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October 19, 2026
'Cultural Studies Seeks to Understand What Lies Beneath the Surface of Everyday Things'
Maxim Zhiganov has been exploring different aspects of sound for many years—first in relation to his native Perm and then on a broader, global scale. In this interview for the HSE Young Scientists project, he talks about sound maps, the special issue of the journal Logos, and why it is important to make the familiar less clear and obvious.
October 14, 2026
At HSE University–St Petersburg, Experts Discuss How AI Changes Tourist Routes and Cultural Heritage
Neural networks are no longer simple travel assistants; now they decide what to include on the route and what to skip. Artificial intelligence changes how people perceive a place and shapes the image of tourist centres. At the II International Scientific and Practical Conference ’Information Technologies in the Preservation and Promotion of Cultural Heritage,’ researchers discussed who controls algorithmic solutions and in what ways, and whether authentic traditions get lost in them.
October 12, 2026
Road Injuries Leading Cause of Death Among Men Aged 10 to 39
Worldwide, road injuries are the leading cause of death among males aged 10–39. An international team of researchers reached this conclusion as part of the Global Burden of Disease Study 2023, analysing data from 204 countries between 1990 and 2023. Their findings have been published in The Lancet Public Health, with HSE University Professor Vasily Vlassov listed among the collaborators.

 

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Dynamics in the Predictability of Credit Default Swap Spreads of EU Companies

Complexity. 2023. Vol. 2023. Article 7572061.
Kirill Romanyuk, Sarvar Anvarov, Mark Shumilov, Alecksey Zheleyko

The COVID-19 pandemic affected financial instruments and markets all around the world. Credit default swap contracts of EU companies were analysed in this paper. The data consist of daily credit default swap spreads and market capitalisations of EU companies, exchange rates, LIBOR rates, bond yields, and commodity futures prices from January 2010 to February 2022. The dynamics in the performance of forecasting models for credit default swap spreads before and after the declaration of the COVID-19 pandemic were measured by relative error metrics, i.e., relative root mean squared error, relative mean absolute error, and relative mean absolute percentage error. The results show a small drop in the performance right after the declaration of the COVID-19 pandemic that is mitigated by strong performance in the rest of the year, followed by a significant drop in the performance in the second year of the pandemic.

Research target: Economics and Management
Language: English
DOI
Text on another site
Keywords: Credit default swapsкредитные дефолтные свопыпредсказуемостьpredictabilityCOVID-19COVID-19
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