{"title":"Internet financial risk assessment in China based on a particle swarm optimization–analytic hierarchy process and fuzzy comprehensive evaluation","authors":"Zeng Li, Wee‐Yeap Lau, Elya Nabila Abdul Bahri","doi":"10.21314/jrmv.2022.028","DOIUrl":"https://doi.org/10.21314/jrmv.2022.028","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67721028","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 validation of different systemic risk measurement models","authors":"Hu Wang, Shuyang Jiang","doi":"10.21314/jrmv.2023.004","DOIUrl":"https://doi.org/10.21314/jrmv.2023.004","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67721087","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 present a new automated validation tool to validate predictive models for financial organizations based on the regulatory guidance of the US Federal Reserve and the Office of the Comptroller of the Currency. This automated tool is designed to help validate linear and logistic regression models. It automatically completes validation processes for seven areas: data sets, model algorithm assumptions, model coefficients and performance, model stability, backtesting, sensitivity testing and stress testing. The tool is packaged as a PYTHON library and can validate models developed in any language, such as PYTHON, R and the SAS language. Further, it can automatically generate a validation report as a portable document format (PDF) file while saving all the generated tables and charts in separate EXCEL and portable network graphic (PNG) files. With this automated tool, validators can standardize model validation procedures, improve efficiency and reduce human error. The tool can also be used during model development.
{"title":"A new automated model validation tool for financial institutions","authors":"Lingling Fan, Alex Schneider, Mazin Joumaa","doi":"10.21314/jrmv.2023.006","DOIUrl":"https://doi.org/10.21314/jrmv.2023.006","url":null,"abstract":"We present a new automated validation tool to validate predictive models for financial organizations based on the regulatory guidance of the US Federal Reserve and the Office of the Comptroller of the Currency. This automated tool is designed to help validate linear and logistic regression models. It automatically completes validation processes for seven areas: data sets, model algorithm assumptions, model coefficients and performance, model stability, backtesting, sensitivity testing and stress testing. The tool is packaged as a PYTHON library and can validate models developed in any language, such as PYTHON, R and the SAS language. Further, it can automatically generate a validation report as a portable document format (PDF) file while saving all the generated tables and charts in separate EXCEL and portable network graphic (PNG) files. With this automated tool, validators can standardize model validation procedures, improve efficiency and reduce human error. The tool can also be used during model development.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135361457","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}
Suppose the same set of assets enters a primary market in either a monotone decreasing or monotone increasing sequence of asset risk. This study shows that the errors that attend the valuations of those assets in the context of the two different orderings are noncoincident. The sequence in which new assets arrive within a market is thus a source of valuation uncertainty risk. The formal theory shows that both asset risk and valuation uncertainty risk are mitigated if investors condition valuations of new assets on a dynamically evolving intertemporal mechanism that has parameterization as an explicit robust measure for the “cumulative state of [market] incompleteness” (CSI). Theoretically, relative to every preceding state, the CSI is a sufficient measure for the severity of market-conditioned valuation uncertainty risk. Although the derivation of a specific measure for the CSI is beyond the scope of this study, the formal theory arrives at three mathematically specified risk metrics that approximate the properties of the CSI. Let q and M denote, respectively, the individual initial public offering quality and the CSI. The CSI has the explicit parameterization Mt = ⋃ts=1(qs | Ms-1), as is expected of any well-defined measure, is self-propagating.
{"title":"On the mitigation of valuation uncertainty risk: the importance of a robust proxy for the “cumulative state of market incompleteness”","authors":"Oghenovo Adewale Obrimah","doi":"10.21314/jrmv.2023.007","DOIUrl":"https://doi.org/10.21314/jrmv.2023.007","url":null,"abstract":"Suppose the same set of assets enters a primary market in either a monotone decreasing or monotone increasing sequence of asset risk. This study shows that the errors that attend the valuations of those assets in the context of the two different orderings are noncoincident. The sequence in which new assets arrive within a market is thus a source of valuation uncertainty risk. The formal theory shows that both asset risk and valuation uncertainty risk are mitigated if investors condition valuations of new assets on a dynamically evolving intertemporal mechanism that has parameterization as an explicit robust measure for the “cumulative state of [market] incompleteness” (CSI). Theoretically, relative to every preceding state, the CSI is a sufficient measure for the severity of market-conditioned valuation uncertainty risk. Although the derivation of a specific measure for the CSI is beyond the scope of this study, the formal theory arrives at three mathematically specified risk metrics that approximate the properties of the CSI. Let q and M denote, respectively, the individual initial public offering quality and the CSI. The CSI has the explicit parameterization Mt = ⋃ts=1(qs | Ms-1), as is expected of any well-defined measure, is self-propagating.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784017","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 strategies contractors can employ to mitigate the exchange rate risks in hybrid payment systems. In our analysis, contractors face exchange rate risk, due to mismatches between their revenue and cost currencies, as well as property price risk, since they receive a portion of their revenue in the form of dwelling units. We rigorously compare the performance of three distinct risk models within the context of real estate development in Punta del Este, Uruguay. By evaluating these models against empirical data from a hypothetical project, our research provides valuable insights into their effectiveness in managing exchange rate risk. This addresses the critical need to validate risk models in the emerging real estate market of Punta del Este. Our analysis demonstrates a significant reduction in risk and higher expected profits compared with strategies that do not involve hedging.
