During the past several decades, the importance of assessing the risk of GDP growth downturns has increased tremendously. The financial crisis of 2008–2009 and the global lockdown caused by the COVID-19 pandemic demonstrated the vulnerability of the modern economy. As a result, a new framework (Growth-at-Risk) has been developed which allows the estimation of the size of the potential downfall of future GDP growth. However, most of the research focuses on the performance of quantile regression. I apply different approaches to forecasting growth-at-risk, including quantile regression, quantile random forests, and generalised autoregressive conditional heteroscedastic (GARCH) models, using the US economy for the analysis. I find that GARCH-type models perform worse at 5% and 10% coverage levels, but that quantile random forests and quantile regressions seem to have equal predictive ability.
{"title":"Comparison of Models for Growth-at-Risk Forecasting","authors":"Aleksei Kipriyanov","doi":"10.31477/rjmf.202201.23","DOIUrl":"https://doi.org/10.31477/rjmf.202201.23","url":null,"abstract":"During the past several decades, the importance of assessing the risk of GDP growth downturns has increased tremendously. The financial crisis of 2008–2009 and the global lockdown caused by the COVID-19 pandemic demonstrated the vulnerability of the modern economy. As a result, a new framework (Growth-at-Risk) has been developed which allows the estimation of the size of the potential downfall of future GDP growth. However, most of the research focuses on the performance of quantile regression. I apply different approaches to forecasting growth-at-risk, including quantile regression, quantile random forests, and generalised autoregressive conditional heteroscedastic (GARCH) models, using the US economy for the analysis. I find that GARCH-type models perform worse at 5% and 10% coverage levels, but that quantile random forests and quantile regressions seem to have equal predictive ability.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129804475","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}
This paper constructs a dynamic stochastic general equilibrium model with various trends for each GDP by expenditure component and structural breaks. The model is estimated on the sample of 20 Russian time series from 2000Q1 to 2020Q4. It produces high-quality out-of-sample forecasts that outperform autoregressive models. Production efficiency shocks explain more than half of the variance of key variables (both conditional and unconditional). The version with structural breaks produces much better median-based forecasting measures and almost the same mean-based forecasting measures due to significant errors near structural breaks. Various inflation measures respond similarly to monetary policy shocks, but differently to other shocks.
{"title":"Dynamic Stochastic General Equilibrium Model with Multiple Trends and Structural Breaks","authors":"S. Ivashchenko","doi":"10.31477/rjmf.202201.46","DOIUrl":"https://doi.org/10.31477/rjmf.202201.46","url":null,"abstract":"This paper constructs a dynamic stochastic general equilibrium model with various trends for each GDP by expenditure component and structural breaks. The model is estimated on the sample of 20 Russian time series from 2000Q1 to 2020Q4. It produces high-quality out-of-sample forecasts that outperform autoregressive models. Production efficiency shocks explain more than half of the variance of key variables (both conditional and unconditional). The version with structural breaks produces much better median-based forecasting measures and almost the same mean-based forecasting measures due to significant errors near structural breaks. Various inflation measures respond similarly to monetary policy shocks, but differently to other shocks.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128273218","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}
This paper focuses on the effects of the ECB’s unconventional monetary policy on the member countries of the euro area. The analysis is based on a Global VAR model, which allows to take into account mutual influences of processes in the countries of the currency union. Identification of unconventional monetary policy shock is conducted using a shadow interest rate which reflects changes in economic agents’ expectations following the announcement of unconventional monetary policy measures. The model is estimated using data for the euro area from 2007 to 2018 and covers all of the key instances of implementation of unconventional measures by the ECB. The results show that expansionary policy leads to a significant rise in output and prices in the euro area. Additionally, the effects of unconventional monetary policy are heterogeneous across countries: the response to unconventional monetary policy shock is insignificant in countries that are strongly affected by the crisis, and the effectiveness of the measures varies across countries with different levels of banking sector capitalisation. It was also found that the efficiency of unconventional monetary policy measures against deflation depends upon spillovers of the interaction between core and periphery countries in the monetary union.
{"title":"Modelling the Effects of Unconventional Monetary Policy in a Heterogeneous Monetary Union","authors":"Sofya Kolesnik, E. Dobronravova","doi":"10.31477/rjmf.202201.03","DOIUrl":"https://doi.org/10.31477/rjmf.202201.03","url":null,"abstract":"This paper focuses on the effects of the ECB’s unconventional monetary policy on the member countries of the euro area. The analysis is based on a Global VAR model, which allows to take into account mutual influences of processes in the countries of the currency union. Identification of unconventional monetary policy shock is conducted using a shadow interest rate which reflects changes in economic agents’ expectations following the announcement of unconventional monetary policy measures. The model is estimated using data for the euro area from 2007 to 2018 and covers all of the key instances of implementation of unconventional measures by the ECB. The results show that expansionary policy leads to a significant rise in output and prices in the euro area. Additionally, the effects of unconventional monetary policy are heterogeneous across countries: the response to unconventional monetary policy shock is insignificant in countries that are strongly affected by the crisis, and the effectiveness of the measures varies across countries with different levels of banking sector capitalisation. It was also found that the efficiency of unconventional monetary policy measures against deflation depends upon spillovers of the interaction between core and periphery countries in the monetary union.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131237164","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 forecast the dynamics of unemployment in Russia using several machine learning methods: random forest, gradient boosting, elastic net, and neural networks. The scientific contribution of this paper is threefold. First, along with feed-forward, fully connected neural networks, we use sequence-to-sequence model recurrent neural networks, which take the time-series structure of the sample dataset into account. Second, in addition to univariate long short-term memory models, we include additional macroeconomic indicators in order to estimate multivariate recurrent neural networks. Third, the model evaluation process considers revisions of statistical information in real-time datasets. In order to increase the model’s predictive performance, we use additional unstructured indicators: search queries and news indices. Relative to the structural model of unemployment dynamics, the mean absolute forecast error for one month ahead is reduced by 65%, to 0.12 percentage points of the unemployment rate in the recurrent neural networks and long short-term memory models, and by 56%, to 0.14 percentage points in the modified gradient boosting algorithms. When accounting for revisions of statistical information, further reduction of the root-mean-square error by the models proposed is revealed, which highlights the importance of accounting for possible changes in the calculation of the values of macroeconomic indicators.
