This paper surveys a recent IBRN initiative that studied the interactions of monetary and macroprudential policies. The general research questions is: To what extent does macroprudential policy reshape the transmission of monetary policy? The concrete context differs across studies. There are three main findings. First, domestic prudential policy in recipient economies can partially offset inward transmission from systemic economies to domestic lending, with the size of the effect being heterogeneous across banks in source countries. Second, the stance of prudential policy in the source economy also matters and can affect the intensity of outward monetary transmission. Finally, there is much heterogeneity in the strength of effect across different prudential instruments.
{"title":"IBRN Initiative on Interactions of Monetary and Prudential Policies","authors":"K. Styrin, Yulia V. Ushakova","doi":"10.31477/rjmf.202003.58","DOIUrl":"https://doi.org/10.31477/rjmf.202003.58","url":null,"abstract":"This paper surveys a recent IBRN initiative that studied the interactions of monetary and macroprudential policies. The general research questions is: To what extent does macroprudential policy reshape the transmission of monetary policy? The concrete context differs across studies. There are three main findings. First, domestic prudential policy in recipient economies can partially offset inward transmission from systemic economies to domestic lending, with the size of the effect being heterogeneous across banks in source countries. Second, the stance of prudential policy in the source economy also matters and can affect the intensity of outward monetary transmission. Finally, there is much heterogeneity in the strength of effect across different prudential instruments.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132386251","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 perform a microeconomic analysis of positive and negative imbalances in the maturity structure of Russian banks’ transactions. In particular, using Heckman selection models at the cross-section of Russian banks, we test the ability of such imbalances to predict the probability of the detection of banks’ negative net worth and its expected magnitude in advance (three months before negative worth detection). The estimation results show that, first, certain indicators of imbalances do offer ‘value added’ in predicting ‘holes’ in banks’ capital: taking into account these imbalances in banks’ short- and medium-term transactions with households and short-term transactions with enterprises improves the quality of out-ofsample forecasts. Second, the very division into positive and negative imbalances makes sense: the effects are in many cases found to be opposite with respect to the size and likelihood of negative net worth detection at banks. Third, a separate analysis of banking transactions with households and those with businesses is also of great importance: the effect of imbalances in transactions similar in maturity structure but with different types of economic agents is in many cases opposite in sign. The results may be useful for the Bank of Russia in identifying potentially fragile banks as part of its prudential policy.
{"title":"Maturity Structure of Banking Transactions and Its Role in Predicting Negative Net Worth of Banks","authors":"Mikhail Mikhail, Cerge-Ei","doi":"10.31477/rjmf.202002.70","DOIUrl":"https://doi.org/10.31477/rjmf.202002.70","url":null,"abstract":"In this paper, we perform a microeconomic analysis of positive and negative imbalances in the maturity structure of Russian banks’ transactions. In particular, using Heckman selection models at the cross-section of Russian banks, we test the ability of such imbalances to predict the probability of the detection of banks’ negative net worth and its expected magnitude in advance (three months before negative worth detection). The estimation results show that, first, certain indicators of imbalances do offer ‘value added’ in predicting ‘holes’ in banks’ capital: taking into account these imbalances in banks’ short- and medium-term transactions with households and short-term transactions with enterprises improves the quality of out-ofsample forecasts. Second, the very division into positive and negative imbalances makes sense: the effects are in many cases found to be opposite with respect to the size and likelihood of negative net worth detection at banks. Third, a separate analysis of banking transactions with households and those with businesses is also of great importance: the effect of imbalances in transactions similar in maturity structure but with different types of economic agents is in many cases opposite in sign. The results may be useful for the Bank of Russia in identifying potentially fragile banks as part of its prudential policy.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116683175","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 : 2020-06-01DOI: 10.31477/rjmf.202002.101
H. Penikas
In contemporary world, binary choice models are used in many areas. However, for all such areas, a problem arises when the share of one of the classes in the data sample is small. If this share is significantly small, this class is referred to as low default class. The purpose of this paper is to examine the definitions of such a portfolio and the approaches to building models on its basis. Although various methods exist for obtaining results, this paper shows that distinguishing a low default portfolio class, on the one hand, benefits banks, as does any more detailed segmentation, but, on the other hand, it deteriorates the statistical properties of the models for the probability of default. It is therefore justified that for the internal rating based approach in the framework of Basel II and Basel III the regulator should require that banks build their models based on combined data sets discouraging them from setting excessive low default portfolio classes.
