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Subject
News
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Модельный риск и базовые подходы к его численному измерению на примере моделей оценки рыночного риска

Финансовый журнал. 2022. Т. 14. № 2. С. 91–112.
Нэвэла А. Ю., Lapshin V. A.

Model risk is currently a topic of great interest both to the academic community and to the financial industry; however, there is not yet any generally accepted approach to measuring it as of now. We give a review of basic model risk definitions and different indicators and approaches to model risk estimation and calculation within a common setting. The subject of this work is model risk itself that arises while using models to estimate financial risk and its quantitative indicators. The study is of particular relevance to financial institutions which for a long time have been actively applying different risk models. Since there is no generally accepted way to do so, choosing the exact approach becomes an important step in modelling. The aim of the paper is to demonstrate different approaches to model risk estimation on the same example of estimating the model risk of Value-at-Risk models and compare them within one setting. Methods used: analysis of time series, theoretical distribution parameters estimation. The result of the work is a list of methods and approaches for estimating and calculating model risk with the examples of applying these methods to a real risk-management task with appropriate interpretation. This work can be useful for researchers and risk managers of financial institutions.

Research target: Economics and Management
Language: Russian
Full text
DOI
Keywords: рыночный рискстоимость под риском (Value-at-Risk)точностьконсервативностьмодельный рискметоды оценки модельного рискаэффективность оценок
Publication based on the results of:
Model Risks in Financial Risk Estimation Problems (2021)
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