{"title":"A prudent loss given default estimation for mortgages. II","authors":"Bogie Ozdemir, Emma Huang","doi":"10.21314/jrmv.2021.008","DOIUrl":"https://doi.org/10.21314/jrmv.2021.008","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"51 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75556906","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":"A pricing model with dynamic credit rating transition matrixes","authors":"Yun-Cheng Tsai,Sheng-Hsuan Lin,Yuh-Dauh Lyuu","doi":"10.21314/jrmv.2021.007","DOIUrl":"https://doi.org/10.21314/jrmv.2021.007","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" 55","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138494479","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":"Evaluation of backtesting techniques on risk models with different horizons","authors":"Grigorios Kontaxis, I. Tsolas","doi":"10.21314/jrmv.2021.011","DOIUrl":"https://doi.org/10.21314/jrmv.2021.011","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"55 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83747620","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 value-at-risk of time-series momentum and contrarian trading strategies","authors":"Keunbae Ahn,Jihye Park,KiHoon Hong","doi":"10.21314/jrmv.2021.006","DOIUrl":"https://doi.org/10.21314/jrmv.2021.006","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" 69","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138494478","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 formulates a mathematical model for generating receiver operating characteristic (ROC) curves without underlying data. Credit scoring practitioners know that the Gini coefficient usually drops if it is only calculated on cases above the cutoff. This fact is not a mathematical necessity, however, as it is theoretically possible to get an ROC curve that keeps the same Gini coefficient no matter how big a share of lowest score cases are excluded from the calculation (a “right-hand” fractal ROC curve). Analogously, a left-hand fractal ROC curve would be a curve that keeps its Gini coefficient constant below any cutoff point. The model proposed here is a linear combination of left- and right-hand ROC curves. A bifractal ROC curve is drawn with just two parameters: one responsible for the shape of the curve and the other responsible for the area under the curve (a Gini coefficient). As is shown in this paper, most real-life credit-scoring ROC curves lie between the two fractal curves. In consequence, the Gini coefficient will be consistently lower when computed only on approved loans.
{"title":"Bifractal Receiver Operating Characteristic Curves: A Formula for Generating Receiver Operating Characteristic Curves in Credit-Scoring Contexts","authors":"Błażej Kochański","doi":"10.21314/JRMV.2020.231","DOIUrl":"https://doi.org/10.21314/JRMV.2020.231","url":null,"abstract":"This paper formulates a mathematical model for generating receiver operating characteristic (ROC) curves without underlying data. Credit scoring practitioners know that the Gini coefficient usually drops if it is only calculated on cases above the cutoff. This fact is not a mathematical necessity, however, as it is theoretically possible to get an ROC curve that keeps the same Gini coefficient no matter how big a share of lowest score cases are excluded from the calculation (a “right-hand” fractal ROC curve). Analogously, a left-hand fractal ROC curve would be a curve that keeps its Gini coefficient constant below any cutoff point. The model proposed here is a linear combination of left- and right-hand ROC curves. A bifractal ROC curve is drawn with just two parameters: one responsible for the shape of the curve and the other responsible for the area under the curve (a Gini coefficient). As is shown in this paper, most real-life credit-scoring ROC curves lie between the two fractal curves. In consequence, the Gini coefficient will be consistently lower when computed only on approved loans.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49172892","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":"Validation of index and benchmark assignment: adequacy of capturing tail risk","authors":"Lukasz Prorokowski","doi":"10.21314/jrmv.2019.214","DOIUrl":"https://doi.org/10.21314/jrmv.2019.214","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"1 1","pages":"71-105"},"PeriodicalIF":0.7,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88649407","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":"Value-at-risk in the European energy market: a comparison of parametric, historical simulation and quantile regression value-at-risk","authors":"Sjur Westgaard, Gisle Hoel Århus, M. Frydenberg","doi":"10.21314/jrmv.2019.213","DOIUrl":"https://doi.org/10.21314/jrmv.2019.213","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"21 1","pages":"43-69"},"PeriodicalIF":0.7,"publicationDate":"2019-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75317458","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":"Quantification of the estimation risk inherent in loss distribution approach models","authors":"Kevin Panman, Liesl van Biljon, L. Haasbroek","doi":"10.21314/jrmv.2019.212","DOIUrl":"https://doi.org/10.21314/jrmv.2019.212","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43162317","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 investigates the effects of window size selection on various models for Value-at-Risk (VaR) forecasting using high performance computing. Subsequently, automated procedures using change-point analysis for optimal window size selection are proposed. In particular, stationary bootstrapping and the peaks-over-threshold methods are utilized for the rolling daily VaR estimation and are contrasted with the classical conditional Gaussian model. It is evidenced that change-point procedures can, on average, result in more adequate risk predictions than a predetermined fixed window size. The data sets analyzed include indices across 5 continents, i.e., the Dow Jones Industrial Average Index (DJI), the Financial Times Stock Exchange 100 Index (UKX), the NIKKEI Top 225 Index (NKY), the Johannesburg Stock Exchange Top 40 Index (JSE Top40), the Ibovespa Brazil Sao Paulo Stock Exchange All Index (IBOV), and the Bombay Stock Exchange Top 500 Index (BSE 500).