{"title":"Low Default Portfolios in Basel II and Basel III as a Special Case of Significantly Unbalanced Classes in Binary Choice Models","authors":"H. Penikas","doi":"10.31477/rjmf.202002.101","DOIUrl":"https://doi.org/10.31477/rjmf.202002.101","url":null,"abstract":"In contemporary world, binary choice models are used in many areas. However, for all such areas, a problem arises when the share of one of the classes in the data sample is small. If this share is significantly small, this class is referred to as low default class. The purpose of this paper is to examine the definitions of such a portfolio and the approaches to building models on its basis. Although various methods exist for obtaining results, this paper shows that distinguishing a low default portfolio class, on the one hand, benefits banks, as does any more detailed segmentation, but, on the other hand, it deteriorates the statistical properties of the models for the probability of default. It is therefore justified that for the internal rating based approach in the framework of Basel II and Basel III the regulator should require that banks build their models based on combined data sets discouraging them from setting excessive low default portfolio classes.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127778935","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 research question raised in this paper is an investigation of the effect of the announcement of US and EU sanctions on the stock returns of the targeted companies listed on the Moscow Exchange. The strategy for identification is based on firm-specific and multivariate short-term event studies. Firm-specific event study of eight sanctions that targeted 14 entities at different times results in a statistically significant ?5.4% estimate of the expected cumulative abnormal return within a window of seven trading days.
{"title":"Announcements of Sanctions and the Russian Equity Market: An Event Study Approach","authors":"Pavel Dovbnya","doi":"10.31477/rjmf.202001.74","DOIUrl":"https://doi.org/10.31477/rjmf.202001.74","url":null,"abstract":"The research question raised in this paper is an investigation of the effect of the announcement of US and EU sanctions on the stock returns of the targeted companies listed on the Moscow Exchange. The strategy for identification is based on firm-specific and multivariate short-term event studies. Firm-specific event study of eight sanctions that targeted 14 entities at different times results in a statistically significant ?5.4% estimate of the expected cumulative abnormal return within a window of seven trading days.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123376623","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}
Forecasting Russian inflation is an important practical task. This paper applies two benchmark machine learning models to this task. Although machine learning in general has been an active area of research for the past 20 years, those methods began gaining popularity in the literature on inflation forecasting only recently. In this paper, I employ neural networks and support-vector machines to forecast inflation in Russia. I also apply Shapley decomposition to obtain economic interpretation of inflation forecasts. The performance of these two models is then compared with the performance of more conventional approaches that serve as benchmark forecasts. These are an autoregression and a linear regression with regularisation (a.k.a. ridge regression). My empirical findings suggest that both machine learning models forecast inflation no worse than the conventional benchmarks and that the Shapley decomposition is a suitable framework that yields a meaningful interpretation to the neural network forecast. I conclude that machine learning methods offer a promising tool of inflation forecasting.
{"title":"Forecasting Inflation in Russia Using Neural Networks","authors":"E. Pavlov","doi":"10.31477/rjmf.202001.57","DOIUrl":"https://doi.org/10.31477/rjmf.202001.57","url":null,"abstract":"Forecasting Russian inflation is an important practical task. This paper applies two benchmark machine learning models to this task. Although machine learning in general has been an active area of research for the past 20 years, those methods began gaining popularity in the literature on inflation forecasting only recently. In this paper, I employ neural networks and support-vector machines to forecast inflation in Russia. I also apply Shapley decomposition to obtain economic interpretation of inflation forecasts. The performance of these two models is then compared with the performance of more conventional approaches that serve as benchmark forecasts. These are an autoregression and a linear regression with regularisation (a.k.a. ridge regression). My empirical findings suggest that both machine learning models forecast inflation no worse than the conventional benchmarks and that the Shapley decomposition is a suitable framework that yields a meaningful interpretation to the neural network forecast. I conclude that machine learning methods offer a promising tool of inflation forecasting.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125977743","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 studies the effects of the pension increase in Russia in 2010 on the labour force participation decisions and living arrangements of senior people and their family members. There is not much research on the effects of pension rises in Russia. In particular, researchers have not yet analysed the influence of pension increases in Russia on non-elderly people nor the heterogeneity of this influence. The increase in pensions in 2010 is of particular interest due to its unique magnitude, its relative independence from economic trends in Russia at that time, and its plausible exogeneity for pensioners. This study provides evidence that this jump in pension caused an approximately 5 percentage point increase in the relative number of seniors who chose to retire. The effect was stronger in the two biggest cities of Russia, namely Moscow and Saint Petersburg, where before 2010 a substantial number of people continued to work after reaching the pension age. One out of four employed pensioners living in these cities left the labour force in 2010. In addition, this paper shows a relatively unexpected external effect on younger individuals. The labour force participation decisions of younger people who lived with pension receivers were influenced considerably. The non-seniors who lived with pensioners, compared with their peers, were less likely to work or to look for jobs. The change in pensions also affected living arrangements. The rate of pension receivers living with their children and grandchildren went up significantly. Thus, the evidence from the 2010 pension increase highlights the fact that policies might have an impact not only on the target group of population, but on the family members of this group as well.
