An early warning of corporate financial crises has long been the focus of investors and enterprises. Integrated early warning models for financial crises perform better than normal models, but most integrated models are very complex, elusive and computationally time-consuming. This paper aims to simplify the early warning model for financial crises by collecting and analyzing the financial data of Chinese special treatment (ST) companies, normally listed companies and cancel special treatment (CST) companies. To further predict the financial risks of companies, we put forward a finance-predicting model based on the k-means++ algorithm and an improved radial basis function neural network (RBF NN), and we compare their respective statistics. We indicate by experiment that combining k-means++ with the improved RBF NN helps to better predict financial risks for companies, which is effective in the risk control of financial management.
{"title":"A K-means++-improved Radial Basis Function Neural Network Model for Corporate Financial Crisis Early Warning: An Empirical Model Validation for Chinese Listed Companies","authors":"Danyang Lv, Chong Wu, Linxiao Dong","doi":"10.21314/jrmv.2020.223","DOIUrl":"https://doi.org/10.21314/jrmv.2020.223","url":null,"abstract":"An early warning of corporate financial crises has long been the focus of investors and enterprises. Integrated early warning models for financial crises perform better than normal models, but most integrated models are very complex, elusive and computationally time-consuming. This paper aims to simplify the early warning model for financial crises by collecting and analyzing the financial data of Chinese special treatment (ST) companies, normally listed companies and cancel special treatment (CST) companies. To further predict the financial risks of companies, we put forward a finance-predicting model based on the k-means++ algorithm and an improved radial basis function neural network (RBF NN), and we compare their respective statistics. We indicate by experiment that combining k-means++ with the improved RBF NN helps to better predict financial risks for companies, which is effective in the risk control of financial management.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49617693","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}
{"title":"On the mathematical modeling of point-in-time and through-the-cycle probability of default estimation/validation","authors":"Xin Zhang, T. Tung","doi":"10.21314/jrmv.2018.199","DOIUrl":"https://doi.org/10.21314/jrmv.2018.199","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48256182","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}
{"title":"Incorporating volatility in tolerance intervals for pair-trading strategy and backtesting","authors":"Cathy W. S. Chen, Tsai-Yu Lin, T. Y. Huang","doi":"10.21314/jrmv.2019.202","DOIUrl":"https://doi.org/10.21314/jrmv.2019.202","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49105976","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}
{"title":"The utility of Basel III rules on excessive violations of internal risk models","authors":"Wayne Tarrant","doi":"10.21314/JRMV.2018.200","DOIUrl":"https://doi.org/10.21314/JRMV.2018.200","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"48 26","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41285728","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}
Understanding and quantifying the model risk inherent in loss projection models used in the macroeconomic stress testing and impairment estimation is of significant concern for both banks and regulators. The application of relative entropy techniques allow model misspecification robustness to be numerically quantified using exponential tilting towards an alternative probability law. Using a particular loss forecasting model we quantify the model worst-case loss term-structures to yield insight into the behavior of the worst-case. The worst-case obtained represents in general an upward scaling of the term-structure consistent with the exponential tilting adjustment. The relative entropy approach to model risk we use has its foundation in economics with robust forecasting analysis and has recently started to be applied in risk management. The technique can complement the traditional model risk quantification techniques where a specific direction or range of model misspecification reasons are usually considered, such as, model sensitivity analysis, model parameter uncertainty analysis, competing models, and, conservative model assumptions.
