autoregressional conditional heteroskedasticity model: case of Student distribution. Matematičeskoe modelirovanie, Tome 35 (2023) no. 1, pp. 51-58. http://geodesic.mathdoc.fr/item/MM_2023_35_1_a3/