The Impact of News Flows on Inflation Expectations in Russia
https://doi.org/10.21686/2413-2829-2026-3-18-33
Abstract
The article analyzes the impact of subject news flows on inflation expectations in Russia in respect of two separate groups of agents, i. e. the professional audience (finance market participants) and the population within two time periods – before 2022 and after the date. For the professional audience prescribed inflation (BEIR) estimated on the basis of profitability of federal loan bonds is used as the indicator of inflation expectations; for the population it is data of ‘inFOM’ surveys. As news data the research used the block of nearly 3M texts from the biggest Russian media. The subject content of news is identified by regular expressions and for each subject daily indicators are built. The research used Bayesian version of AR-X-GARCH-X model, which provides an opportunity to assess the impact of news both on expectation levels and their volatility. The findings show that before 2022 inflation news increased the level of inflation expectations of professional participants, while news on politics reduced their uncertainty. After 2022 serious drop in influence was caused by news about the Bank of Russia, which can testify to the rise in confidence to its communications policy. As for the population neither news indicator turned to be considerable, which can demonstrate adaptive nature of their expectations and agrees with previous investigations.
About the Authors
M. A. Ivanov
Lomonosov Moscow State University
Russian Federation
Russian Federation
Mikhail A. Ivanov, Assistant Professor of the Department for Mathematical Methods of Economic Analysis, Faculty of Economics
building 51, 1 Leninskie Gory, Moscow, 119991
O. A. Klachkova
Lomonosov Moscow State University
Russian Federation
Russian Federation
Olga A. Klachkova, PhD, Associate Professor of the Department for Mathematical Methods of Economic Analysis, Faculty of Economics
building 51, 1 Leninskie Gory, Moscow, 119991
References
1. Gavrilov V., Ivanov M. A., Klachkova O. A., Korolev V. Yu., Roshchina Ya. A. Vliyanie tematicheskikh novostnykh potokov na komponenty volatilnosti fondovogo rynka Rossii [The Impact of Subject News Flows on Volatility Components of Stock Market in Russia]. Vestnik Instituta ekonomiki Rossiyskoy akademii nauk [Bulletin of the Institute of Economics of the Russian Academy of Sciences], 2022, No. 2, pp. 93–111. (In Russ.).
2. Goloshchapova I. O. Razrabotka metodiki postroeniya vysokochastotnykh indikatorov ekonomicheskikh ozhidaniy naseleniya na osnove bolshikh dannykh (na primere inflyatsionnykh ozhidaniy). Diss. kand. ekon. nauk [Developing Methodology of Building High-Frequency Indicators of Economic Expectations of Population on the Basis of Big Data (illustrated by Inflation Expectations). PhD econ. sci. diss.]. Moscow, 2018. (In Russ.).
3. Goloshchapova I. O., Andreev M. L. Otsenka inflyatsionnykh ozhidaniy rossiyskogo naseleniya metodami mashinnogo obucheniya [Assessing Inflation Expectations of Russian Population by Methods of Machine Learning]. Voprosy ekonomiki [Issues of Economics], 2017, No. 6, pp. 71–93. (In Russ.).
4. Zhemkov M. I., Kuznetsova O. S. Izmerenie inflyatsionnykh ozhidaniy uchastnikov finansovogo rynka v Rossii [Assessing Inflation Expectations of Finance Market Participants in Russia]. Voprosy ekonomiki [Issues of Economics], 2017, No. 10, pp. 111–122. (In Russ.).
5. Inflyatsionnye ozhidaniya i potrebitelskie nastroeniya. Informatsionno-analiticheskiy kommentariy [Inflation Expectations and Customer Feelings. Information and Analytical Comments], 2020, № 4 (40). (In Russ.).
6. Matevosova A. M. Issledovanie inflyatsionnykh ozhidaniy rossiyskogo naseleniya v usloviyakh sanktsiy na osnove bolshikh dannykh [Studying Inflation Expectation of Russian Population in Conditions of Sanctions on the Basis of Big Data]. Vestnik Instituta ekonomiki Rossiyskoy akademii nauk [Bulletin of the Institute of Economics of the Russian Academy of Sciences], 2023, No. 5, pp. 181–200. (In Russ.).
7. Petrova D. A. Otsenka inflyatsionnykh ozhidaniy na osnove internet-dannykh [Assessing Inflation Expectations on the Basis of Internet-Data]. Prikladnaya ekonometrika [Applied Econometrics], 2022, No. 2 (66), pp. 25–38. (In Russ.).
8. Baker S. R., Bloom N., Davis S. J. Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics, 2016, Vol. 131, No. 4, pp. 1593–1636.
9. Bollerslev T. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 1986, Vol. 31, No. 3, pp. 307–327.
10. Chen M.-H., Shao Q.-M. Monte Carlo Estimation of Bayesian Credible and HPD Intervals. Journal of Computational and Graphical Statistics, 1999, Vol. 8, No. 1, pp. 69–92.
11. Coibion O., Gorodnichenko Y., Weber M. Monetary Policy Communications and Their Effects on Household Inflation Expectations. Journal of Political Economy, 2022, Vol. 130, No. 6, pp. 1537–1584.
12. Creal D., Koopman S. J., Lucas A. Generalized Autoregressive Score Models with Applications. Journal of Applied Econometrics, 2013, Vol. 28, No. 5, pp. 777–795.
13. Erokhin A., Klachkova O. Influence of Readability and Tone of Bank of Russia Text on Inflation Expectations. Russian Journal of Money and Finance, 2024, Vol. 83, No. 4, pp. 27–47.
14. Francq C., Sucarrat G. An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation. Journal of Financial Econometrics, 2018, Vol. 16, No. 1, pp. 129–154.
15. Francq C., Wintenberger O., Zakoïan J.-M. GARCH Models without Positivity Constraints: Exponential or log GARCH? Journal of Econometrics, 2013, Vol. 177, No. 1, pp. 34–46.
16. Harvey A., Chakravarty T. Beta-t-(E)GARCH. Cambridge, University of Cambridge, 2008.
17. Hoffman M. D., Gelman A. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 2014, Vol. 15, pp. 1593–1623.
18. Nelson D. B. Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 1991, Vol. 59, No. 2, p. 347.
Review
For citations:
Ivanov M.A., Klachkova O.A. The Impact of News Flows on Inflation Expectations in Russia. Vestnik of the Plekhanov Russian University of Economics. 2026;(3):18-33. (In Russ.) https://doi.org/10.21686/2413-2829-2026-3-18-33
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