In this paper, we analyze the impact of a decline in property prices that leads to stressed recovery rates for collateral on the loss given default (LGD) parameter in portfolios of mortgage loans. After discussing the shape of a portfolio's loan-to-value (LTV) distribution, we prove that the average LGD's stress sensitivity depends on the LTV distribution, and we derive a closed-form solution for portfolio LGD under the assumption of beta-distributed LTV ratios. Further, we present numerical evidence that the relationship between LTV distribution and portfolio LGD is crucial for understanding the stress resilience of banks involved in the mortgage business. Our formula appears to be a meaningful starting point for benchmarking analyses by regulators, rating agencies and risk managers.
{"title":"Benchmarking the Loss Given Default Parameter for Mortgage Loan Portfolios Under Stress","authors":"C. Greve, L. Hahnenstein","doi":"10.21314/JCR.2016.217","DOIUrl":"https://doi.org/10.21314/JCR.2016.217","url":null,"abstract":"In this paper, we analyze the impact of a decline in property prices that leads to stressed recovery rates for collateral on the loss given default (LGD) parameter in portfolios of mortgage loans. After discussing the shape of a portfolio's loan-to-value (LTV) distribution, we prove that the average LGD's stress sensitivity depends on the LTV distribution, and we derive a closed-form solution for portfolio LGD under the assumption of beta-distributed LTV ratios. Further, we present numerical evidence that the relationship between LTV distribution and portfolio LGD is crucial for understanding the stress resilience of banks involved in the mortgage business. Our formula appears to be a meaningful starting point for benchmarking analyses by regulators, rating agencies and risk managers.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"78 1","pages":"79-107"},"PeriodicalIF":0.3,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88407751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We conduct a comprehensive study of some parametric models that are designed to fit the unusual bounded and bimodal distribution of loss given default (LGD). We first examine a smearing estimator, a Monte Carlo estimator and a global adjustment approach to refine transformation regression models that address issues with LGD boundary values. Although these refinements only marginally improve model performance, the smearing and Monte Carlo estimators help to reduce the sensitivity of transformation regressions to the adjustment factor. We then conduct a horse race among the refined transformation methods, five parametric models that are specifically suitable for LGD modeling (two-step, inflated beta, Tobit, censored gamma and two-tiered gamma regressions), fractional response regression and standard linear regression. We find that the sophisticated parametric models do not clearly outperform the simpler ones in either predictive accuracy or rank-ordering ability, in-sample, out-of-sample or out of time. Therefore, it is important for modelers and researchers to choose the model that is appropriate for their particular data set, considering differences in model complexity, computational burden, ease of implementation and model performance.
{"title":"Further Investigation of Parametric Loss Given Default Modeling","authors":"Phillip Li, M. Qi, Xiaofei Zhang, Xinlei Zhao","doi":"10.21314/JCR.2016.215","DOIUrl":"https://doi.org/10.21314/JCR.2016.215","url":null,"abstract":"We conduct a comprehensive study of some parametric models that are designed to fit the unusual bounded and bimodal distribution of loss given default (LGD). We first examine a smearing estimator, a Monte Carlo estimator and a global adjustment approach to refine transformation regression models that address issues with LGD boundary values. Although these refinements only marginally improve model performance, the smearing and Monte Carlo estimators help to reduce the sensitivity of transformation regressions to the adjustment factor. We then conduct a horse race among the refined transformation methods, five parametric models that are specifically suitable for LGD modeling (two-step, inflated beta, Tobit, censored gamma and two-tiered gamma regressions), fractional response regression and standard linear regression. We find that the sophisticated parametric models do not clearly outperform the simpler ones in either predictive accuracy or rank-ordering ability, in-sample, out-of-sample or out of time. Therefore, it is important for modelers and researchers to choose the model that is appropriate for their particular data set, considering differences in model complexity, computational burden, ease of implementation and model performance.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"18 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2016-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84435606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper analyzes the validity of using the loan-to-value (LTV) ratio to explain the behavior of mortgage borrowers at an empirical level. To perform this analysis we use data for mortgage loan portfolios securitized in Spain during the period 2005–8. In the regression models developed, we find that higher initial LTV ratios are associated with greater default risk. The relation between the probability of default and LTV seems to be nonlinear, and a sharp increase is seen for values greater than 80%. Our findings confirm the adequacy of the new Basel III proposal that sets nonlinear capital requirement levels for banks holding residential mortgage loans at different LTV ratios. However, the significance shown in the regression models estimated with the “seasoning” variable could be considered in order to improve the models used to measure capital requirements.
