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Matematicheskoe modelirovanie, 2023, Volume 35, Number 1, Pages 51–58
DOI: https://doi.org/10.20948/mm-2023-01-04 (Mi mm4433) 		 
Structural break detection in autoregressional conditional heteroskedasticity model: case of Student distribution

D. A. Borzykhab, A. A. Yazykovab

a Moscow Institute of Physics and Technology
b National Research University Higher School of Economics (NRU HSE)
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DOI: https://doi.org/10.20948/mm-2023-01-04
Abstract: 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, t-distribution, Student distribution, volatility, change points, structural breaks, structural shifts, ICSS, CUSUM.
Funding agency Grant number
Russian Foundation for Basic Research 19-31-90169
Received: 17.10.2022
Revised: 09.11.2022
Accepted: 14.11.2022
English version:
Mathematical Models and Computer Simulations, 2023, Volume 15, Issue 4, Pages 654–659
DOI: https://doi.org/10.1134/S2070048223040026
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: D. A. Borzykh, A. A. Yazykov, “Structural break detection in autoregressional conditional heteroskedasticity model: case of Student distribution”, Mat. Model., 35:1 (2023), 51–58; Math. Models Comput. Simul., 15:4 (2023), 654–659
Citation in format AMSBIB
\Bibitem{BorYaz23}
\by D.~A.~Borzykh, A.~A.~Yazykov
\paper Structural break detection in autoregressional conditional heteroskedasticity model: case of Student distribution
\jour Mat. Model.
\yr 2023
\vol 35
\issue 1
\pages 51--58
\mathnet{http://mi.mathnet.ru/mm4433}
\crossref{https://doi.org/10.20948/mm-2023-01-04}
\mathscinet{https://mathscinet.ams.org/mathscinet-getitem?mr=4566991}
\transl
\jour Math. Models Comput. Simul.
\yr 2023
\vol 15
\issue 4
\pages 654--659
\crossref{https://doi.org/10.1134/S2070048223040026}
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