{"title":"Expected and Unexpected Consequences of Russian Pension Increase in 2010","authors":"I. Suvorov","doi":"10.31477/rjmf.202001.92","DOIUrl":"https://doi.org/10.31477/rjmf.202001.92","url":null,"abstract":"This paper studies the effects of the pension increase in Russia in 2010 on the labour force participation decisions and living arrangements of senior people and their family members. There is not much research on the effects of pension rises in Russia. In particular, researchers have not yet analysed the influence of pension increases in Russia on non-elderly people nor the heterogeneity of this influence. The increase in pensions in 2010 is of particular interest due to its unique magnitude, its relative independence from economic trends in Russia at that time, and its plausible exogeneity for pensioners. This study provides evidence that this jump in pension caused an approximately 5 percentage point increase in the relative number of seniors who chose to retire. The effect was stronger in the two biggest cities of Russia, namely Moscow and Saint Petersburg, where before 2010 a substantial number of people continued to work after reaching the pension age. One out of four employed pensioners living in these cities left the labour force in 2010. In addition, this paper shows a relatively unexpected external effect on younger individuals. The labour force participation decisions of younger people who lived with pension receivers were influenced considerably. The non-seniors who lived with pensioners, compared with their peers, were less likely to work or to look for jobs. The change in pensions also affected living arrangements. The rate of pension receivers living with their children and grandchildren went up significantly. Thus, the evidence from the 2010 pension increase highlights the fact that policies might have an impact not only on the target group of population, but on the family members of this group as well.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121359670","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}
Sabit T. Khakimzhanov, Ye.S. Mustafin, Olzhas Kubenbayev, Dulat Atabek
Motivated by the shortcomings of the yield curve method used by the Kazakhstan Stock Exchange (KASE), we designed an algorithmic method of constructing a yield curve in a market with low and variable liquidity. We chose Nelson-Seigel as a curve and the ten most recent transactions in each subrange of maturity as the data. Both decisions stemmed from the constraints of an illiquid and inefficient market. The parsimony and rigidity of Nelson-Seigel proved useful when trades are few and prices are far apart. The choice of sampling is meant to produce enough sufficiently spaced observations, albeit at the expense of synchronicity. To provide the user better context for the curve and enable informed interpretation, we recommend supplementing the curves and their parameters with metrics of fit and age of the sample. Using the data from KASE, we computed the curve for each week starting from mid-2010 to end-2018 and made the results publicly available to provide access to interest rate data for analysts and to facilitate macroeconomic research.
{"title":"Constructing a Yield Curve in a Market with Low Liquidity","authors":"Sabit T. Khakimzhanov, Ye.S. Mustafin, Olzhas Kubenbayev, Dulat Atabek","doi":"10.31477/rjmf.201904.71","DOIUrl":"https://doi.org/10.31477/rjmf.201904.71","url":null,"abstract":"Motivated by the shortcomings of the yield curve method used by the Kazakhstan Stock Exchange (KASE), we designed an algorithmic method of constructing a yield curve in a market with low and variable liquidity. We chose Nelson-Seigel as a curve and the ten most recent transactions in each subrange of maturity as the data. Both decisions stemmed from the constraints of an illiquid and inefficient market. The parsimony and rigidity of Nelson-Seigel proved useful when trades are few and prices are far apart. The choice of sampling is meant to produce enough sufficiently spaced observations, albeit at the expense of synchronicity. To provide the user better context for the curve and enable informed interpretation, we recommend supplementing the curves and their parameters with metrics of fit and age of the sample. Using the data from KASE, we computed the curve for each week starting from mid-2010 to end-2018 and made the results publicly available to provide access to interest rate data for analysts and to facilitate macroeconomic research.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129209268","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}
We estimate the natural rate of interest for Russia in the short term and long term using three definitions of the rate and discuss the possible implications of the results for monetary policy. To start with, we consider partial equilibrium (under no-arbitrage condition), which is presented in the papers on estimating the natural rate. The estimates turn out to be extremely sensitive to assumptions about model parameters. The estimates based on the uncovered interest rate parity, though dependent only on observable (market) variables, impose an additional strong assumption of the path of the future equilibrium exchange rate. We supplement these calculations with calculations in panel data (for long-term equilibrium) and using semi-structural methods (for current equilibrium). To get estimates according to the strict definition of the natural rate we estimate a real business cycle model of the resource-based economy with investments using Russian data. All the estimates are highly uncertain. Taking into account the latter, the central bank should use robust monetary policy rules and avoid communicating the natural rate at least until there has been a sufficient history of business cycles in Russia.