{"title":"Modeling corporate customers’ credit risk considering the ensemble approaches in multiclass classification: evidence from Iranian corporate credits","authors":"Parastoo Rafiee Vahid, Abbas Ahmadi","doi":"10.21314/JCR.2016.213","DOIUrl":"https://doi.org/10.21314/JCR.2016.213","url":null,"abstract":"This paper analyzes the validity of using the loan-to-value (LTV) ratio to explain the behavior of mortgage borrowers at an empirical level. To perform this analysis we use data for mortgage loan portfolios securitized in Spain during the period 2005–8. In the regression models developed, we find that higher initial LTV ratios are associated with greater default risk. The relation between the probability of default and LTV seems to be nonlinear, and a sharp increase is seen for values greater than 80%. Our findings confirm the adequacy of the new Basel III proposal that sets nonlinear capital requirement levels for banks holding residential mortgage loans at different LTV ratios. However, the significance shown in the regression models estimated with the “seasoning” variable could be considered in order to improve the models used to measure capital requirements.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"1 1","pages":"71-95"},"PeriodicalIF":0.3,"publicationDate":"2016-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86440262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In credit risk modeling, banks and insurance companies routinely use a single model for estimating key risk parameters. Combining several models to make a final prediction is not often considered. Using an ensemble or a collection of models rather than a single model can improve the accuracy and robustness of prediction results. In this study, we investigate two well-established ensemble learning methods (stochastic gradient boosting and random forest) and propose two new ensembles (ensemble by partial least squares and bag-boosting) in the application of predicting the loss given default. We demonstrate that an ensemble approach significantly increases the discriminatory power of the model compared with a single decision tree. In addition, the ensemble learning methods can be applied directly to predicting the exposure at default and probability of default with some simple modifications. The proposed approaches introduce a novel modeling framework that banks and other financial institutions can use to estimate and validate credit risk parameters based on the internal data of different portfolios. Moreover, the proposed approaches can be readily extended to general portfolio risk modeling in the areas of regulatory capital and economic capital management, loss forecasting, stress testing and pre-provision net revenue projections.
{"title":"Estimating Credit Risk Parameters Using Ensemble Learning Methods: An Empirical Study on Loss Given Default","authors":"Han Sheng Sun, Zi Jin","doi":"10.21314/jcr.2016.212","DOIUrl":"https://doi.org/10.21314/jcr.2016.212","url":null,"abstract":"In credit risk modeling, banks and insurance companies routinely use a single model for estimating key risk parameters. Combining several models to make a final prediction is not often considered. Using an ensemble or a collection of models rather than a single model can improve the accuracy and robustness of prediction results. In this study, we investigate two well-established ensemble learning methods (stochastic gradient boosting and random forest) and propose two new ensembles (ensemble by partial least squares and bag-boosting) in the application of predicting the loss given default. We demonstrate that an ensemble approach significantly increases the discriminatory power of the model compared with a single decision tree. In addition, the ensemble learning methods can be applied directly to predicting the exposure at default and probability of default with some simple modifications. The proposed approaches introduce a novel modeling framework that banks and other financial institutions can use to estimate and validate credit risk parameters based on the internal data of different portfolios. Moreover, the proposed approaches can be readily extended to general portfolio risk modeling in the areas of regulatory capital and economic capital management, loss forecasting, stress testing and pre-provision net revenue projections.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"11 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2016-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80168145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. O. González, Pablo Durán Santomil, Milagros Vivel Búa
This paper analyzes the validity of using the loan-to-value (LTV) ratio to explain the behavior of mortgage borrowers at an empirical level. To perform this analysis we use data for mortgage loan portfolios securitized in Spain during the period 2005-8. In the regression models developed, we find that higher initial LTV ratios are associated with greater default risk. The relation between the probability of default and LTV seems to be nonlinear, and a sharp increase is seen for values greater than 80%. Our findings confirm the adequacy of the new Basel III proposal that sets nonlinear capital requirement levels for banks holding residential mortgage loans at different LTV ratios. However, the significance shown in the regression models estimated with the "seasoning" variable could be considered in order to improve the models used to measure capital requirements.