{"title":"Forecasting Unemployment in Russia Using Machine Learning Methods","authors":"Urmat Dzhunkeev","doi":"10.31477/rjmf.202201.73","DOIUrl":"https://doi.org/10.31477/rjmf.202201.73","url":null,"abstract":"In this paper, we forecast the dynamics of unemployment in Russia using several machine learning methods: random forest, gradient boosting, elastic net, and neural networks. The scientific contribution of this paper is threefold. First, along with feed-forward, fully connected neural networks, we use sequence-to-sequence model recurrent neural networks, which take the time-series structure of the sample dataset into account. Second, in addition to univariate long short-term memory models, we include additional macroeconomic indicators in order to estimate multivariate recurrent neural networks. Third, the model evaluation process considers revisions of statistical information in real-time datasets. In order to increase the model’s predictive performance, we use additional unstructured indicators: search queries and news indices. Relative to the structural model of unemployment dynamics, the mean absolute forecast error for one month ahead is reduced by 65%, to 0.12 percentage points of the unemployment rate in the recurrent neural networks and long short-term memory models, and by 56%, to 0.14 percentage points in the modified gradient boosting algorithms. When accounting for revisions of statistical information, further reduction of the root-mean-square error by the models proposed is revealed, which highlights the importance of accounting for possible changes in the calculation of the values of macroeconomic indicators.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131362710","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}
Dmitry A Gornostaev, A. Ponomarenko, S. Seleznev, Alexandra Sterkhova
We compiled, as part of a research project of Bank of Russia, a database on the revisions of a large set of short-term economic indicators and published it on the Bank of Russia website. The Research and Forecasting Department of the Bank of Russia plans to update this database in the future. We also perform an illustrative analysis of the properties of the revisions for a number of indicators. The preliminary results of this work indicate that the magnitude of the revisions is in many cases substantial.
{"title":"A Real-Time Historical Database of Macroeconomic Indicators for Russia","authors":"Dmitry A Gornostaev, A. Ponomarenko, S. Seleznev, Alexandra Sterkhova","doi":"10.31477/rjmf.202201.88","DOIUrl":"https://doi.org/10.31477/rjmf.202201.88","url":null,"abstract":"We compiled, as part of a research project of Bank of Russia, a database on the revisions of a large set of short-term economic indicators and published it on the Bank of Russia website. The Research and Forecasting Department of the Bank of Russia plans to update this database in the future. We also perform an illustrative analysis of the properties of the revisions for a number of indicators. The preliminary results of this work indicate that the magnitude of the revisions is in many cases substantial.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133611948","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}
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.
{"title":"Effect of Monetary Policy on Investment in Russian Regions","authors":"A. Shevelev, M. Kvaktun, K. Virovets","doi":"10.31477/rjmf.202104.31","DOIUrl":"https://doi.org/10.31477/rjmf.202104.31","url":null,"abstract":"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.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"13 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":"132178759","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}
Pub Date : 2021-12-01DOI: 10.31477/rjmf.202104.124
Ivan Khotulev
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.
{"title":"Review of the Bank of Russia – NES Workshop ‘Main Challenges in Banking: Risks, Liquidity, Pricing, and Digital Currencies’","authors":"Ivan Khotulev","doi":"10.31477/rjmf.202104.124","DOIUrl":"https://doi.org/10.31477/rjmf.202104.124","url":null,"abstract":"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.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"37 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":"121930743","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}
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.
{"title":"Forecasting Aggregate Retail Sales with Google Trends","authors":"E. Golovanova, A. Zubarev","doi":"10.31477/rjmf.202104.50","DOIUrl":"https://doi.org/10.31477/rjmf.202104.50","url":null,"abstract":"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.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"5 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":"134070057","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}
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.
{"title":"Determinants of Russia’s Sovereign Risk","authors":"E. Grigoryeva","doi":"10.31477/rjmf.202104.74","DOIUrl":"https://doi.org/10.31477/rjmf.202104.74","url":null,"abstract":"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.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"104 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":"128587491","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, 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.
{"title":"Balance Sheet Channel of Monetary Policy: Evidence from Credit Spreads of Russian Firms","authors":"Filipp Prokopev","doi":"10.31477/rjmf.202104.03","DOIUrl":"https://doi.org/10.31477/rjmf.202104.03","url":null,"abstract":"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.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"108 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":"123536886","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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