{"title":"Estimates of the Natural Rate of Interest for Russia: Is Navigating by the Stars Useful?","authors":"A. Sinyakov, Alexey Porshakov","doi":"10.31477/rjmf.201904.03","DOIUrl":"https://doi.org/10.31477/rjmf.201904.03","url":null,"abstract":"We estimate the natural rate of interest for Russia in the short term and long term using three definitions of the rate and discuss the possible implications of the results for monetary policy. To start with, we consider partial equilibrium (under no-arbitrage condition), which is presented in the papers on estimating the natural rate. The estimates turn out to be extremely sensitive to assumptions about model parameters. The estimates based on the uncovered interest rate parity, though dependent only on observable (market) variables, impose an additional strong assumption of the path of the future equilibrium exchange rate. We supplement these calculations with calculations in panel data (for long-term equilibrium) and using semi-structural methods (for current equilibrium). To get estimates according to the strict definition of the natural rate we estimate a real business cycle model of the resource-based economy with investments using Russian data. All the estimates are highly uncertain. Taking into account the latter, the central bank should use robust monetary policy rules and avoid communicating the natural rate at least until there has been a sufficient history of business cycles in Russia.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131901823","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 regards financial risk management, its issues have already been worked through rather extensively both in theory and in practice. However, this is not the case when it comes to operational risks. The reason for this does not lie in the two types of risk having differing levels of significance (both types can have disastrous consequences for an organisation); it is rather due to their differing nature and, in the case of operational risks, more complex cause and effect relationships between the sources and consequences of risks. These circumstances make it extremely challenging not only to assess, but also to understand operational risks. Moreover, whereas a lot has already been done in regards to the operational risks faced by commercial institutions (which is largely thanks to the efforts of supervisory bodies who consider this a matter of financial stability), it is only in the last two decades that supervisory bodies themselves (central banks and other regulators, hereinafter referred to as regulators) have been seen to implement operational risk management systems. In this paper, we analyse the prerequisites for the formation of a systematic approach to operational risk management in international practices and conduct comparative analysis of its reasonableness for using by regulators and commercial institutions. Our analysis confirms the relevance of this new trend and demonstrates that, given their specific nature, when regulators implement operational risk management systems in their activities, risk culture and top-down support issues come to the fore.
{"title":"Systematic Approach to Operational Risk Management in Central Banks (Regulators): Prerequisites, Current Issues, and Development Prospects","authors":"V. Goreglyad","doi":"10.31477/rjmf.201904.99","DOIUrl":"https://doi.org/10.31477/rjmf.201904.99","url":null,"abstract":"As regards financial risk management, its issues have already been worked through rather extensively both in theory and in practice. However, this is not the case when it comes to operational risks. The reason for this does not lie in the two types of risk having differing levels of significance (both types can have disastrous consequences for an organisation); it is rather due to their differing nature and, in the case of operational risks, more complex cause and effect relationships between the sources and consequences of risks. These circumstances make it extremely challenging not only to assess, but also to understand operational risks. Moreover, whereas a lot has already been done in regards to the operational risks faced by commercial institutions (which is largely thanks to the efforts of supervisory bodies who consider this a matter of financial stability), it is only in the last two decades that supervisory bodies themselves (central banks and other regulators, hereinafter referred to as regulators) have been seen to implement operational risk management systems. In this paper, we analyse the prerequisites for the formation of a systematic approach to operational risk management in international practices and conduct comparative analysis of its reasonableness for using by regulators and commercial institutions. Our analysis confirms the relevance of this new trend and demonstrates that, given their specific nature, when regulators implement operational risk management systems in their activities, risk culture and top-down support issues come to the fore.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129931269","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 Bank of Russia’s international research conference, ‘Macroprudential Policy Effectiveness: Theory and Practice’, was held in St. Petersburg in early July. This review will briefly summarize the discussions with a strong focus on research insights, both the authors’ and our own, that are of practical importance to central bank policy.
{"title":"Review of Bank of Russia Conference on ‘Macroprudential Policy Effectiveness: Theory and Practice’","authors":"N. Ivanova, M. Andreev, A. Sinyakov, I. Shevchuk","doi":"10.31477/rjmf.201903.89","DOIUrl":"https://doi.org/10.31477/rjmf.201903.89","url":null,"abstract":"The Bank of Russia’s international research conference, ‘Macroprudential Policy Effectiveness: Theory and Practice’, was held in St. Petersburg in early July. This review will briefly summarize the discussions with a strong focus on research insights, both the authors’ and our own, that are of practical importance to central bank policy.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132574098","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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