{"title":"The Impact of Loan-to-Value on the Default Rate of Residential Mortgage-Backed Securities","authors":"L. O. González, Pablo Durán Santomil, Milagros Vivel Búa","doi":"10.21314/JCR.2016.210","DOIUrl":"https://doi.org/10.21314/JCR.2016.210","url":null,"abstract":"This paper analyzes the validity of using the loan-to-value (LTV) ratio to explain the behavior of mortgage borrowers at an empirical level. To perform this analysis we use data for mortgage loan portfolios securitized in Spain during the period 2005-8. In the regression models developed, we find that higher initial LTV ratios are associated with greater default risk. The relation between the probability of default and LTV seems to be nonlinear, and a sharp increase is seen for values greater than 80%. Our findings confirm the adequacy of the new Basel III proposal that sets nonlinear capital requirement levels for banks holding residential mortgage loans at different LTV ratios. However, the significance shown in the regression models estimated with the \"seasoning\" variable could be considered in order to improve the models used to measure capital requirements.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"4 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2016-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75875730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a credit portfolio framework that allows for dependencies between default probabilities, secured and unsecured recovery rates and exposures at default (EADs). The overall approach is an extension of the factor models of Pykhtin (2003) and Miu and Ozdemir (2006), with respect to differentiated recovery rates and the inclusion of dependent exposures. As there is empirical evidence for dependence between these risk parameters and observations for the EAD, and since the secured and unsecured recovery rates are available only in the case of a default, we propose a multivariate extension of the selection model of Heckman in order to estimate the unknown parameters within a maximum likelihood framework. Finally, we empirically demonstrate the effects of the dependence structure on the portfolio loss distribution and its risk measure for a hypothetical loan portfolio.
{"title":"A Credit Portfolio Framework Under Dependent Risk Parameters: Probability of Default, Loss Given Default and Exposure at Default","authors":"Johanna Eckert, K. Jakob, M. Fischer","doi":"10.21314/JCR.2016.202","DOIUrl":"https://doi.org/10.21314/JCR.2016.202","url":null,"abstract":"This paper introduces a credit portfolio framework that allows for dependencies between default probabilities, secured and unsecured recovery rates and exposures at default (EADs). The overall approach is an extension of the factor models of Pykhtin (2003) and Miu and Ozdemir (2006), with respect to differentiated recovery rates and the inclusion of dependent exposures. As there is empirical evidence for dependence between these risk parameters and observations for the EAD, and since the secured and unsecured recovery rates are available only in the case of a default, we propose a multivariate extension of the selection model of Heckman in order to estimate the unknown parameters within a maximum likelihood framework. Finally, we empirically demonstrate the effects of the dependence structure on the portfolio loss distribution and its risk measure for a hypothetical loan portfolio.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"75 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2016-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81562373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bills for unreimbursed medical care may be reported to national credit reporting agencies by third-party debt collectors. The use of this information in credit scoring models, which have not traditionally distinguished collection accounts for medical bills from other collection accounts, has been controversial because of the unique characteristics of medical debt. This paper explores the predictive value of medical collections in the context of a credit scoring model. We find that medical collections are less predictive of future credit performance than nonmedical collections. We also find that medical collections that have been paid in full are less predictive than those that remain unpaid. These results suggest that the practice of treating all collections the same over-penalizes the credit scores of consumers with medical collections and reduces the predictiveness of credit scoring models.
{"title":"Are All Collections Equal? The Case of Medical Debt","authors":"Kenneth P. Brevoort, Michelle Kambara","doi":"10.21314/jcr.2015.201","DOIUrl":"https://doi.org/10.21314/jcr.2015.201","url":null,"abstract":"Bills for unreimbursed medical care may be reported to national credit reporting agencies by third-party debt collectors. The use of this information in credit scoring models, which have not traditionally distinguished collection accounts for medical bills from other collection accounts, has been controversial because of the unique characteristics of medical debt. This paper explores the predictive value of medical collections in the context of a credit scoring model. We find that medical collections are less predictive of future credit performance than nonmedical collections. We also find that medical collections that have been paid in full are less predictive than those that remain unpaid. These results suggest that the practice of treating all collections the same over-penalizes the credit scores of consumers with medical collections and reduces the predictiveness of credit scoring models.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2015-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75125719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a novel multifactor analytic framework for credit portfolio modeling that incorporates the impact of the probability of default-loss given default correlation. In particular, we provide explicit expressions for calculating volatility, value-at-risk and expected shortfall, along with the associated Euler risk contributions. This approach is an extension and application of the framework proposed by Voropaev in 2011 and Buet-Golfouse and Owen in 2015. The main intended application is for large loan or mortgage portfolios, and as such we neglect idiosyncratic risk adjustments. This simplifies the expressions and improves computational speed. We finish by comparing the analytic results with a vanilla Monte Carlo implementation.