{"title":"Quantification of model risk in stress testing and scenario analysis","authors":"Jimmy Skoglund","doi":"10.21314/jrmv.2019.201","DOIUrl":"https://doi.org/10.21314/jrmv.2019.201","url":null,"abstract":"Understanding and quantifying the model risk inherent in loss projection models used in the macroeconomic stress testing and impairment estimation is of significant concern for both banks and regulators. The application of relative entropy techniques allow model misspecification robustness to be numerically quantified using exponential tilting towards an alternative probability law. Using a particular loss forecasting model we quantify the model worst-case loss term-structures to yield insight into the behavior of the worst-case. The worst-case obtained represents in general an upward scaling of the term-structure consistent with the exponential tilting adjustment. The relative entropy approach to model risk we use has its foundation in economics with robust forecasting analysis and has recently started to be applied in risk management. The technique can complement the traditional model risk quantification techniques where a specific direction or range of model misspecification reasons are usually considered, such as, model sensitivity analysis, model parameter uncertainty analysis, competing models, and, conservative model assumptions.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138494451","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}
The estimation of portfolio value-at-risk (VaR) requires a good estimate of the covariance matrix. As it is well known that a sample covariance matrix based on some historical rolling window is noisy and is a poor estimate for the high-dimensional population covariance matrix, to estimate the conditional portfolio VaR we develop a framework using the dynamic conditional covariance model, within which various de-noising tools are employed for the estimation of the unconditional covariance target. Various de-noising treatments in our study include shrinkage methods, random matrix theory methods and regularization methods. We validate the model empirically by using various coverage tests and loss function measures and discover that the choice of de-noising treatments for the covariance target plays a critical role in measuring the accuracy of the dynamic portfolio VaR estimates. In our large-scale empirical evaluation of de-noising tools, the regularization methods seem to produce the poorest VaR estimates under various coverage tests and loss function measures, while the shrinkage methods and the random matrix theory methods produce comparable results.
{"title":"An Empirical Evaluation of Large Dynamic Covariance Models in Portfolio Value-at-Risk Estimation","authors":"K. Law, W. Li, P. Yu","doi":"10.21314/jrmv.2020.221","DOIUrl":"https://doi.org/10.21314/jrmv.2020.221","url":null,"abstract":"The estimation of portfolio value-at-risk (VaR) requires a good estimate of the covariance matrix. As it is well known that a sample covariance matrix based on some historical rolling window is noisy and is a poor estimate for the high-dimensional population covariance matrix, to estimate the conditional portfolio VaR we develop a framework using the dynamic conditional covariance model, within which various de-noising tools are employed for the estimation of the unconditional covariance target. Various de-noising treatments in our study include shrinkage methods, random matrix theory methods and regularization methods. We validate the model empirically by using various coverage tests and loss function measures and discover that the choice of de-noising treatments for the covariance target plays a critical role in measuring the accuracy of the dynamic portfolio VaR estimates. In our large-scale empirical evaluation of de-noising tools, the regularization methods seem to produce the poorest VaR estimates under various coverage tests and loss function measures, while the shrinkage methods and the random matrix theory methods produce comparable results.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43337688","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}
Saeed Shaker-Akhtekhane, M. Seighali, Solmaz Poorabbas
{"title":"A comprehensive evaluation of value-at-risk models and a comparison of their performance in emerging markets","authors":"Saeed Shaker-Akhtekhane, M. Seighali, Solmaz Poorabbas","doi":"10.21314/JRMV.2018.196","DOIUrl":"https://doi.org/10.21314/JRMV.2018.196","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"83 1","pages":"1-16"},"PeriodicalIF":0.7,"publicationDate":"2018-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89216152","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}
{"title":"Evaluating the credit exposure of interest rate derivatives under the real-world measure","authors":"T. Yasuoka","doi":"10.21314/JRMV.2018.195","DOIUrl":"https://doi.org/10.21314/JRMV.2018.195","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"6 1","pages":"69-95"},"PeriodicalIF":0.7,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91065155","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}
{"title":"Back to backtesting: integrated backtesting for value-at-risk and expected shortfall in practice","authors":"Carsten S. Wehn","doi":"10.21314/JRMV.2018.197","DOIUrl":"https://doi.org/10.21314/JRMV.2018.197","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"61 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2018-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90698490","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}
{"title":"Analytical expressions of risk quantities for composite models","authors":"J. Sarabia, E. Calderín-Ojeda","doi":"10.21314/JRMV.2018.194","DOIUrl":"https://doi.org/10.21314/JRMV.2018.194","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"7 1","pages":"75-101"},"PeriodicalIF":0.7,"publicationDate":"2018-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91032520","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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