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.
{"title":"Impact of Government Measures to Support Mortgage Lending on Housing Affordability in Russia: Regional Evidence","authors":"I. Roshchina, Natalia Ilyunkina","doi":"10.31477/rjmf.202104.98","DOIUrl":"https://doi.org/10.31477/rjmf.202104.98","url":null,"abstract":"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.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"30 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115065114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Probability of Default Model to Estimate Ex Ante Credit Risk","authors":"Anna Burova, H. Penikas, S. Popova","doi":"10.31477/rjmf.202103.49","DOIUrl":"https://doi.org/10.31477/rjmf.202103.49","url":null,"abstract":"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.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"731 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133817340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Assessment of the Clarity of Bank of Russia Monetary Policy Communication by Neural Network Approach","authors":"Alina Evstigneeva, Mark Sidorovskiy","doi":"10.31477/rjmf.202103.03","DOIUrl":"https://doi.org/10.31477/rjmf.202103.03","url":null,"abstract":"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.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133342297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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).
{"title":"Review of the Bank of Russia – NES workshop ‘Identification and measurement of macroprudential policies’ effects’","authors":"H. Penikas","doi":"10.31477/rjmf.202103.94","DOIUrl":"https://doi.org/10.31477/rjmf.202103.94","url":null,"abstract":"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).","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123744353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"The impact of the Bank of Russia’s macroprudential policy on the risk exposure of banks’ consumer loan portfolios","authors":"Dmitry Miroshnichenko","doi":"10.31477/rjmf.202103.73","DOIUrl":"https://doi.org/10.31477/rjmf.202103.73","url":null,"abstract":"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.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"453 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125786158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we apply a set of machine learning and econometrics models, namely: Elastic Net, Random Forest, XGBoost, and SSVS to nowcasting (estimate for the current period) the dollar volumes of Russian exports and imports by a commodity group. We use lags in the volumes of export and import commodity groups, and exchange prices for some goods and other variables, due to which the curse of dimensionality becomes quite acute. The models we use have proven themselves well in forecasting in the presence of the curse of dimensionality, when the number of model parameters exceeds the number of observations. The best-performing model appears to be the weighted machine learning model, which outperforms the ARIMA benchmark model in nowcasting the volume of both exports and imports. According to the Diebold– Mariano test, in the case of the largest commodity groups our model often manages to obtain significantly more accurate nowcasts relative to the ARIMA model. The resulting estimates turn out to be quite close to the Bank of Russia’s historical forecasts built under comparable conditions.
在本文中，我们应用了一组机器学习和计量经济学模型，即:Elastic Net, Random Forest, XGBoost和SSVS来临近预测(当前时期的估计)商品组的俄罗斯进出口美元量。我们在进出口商品组的数量中使用了滞后性，并对一些商品的价格和其他变量进行了交换，因此维度的诅咒变得相当严重。当模型参数的数量超过观测值的数量时，我们所使用的模型已经证明了自己在存在维度诅咒的情况下的预测能力。表现最好的模型似乎是加权机器学习模型，它在近距离预测出口量和进口量方面优于ARIMA基准模型。根据Diebold - Mariano测试，在最大商品组的情况下，我们的模型通常能够获得比ARIMA模型更准确的临近预报。由此得出的估计结果与俄罗斯央行在可比条件下做出的历史预测非常接近。
{"title":"Nowcasting Growth Rates of Russia’s Export and Import by Commodity Group","authors":"Ksenia Mayorova, Nikita Nikita","doi":"10.31477/rjmf.202103.34","DOIUrl":"https://doi.org/10.31477/rjmf.202103.34","url":null,"abstract":"In this paper, we apply a set of machine learning and econometrics models, namely: Elastic Net, Random Forest, XGBoost, and SSVS to nowcasting (estimate for the current period) the dollar volumes of Russian exports and imports by a commodity group. We use lags in the volumes of export and import commodity groups, and exchange prices for some goods and other variables, due to which the curse of dimensionality becomes quite acute. The models we use have proven themselves well in forecasting in the presence of the curse of dimensionality, when the number of model parameters exceeds the number of observations. The best-performing model appears to be the weighted machine learning model, which outperforms the ARIMA benchmark model in nowcasting the volume of both exports and imports. According to the Diebold– Mariano test, in the case of the largest commodity groups our model often manages to obtain significantly more accurate nowcasts relative to the ARIMA model. The resulting estimates turn out to be quite close to the Bank of Russia’s historical forecasts built under comparable conditions.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"372 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128899093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper presents a semi-structural model of a regional economy based on the standard version of the neo-Keynesian model in gaps. The main feature of this tool is its ability to predict regional indicators and model the regional heterogeneity of the national economy. In our model, Russia is divided into two macro-regions: the Central Federal District and the rest of Russia in aggregate. These regions are modelled separately but are interrelated. The benefit of this approach is that it allows us to analyse how shocks in one region are passed along to others, how the regions react to general shocks and what the appropriate monetary policy response should be. The model represents a simple and convenient tool for building macroeconomically consistent forecasts and generating recommendations in the area of monetary policy based on regional specifics.
