HSE University-St Petersburg Master's Student Speaks at the Largest International Conference on Statistics
Yury Burchanov, student of the master's programme 'Finance', studies credit risk modeling in modern financial markets. He presented his research at the Royal Statistical Society International Conference.
The Royal Statistical Society International Conference is one of the main research forums in the sphere of data analysis. The event took place at the University of Edinburgh. The programme included presentations, themed sessions, and practical seminars. Among the participants were 700 statisticians and specialists from 30 countries, including Yury Burchanov, first-year student of the master's programme 'Finance'.
The young researcher took an interest in the sphere of finance and analysis of the securities market in the second year of the bachelor's programme 'Economics' at HSE University-St Petersburg. His thesis supervisor—Kirill Romanyuk,associate professor of the Department of Finance—praised the student's graduation thesis and offered him to take part in the contest for travel grants in spring 2025. It is the first time that Yury Burchanov has gone to a foreign university as a speaker.
'In the third year, I took part in the academic mobility programme and studied at the Catholic University of Sacred Heart in Milan. Perhaps, this experience played its part in the competitive selection', shared Yury Burchanov.
The master's student presented his paper based on his graduation thesis: forecasting a company's credit risk with respect to time-varying volatility. The main research goal is to understand what analysis tools work the best in stable periods, and which ones—in times of economic shocks. Yury analysed quote rates of credit default swaps (CDS) of European companies from 2020 to 2022, their market capitalisations, as well as currency rates, prices of minerals, interbank lending rates, and other factors.
Yury Burchanov, first-year student of the master's programme 'Finance'
As a methodology, I chose advanced GARCH models (tools of financial econometrics), which use previous sharp price movements to predict future volatility. It allows us to identify quickly the periods of calm and turbulence in the market, to be more specific, to forecast profitability, assess risks, and gain reliable statistical findings on financial data.
Yury Burchanov found out that in times of economic uncertainty, the best ones are special prediction methods. His approaches turned out to be more accurate than the best theoretical forecast by 4% per day. This finding will help banks and investors to assess better how risky it is to lend money to a certain company. 'From a practical standpoint, this research will be beneficial in the first place for financial institutions, corporate traders, managers, and operations analysts. The same goes for government agencies, which would be able to apply more effectively fiscal and monetary measures and forecast crises', added Yury Burchanov.
In the master's programme 'Finance', the student started working on comparing the current results of the analysis to the data of more complex and modern models. In the future, he plans to create a software code for seven basic algorithms for finding violent changes in the data dynamics (structural breaks) of financial time series for more productive aggregation of future analysis results.