Russian Journal of Money and Finance
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This study investigates housing affordability in Russia: factors of affordability, quantitative indicators, and government support measures. We are especially interested in the mortgage rate subsidy programmes that were implemented in 2015–2016 and 2020–2021 and their impact on housing affordability indicators. In order to evaluate impact of the first programme, we use a model of the real estate market and we decompose the index of housing affordability into different factors. As a result of our econometric analysis, we conclude that in general the programme was successful. Data about the second programme are not yet sufficient, so we evaluate its impact by a statistical analysis of the dynamics of the main indicators. We conclude that the impact is ambiguous: up until a particular moment (different in different regions), borrowers could benefit from the programme, but after that moment the increase in housing prices caused by the programme itself were exceeding the benefits from the subsidised rates. In conclusion, we provide some methods to improve the effectiveness of government measures to support housing affordability, which could be useful in the development of new programmes.

In October 2021, the Bank of Russia and the New Economic School (NES) hosted a joint international online workshop titled ‘Main Challenges in Banking: Risks, Liquidity, Pricing, and Digital Currencies’. Five papers were presented. They addressed various issues in banking which are currently of paramount importance to central bankers, market participants, and academics: the connections between systemic risk and the real economy, the digitalisation of finance and information asymmetries, credit spreads and monetary policy, the improvement of information flows and outcomes in credit markets, the introduction of central bank digital currencies, and bank intermediation.

This paper assesses the effect of monetary policy on investment in Russian regions. In the first stage of the research, we estimate the responses of regional investment to interbank market rate shocks using structural vector autoregressions. In the second stage, we estimate regression models using impulse responses as dependent variables and explanatory factors as independent variables. The regression calculations are performed using the Elastic Net regularisation technique. We find that regions with higher shares of manufacturing, agriculture and construction are more responsive to monetary policy shocks. In addition, we identified the high importance of these sectors in explaining the different effects of monetary policy on investment. The results also show that the larger is the share of the mining and quarrying sector in the gross regional product (GRP) and the greater the imports to GRP ratio, the smaller is the absolute change in investment from a monetary policy shock.

This paper presents an empirical analysis of the determinants of Russia’s sovereign risk. The spreads on sovereign Russian credit default swaps (CDS) were used as a measure of risk. Based on the accuracy of out-of-sample forecasts, the factors that influence Russian CDS were selected: the implied volatility of the rouble exchange rate, the size of foreign exchange reserves relative to GDP, and the average spread on other emerging market CDS as a proxy for global factors. In turn, the CDS of emerging market countries are determined by the volatility of their currencies, the slope of the US government bond curve, and also by the increments of the dollar index.

As the internet grows in popularity, many purchases are being made in online stores. Google Trends is an online tool that collects data on user queries and forms categories from them. We forecast the dynamics of both aggregate retail sales and individual categories of food and non-food products using macroeconomic variables and Google Trends categories that correspond to various product groups. For each type of retail, we consider the best forecasting models from macroeconomic variables and try to improve them by adding trends. For these purposes, we use pseudo-out-of-sample nowcasting as well as recursive forecasting several months ahead. We conclude that forecasts for food and non-food products can improve significantly once trends are added to the models.

In this paper, I analyse the relationship between the credit spreads of Russian bond issuers and monetary policy shocks. According to the theory of demand-side financial imperfections, in the presence of financial frictions, the higher the net worth of a firm, the lower its external finance premium. The theory of the balance sheet channel of monetary policy suggests that monetary shocks may affect the net worth of a firm through debt outflows. Together, these ideas predict that the external finance premium of more indebted companies is more sensitive to monetary policy shocks. However, my empirical findings from the credit spreads of Russian companies do not support this theory.

A genuine measure of ex ante credit risk links borrower’s financial position with the odds of default. Comprehension of a borrower’s financial position is proxied by the derivatives of its filled financial statements, i.e., financial ratios. We identify statistically significant relationships between shortlisted financial ratios and subsequent default events and develop a probability of default (PD) model that assesses the likelihood of a borrower going into delinquency at a one-year horizon. We compare the PD model constructed against alternative measures of ex ante credit risk that are widely used in related literature on bank risk taking, i.e., credit quality groups (prudential reserve ratios) assigned to creditors by banks and the credit spreads in interest rates. We find that the PD model predicts default events more accurately at a horizon of one year compared to prudential reserve rates. We conclude that the measure of ex ante credit risk developed is feasible for estimating risk-taking behaviour by banks and analysing shifts in portfolio composition.

In the first week of June 2021, the Bank of Russia and the New Economic School hosted a joint international online workshop titled ‘Identification and Measurement of Macroprudential Policies Effects’. Participants’ presentations suggest that macroprudential policy measures against high-risk lending produce their intended effects, but also, as a rule, bring about side effects. These effects may include a reduction in low-risk loan disbursements, if such measures are disincentivising in nature (as in Russia), or, vice versa, significant growth in the portfolio of low-risk loans, if the macroprudential tools are of a restrictive nature (as in Switzerland and Ireland).

Inflation targeting requires clear and transparent central bank’s communication. Analysts and market participants understand it as a broad list of information disclosed by the central bank. The general public understands it rather as the ability of a central bank to speak and explain its decisions in a plain language. In recent decades, monetary authorities in many countries have made significant progress in this direction. However, there has been no research on the quality of communication for the Bank of Russia. This paper aims to create a tool for automated evaluation of the readability of the Bank of Russia’s monetary policy communication, taking into account the available experience of linguistic and textual analysis, including machine learning methods, as well as to provide recommendations for its improvement. This can contribute to improving the effectiveness of the Bank of Russia communication on monetary policy, which is vital for its credibility, anchoring inflation expectations, and predictability of the regulator’s decisions.

In this paper, the author examines the efficiency of risk weight add-ons introduced by the Bank of Russia depending on borrowers’ debt burden in terms of discouraging high-risk unsecured rouble consumer lending and the effect of these add-ons on banks’ capital adequacy. The analysis is based on open bank reporting data for the period from October 2019 through August 2020. We show that in this time frame, most banks increased their capital. At the same time, the results obtained do not enable us to confirm the hypothesis that this measure has a pronounced effect on the reduction of the risk profile of consumer loan portfolios. We demonstrate that one of the factors that influenced the efficiency of measures introduced by the regulator is the substantially higher profitability of retail lending as compared to corporate lending.