{"title":"Hermite Approximations in Credit Portfolio Modeling with Probability of Default-Loss Given Default Correlation","authors":"A. Owen, J. S. Bryers, Francois Buet-Golfouse","doi":"10.21314/jcr.2015.195","DOIUrl":"https://doi.org/10.21314/jcr.2015.195","url":null,"abstract":"In this paper, we propose a novel multifactor analytic framework for credit portfolio modeling that incorporates the impact of the probability of default-loss given default correlation. In particular, we provide explicit expressions for calculating volatility, value-at-risk and expected shortfall, along with the associated Euler risk contributions. This approach is an extension and application of the framework proposed by Voropaev in 2011 and Buet-Golfouse and Owen in 2015. The main intended application is for large loan or mortgage portfolios, and as such we neglect idiosyncratic risk adjustments. This simplifies the expressions and improves computational speed. We finish by comparing the analytic results with a vanilla Monte Carlo implementation.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"59 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2015-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80728779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enrique Eugenio Batiz‐Zuk, G. Christodoulakis, S. Poon
This paper provides Monte Carlo results for the performance of the method of moments (MM), maximum likelihood (ML) and ordinary least squares (OLS) estimators of the credit loss distribution implied by the Merton (1974) and Vasicek (1987, 2002) framework when the common or idiosyncratic asset-return factor is non-Gaussian and, thus, the true credit loss distribution deviates from the theoretical one. We find that OLS and ML outperform MM in small samples when the true data-generating process comprises a non-Gaussian common factor. This result intensifies as the sample size increases and holds in all cases. We also find that all three estimators present a large bias and variance when the true data-generating process comprises a non-Gaussian idiosyncratic factor. This last result holds independently of the sample size, across different asset correlation levels, and it intensifies for positive shape parameter values.
{"title":"The Robustness of Estimators in Structural Credit Loss Distributions","authors":"Enrique Eugenio Batiz‐Zuk, G. Christodoulakis, S. Poon","doi":"10.21314/JCR.2015.193","DOIUrl":"https://doi.org/10.21314/JCR.2015.193","url":null,"abstract":"This paper provides Monte Carlo results for the performance of the method of moments (MM), maximum likelihood (ML) and ordinary least squares (OLS) estimators of the credit loss distribution implied by the Merton (1974) and Vasicek (1987, 2002) framework when the common or idiosyncratic asset-return factor is non-Gaussian and, thus, the true credit loss distribution deviates from the theoretical one. We find that OLS and ML outperform MM in small samples when the true data-generating process comprises a non-Gaussian common factor. This result intensifies as the sample size increases and holds in all cases. We also find that all three estimators present a large bias and variance when the true data-generating process comprises a non-Gaussian idiosyncratic factor. This last result holds independently of the sample size, across different asset correlation levels, and it intensifies for positive shape parameter values.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"1 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84065819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Covered bonds are a specific example of senior secured debt. If the issuer of the bonds defaults the proceeds of the assets in the cover pool are used for their debt service. If in this situation the cover pool proceeds do not suffice for the debt service, the creditors of the bonds have recourse to the issuer's assets and their claims are pari passu with the claims of the creditors of senior unsecured debt. Historically, covered bonds have been very safe investments. During their more than two hundred years of existence, investors never suffered losses due to missed payments from covered bonds. From a risk management perspective, therefore modelling covered bonds losses is mainly of interest for estimating the impact that the asset encumbrance by the cover pool has on the loss characteristics of the issuer's senior unsecured debt. We explore one-period structural modelling approaches for covered bonds and senior unsecured debt losses with one and two asset value variables respectively. Obviously, two-assets models with separate values of the cover pool and the issuer's remaining portfolio allow for more realistic modelling. However, we demonstrate that exact calibration of such models may be impossible. We also investigate a one-asset model in which the riskiness of the cover pool is reflected by a risk-based adjustment of the encumbrance ratio of the issuer's assets.
{"title":"Fitting a Distribution to Value-at-Risk and Expected Shortfall, with an Application to Covered Bonds","authors":"Dirk Tasche","doi":"10.2139/ssrn.2611360","DOIUrl":"https://doi.org/10.2139/ssrn.2611360","url":null,"abstract":"Covered bonds are a specific example of senior secured debt. If the issuer of the bonds defaults the proceeds of the assets in the cover pool are used for their debt service. If in this situation the cover pool proceeds do not suffice for the debt service, the creditors of the bonds have recourse to the issuer's assets and their claims are pari passu with the claims of the creditors of senior unsecured debt. Historically, covered bonds have been very safe investments. During their more than two hundred years of existence, investors never suffered losses due to missed payments from covered bonds. From a risk management perspective, therefore modelling covered bonds losses is mainly of interest for estimating the impact that the asset encumbrance by the cover pool has on the loss characteristics of the issuer's senior unsecured debt. We explore one-period structural modelling approaches for covered bonds and senior unsecured debt losses with one and two asset value variables respectively. Obviously, two-assets models with separate values of the cover pool and the issuer's remaining portfolio allow for more realistic modelling. However, we demonstrate that exact calibration of such models may be impossible. We also investigate a one-asset model in which the riskiness of the cover pool is reflected by a risk-based adjustment of the encumbrance ratio of the issuer's assets.","PeriodicalId":44244,"journal":{"name":"Journal of Credit Risk","volume":"58 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2015-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73190229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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