Building the uncertainty indicator regarding adjustment of the Bank of Russia’s monetary policy relying on news sources

Elizaveta Golovanova () and Andrei Zubarev
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Elizaveta Golovanova: Russian Academy of National Economy and Public Administration under the President of the Russian Federation (RANEPA)
Andrei Zubarev: Russian Academy of National Economy and Public Administration under the President of the Russian Federation (RANEPA)

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Abstract: Text analysis with machine learning support can be implemented for studying experts' relations to the Bank of Russia. To reach macroeconomic goals, the communication policy of the bank must be predictable and trustworthy. Surveys addressing this theme are still insufficient compare to the theoretical studies on the subject of other bank tools. The goal of this research is to analyze the perception of uncertainty by economic agents. For that purpose, we built an uncertainty indicator based on news sources from the Internet and on textual analysis. The dynamics of the indicator reflect unexpected statements of the Bank of Russia and events affecting monetary policy. Financial theory links monetary policy and stock prices, so we used this fact to examine the impact of the uncertainty indicator on the MOEX and RTS indices. We tested the hypothesis that our indicator is significant in GARCH models for chosen financial series. We found out several specifications in which our indicator is significant. Among the specifications considered, the uncertainty indicator contributes the most to explaining variances of the RTS index. The obtained uncertainty indicator can be used for forecasting of different macroeconomic variables.

Date: 2020-12-31
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Published in Business Informatics - Data Mining and Business Intelligence, 2020, 14 (4), pp.62-75. ⟨10.17323/2587-814X.2020.4.62.75⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05250244

DOI: 10.17323/2587-814X.2020.4.62.75

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