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  • Метод обнаружения структурного сдвига в модели авторегрессионной условной гетероскедастичности: случай распределения Стьюдента
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News
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.
July 24, 2026
‘I Like Self-Fulfilling Prophecies
Andrey Vorchik studies happiness, delivers popular science lectures, and believes that science should address social issues as well. In an interview for the Young Scientists of HSE University project, he spoke about how emotions influence decision-making, the Bermuda Triangle formed by the bathroom, refrigerator, and bed, and the ideal formula for education.

 

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Метод обнаружения структурного сдвига в модели авторегрессионной условной гетероскедастичности: случай распределения Стьюдента

Математическое моделирование. 2023. Т. 35. № 1. С. 51–58.
Borzykh D., Yazykov A.

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.

Research target: Mathematics Computer Science Economics and Management
Language: Russian
Full text
DOI
Keywords: GARCH-моделиструктурные сдвигиstructural breakCUSUMCUSUMStudent's t-distributionGARCH modelGARCH-tGARCH-tt-распределение Стьюдента
Publication based on the results of:
Nonstationary random environments and their applications (2023)
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