{"title":"Forecasting Regional Indicators Based on the Quarterly Projection Model","authors":"Alyona Nelyubina","doi":"10.31477/RJMF.202102.50","DOIUrl":"https://doi.org/10.31477/RJMF.202102.50","url":null,"abstract":"The paper presents a semi-structural model of a regional economy based on the standard version of the neo-Keynesian model in gaps. The main feature of this tool is its ability to predict regional indicators and model the regional heterogeneity of the national economy. In our model, Russia is divided into two macro-regions: the Central Federal District and the rest of Russia in aggregate. These regions are modelled separately but are interrelated. The benefit of this approach is that it allows us to analyse how shocks in one region are passed along to others, how the regions react to general shocks and what the appropriate monetary policy response should be. The model represents a simple and convenient tool for building macroeconomically consistent forecasts and generating recommendations in the area of monetary policy based on regional specifics.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117089172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Turdyeva, A. Tsvetkova, L. Movsesyan, A. Alexey, Dmitriy Chernyadev
In times of crisis, events are moving fast and standard macroeconomic statistics published with a lag cannot quite keep pace with the changing situation. During such periods, there is an increasing need to use high-frequency indicators that allow virtually real-time monitoring of economic activity. In many countries, this is achieved by using financial transaction data. In this paper, we present a methodology for the current analysis of sectoral financial flows in the Russian economy based on data from the Bank of Russia payment system. We use the information on the dynamics of average daily payments for each class of OKVED 2 (the Russian National Classifier of Economic Activities) to develop high- frequency indicators of economic activity, which have been published on the Bank of Russia website since April 2020. We also tentatively discuss the potential of financial transaction data in terms of improving the tools for short-term forecasting of business activity dynamics and solutions to other research problems.
{"title":"Data of Sectoral Financial Flows as a High-Frequency Indicator of Economic Activity","authors":"N. Turdyeva, A. Tsvetkova, L. Movsesyan, A. Alexey, Dmitriy Chernyadev","doi":"10.31477/RJMF.202102.28","DOIUrl":"https://doi.org/10.31477/RJMF.202102.28","url":null,"abstract":"In times of crisis, events are moving fast and standard macroeconomic statistics published with a lag cannot quite keep pace with the changing situation. During such periods, there is an increasing need to use high-frequency indicators that allow virtually real-time monitoring of economic activity. In many countries, this is achieved by using financial transaction data. In this paper, we present a methodology for the current analysis of sectoral financial flows in the Russian economy based on data from the Bank of Russia payment system. We use the information on the dynamics of average daily payments for each class of OKVED 2 (the Russian National Classifier of Economic Activities) to develop high- frequency indicators of economic activity, which have been published on the Bank of Russia website since April 2020. We also tentatively discuss the potential of financial transaction data in terms of improving the tools for short-term forecasting of business activity dynamics and solutions to other research problems.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128192136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Model risk in credit scoring can be understood as the bank’s losses associated with a model quality deterioration. Deterioration in model quality entails an incorrect assessment of the creditworthiness of borrowers and leads to an increase in potentially defaulting applications in the loan portfolio, as the bank relies on the model performance when making lending decisions. The relationship between model quality and financial performance is embedded in the confusion matrix, where the value of a type I error indicates the bank’s lost profit, and the value of a type II error is equivalent to losses in the event of a default. We propose estimating model risk based on the scenario forecast of model quality or the ranking ability of the Gini model over a given time interval. The result of the analysis is the assessment of the bank’s net present value for the current and modified models, depending on the approval level. The proposed approach allows us to solve the problem of the optimal choice of a Gini model and answer the question of how model quality affects financial performance.
{"title":"The Relationship Between the Financial Performance of Banks and the Quality of Credit Scoring Models","authors":"R. Tikhonov, A. Masyutin, V. Vadim","doi":"10.31477/RJMF.202102.76","DOIUrl":"https://doi.org/10.31477/RJMF.202102.76","url":null,"abstract":"Model risk in credit scoring can be understood as the bank’s losses associated with a model quality deterioration. Deterioration in model quality entails an incorrect assessment of the creditworthiness of borrowers and leads to an increase in potentially defaulting applications in the loan portfolio, as the bank relies on the model performance when making lending decisions. The relationship between model quality and financial performance is embedded in the confusion matrix, where the value of a type I error indicates the bank’s lost profit, and the value of a type II error is equivalent to losses in the event of a default. We propose estimating model risk based on the scenario forecast of model quality or the ranking ability of the Gini model over a given time interval. The result of the analysis is the assessment of the bank’s net present value for the current and modified models, depending on the approval level. The proposed approach allows us to solve the problem of the optimal choice of a Gini model and answer the question of how model quality affects financial performance.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123101509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At the end of February 2021, the Bank of Russia and NES held an online international academic seminar ‘Financial Dollarisation: Causes and Consequences’. The seminar addressed a number of aspects of dollarisation, such as the non-linear nature of the relationship between the dynamics of the exchange rate and the demand for foreign currency assets, the existence of the hysteresis effect and efficient distribution of risks associated with the loan dollarisation. In this overview, we will provide a summary of the reports presented at the seminar.
{"title":"Review of the Bank of Russia and NES Seminar ‘Financial Dollarisation: Causes and Consequences’","authors":"Konstantin Egorov, A. Ponomarenko","doi":"10.31477/RJMF.202102.96","DOIUrl":"https://doi.org/10.31477/RJMF.202102.96","url":null,"abstract":"At the end of February 2021, the Bank of Russia and NES held an online international academic seminar ‘Financial Dollarisation: Causes and Consequences’. The seminar addressed a number of aspects of dollarisation, such as the non-linear nature of the relationship between the dynamics of the exchange rate and the demand for foreign currency assets, the existence of the hysteresis effect and efficient distribution of risks associated with the loan dollarisation. In this overview, we will provide a summary of the reports presented at the seminar.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114579246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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