{"title":"A study on window-size selection for threshold and bootstrap value-at-risk models","authors":"Anri Smith, Chun-Kai Huang","doi":"10.21314/jrmv.2019.211","DOIUrl":"https://doi.org/10.21314/jrmv.2019.211","url":null,"abstract":"This paper investigates the effects of window size selection on various models for Value-at-Risk (VaR) forecasting using high performance computing. Subsequently, automated procedures using change-point analysis for optimal window size selection are proposed. In particular, stationary bootstrapping and the peaks-over-threshold methods are utilized for the rolling daily VaR estimation and are contrasted with the classical conditional Gaussian model. It is evidenced that change-point procedures can, on average, result in more adequate risk predictions than a predetermined fixed window size. The data sets analyzed include indices across 5 continents, i.e., the Dow Jones Industrial Average Index (DJI), the Financial Times Stock Exchange 100 Index (UKX), the NIKKEI Top 225 Index (NKY), the Johannesburg Stock Exchange Top 40 Index (JSE Top40), the Ibovespa Brazil Sao Paulo Stock Exchange All Index (IBOV), and the Bombay Stock Exchange Top 500 Index (BSE 500).","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47743468","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}
Modeling loss given default has increased in popularity as it has become a crucial parameter for establishing capital buffers under Basel II and III and for calculating the impairment of financial assets under the International Financial Reporting Standard 9. The most recent literature on this topic focuses mainly on estimation methods and less on the variables used to explain the variability in loss given default. In this paper, we expand this part of the modeling process by constructing a set of client-behavior-based predictors that can be used to construct more precise models, and we investigate the economic justifications empirically to examine their potential usage. The main novelty introduced in this paper is the connection between loss given default and the behavior of the contract owner, not just the contract itself. This approach results in the reduction of the values of selected error measures and progressively improves the forecasting ability. The effect is more visible in a parametric method (fractional regression) than in a nonparametric method (regression tree). Our findings support incorporating client-oriented information into loss given default models.
{"title":"Beyond the Contract: Client Behavior from Origination to Default as the New Set of the Loss Given Default Risk Drivers","authors":"Wojciech Starosta","doi":"10.21314/JRMV.2020.234","DOIUrl":"https://doi.org/10.21314/JRMV.2020.234","url":null,"abstract":"Modeling loss given default has increased in popularity as it has become a crucial parameter for establishing capital buffers under Basel II and III and for calculating the impairment of financial assets under the International Financial Reporting Standard 9. The most recent literature on this topic focuses mainly on estimation methods and less on the variables used to explain the variability in loss given default. In this paper, we expand this part of the modeling process by constructing a set of client-behavior-based predictors that can be used to construct more precise models, and we investigate the economic justifications empirically to examine their potential usage. The main novelty introduced in this paper is the connection between loss given default and the behavior of the contract owner, not just the contract itself. This approach results in the reduction of the values of selected error measures and progressively improves the forecasting ability. The effect is more visible in a parametric method (fractional regression) than in a nonparametric method (regression tree). Our findings support incorporating client-oriented information into loss given default models.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42382603","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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