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News
July 24, 2026
'Physics Is What the World Is Literally Built On'
Physicist Nina Dzhanayeva, recipient of a Vladimir Potanin Foundation scholarship, focuses her research on nanophotonics. In this interview for the HSE Young Scientists project, she discusses nanowells, scientific intuition, and how physics can help in making frangipane cream puffs.
July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.

 

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Credit Risk Modeling Of Residential Mortgage Lending In Russia

NRU Higher School of Economics , 2014. No. WP BRP 30/FE/2014.
Agata Maximovna Poroshina
This paper analyzes the problems of credit risk modeling of residential mortgage lending in Russia. Using unique mortgage loan and macro data from a regional branch of the Agency of Home Mortgage Lending (2008-2012), we find that borrower and mortgage loan characteristics affect the loan performance and play an important role in predicting default as well as a macroeconomic situation. On the residential mortgage market, borrowers with undeclared income have the lowest probability of default, mainly explained by the difference in declared and real income. Obtained results are robust under parametric and semiparametric specifications with correction for selectivity bias.
Priority areas: economics mathematics
Language: English
Full text
Text on another site
Keywords: ипотечное кредитованиеРоссияRussiaкредитный рискдефолтcredit riskdefaultmortgage lendingsample selectionвыборочная селективность
Publication based on the results of:
Сравнительный анализ инструментов параметрического и непараметрического оценивания кредитного риска на рынке ипотечного кредитования (2014)
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