{"title":"Exchange rate risk management for contractors within a hybrid payment scheme: a case study in Punta del Este, Uruguay","authors":"Martin Egozcue","doi":"10.21314/jrmv.2023.011","DOIUrl":"https://doi.org/10.21314/jrmv.2023.011","url":null,"abstract":"This paper investigates the strategies contractors can employ to mitigate the exchange rate risks in hybrid payment systems. In our analysis, contractors face exchange rate risk, due to mismatches between their revenue and cost currencies, as well as property price risk, since they receive a portion of their revenue in the form of dwelling units. We rigorously compare the performance of three distinct risk models within the context of real estate development in Punta del Este, Uruguay. By evaluating these models against empirical data from a hypothetical project, our research provides valuable insights into their effectiveness in managing exchange rate risk. This addresses the critical need to validate risk models in the emerging real estate market of Punta del Este. Our analysis demonstrates a significant reduction in risk and higher expected profits compared with strategies that do not involve hedging.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135506463","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":"https://www.risk.net/journal-of-risk-model-validation/7956071/measuring-the-systemic-importance-of-chinese-banks-a-comparison-of-different-risk-measurement-models","authors":"C. Cai","doi":"10.21314/jrmv.2022.029","DOIUrl":"https://doi.org/10.21314/jrmv.2022.029","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67721113","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 modified hybrid feature-selection method based on a filter and wrapper approach for credit risk forecasting","authors":"Guotai Chi, Mohamed Abdelaziz Mandour","doi":"10.21314/jrmv.2023.001","DOIUrl":"https://doi.org/10.21314/jrmv.2023.001","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67720908","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":"What can we expect from a good margin model? Observations from whole-distribution tests of risk-based initial margin models","authors":"David Murphy","doi":"10.21314/jrmv.2023.002","DOIUrl":"https://doi.org/10.21314/jrmv.2023.002","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"54 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67720991","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 measure the out-of-sample performance of sample-based rolling-window neural network (NN) portfolio optimization strategies. We show that if NN strategies are evaluated using the holdout (train–test split) technique, then high out-of-sample performance scores can commonly be achieved. Although this phenomenon is often employed to validate NN portfolio models, we demonstrate that it constitutes a “fata morgana” that arises due to a particular vulnerability of portfolio optimization to overfitting. To assess whether overfitting is present, we set up a dedicated methodology based on combinatorially symmetric cross-validation that involves performance measurement across different holdout periods and varying portfolio compositions (the random-asset-stabilized combinatorially symmetric cross-validation methodology). We compare a variety of NN strategies with classical extensions of the mean–variance model and the 1 / N strategy. We find that it is by no means trivial to outperform the classical models. While certain NN strategies outperform the 1 / N benchmark, of the almost 30 models that we evaluate explicitly, none is consistently better than the short-sale constrained minimum-variance rule in terms of the Sharpe ratio or the certainty equivalent of returns.
{"title":"Overfitting in portfolio optimization","authors":"Matteo Maggiolo, Oleg Szehr","doi":"10.21314/jrmv.2023.005","DOIUrl":"https://doi.org/10.21314/jrmv.2023.005","url":null,"abstract":"In this paper we measure the out-of-sample performance of sample-based rolling-window neural network (NN) portfolio optimization strategies. We show that if NN strategies are evaluated using the holdout (train–test split) technique, then high out-of-sample performance scores can commonly be achieved. Although this phenomenon is often employed to validate NN portfolio models, we demonstrate that it constitutes a “fata morgana” that arises due to a particular vulnerability of portfolio optimization to overfitting. To assess whether overfitting is present, we set up a dedicated methodology based on combinatorially symmetric cross-validation that involves performance measurement across different holdout periods and varying portfolio compositions (the random-asset-stabilized combinatorially symmetric cross-validation methodology). We compare a variety of NN strategies with classical extensions of the mean–variance model and the 1 / N strategy. We find that it is by no means trivial to outperform the classical models. While certain NN strategies outperform the 1 / N benchmark, of the almost 30 models that we evaluate explicitly, none is consistently better than the short-sale constrained minimum-variance rule in terms of the Sharpe ratio or the certainty equivalent of returns.","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989272","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 and the global financial crisis","authors":"Manh Ha Tran, Ngoc Thanh Mai Tran","doi":"10.21314/jrmv.2022.030","DOIUrl":"https://doi.org/10.21314/jrmv.2022.030","url":null,"abstract":"","PeriodicalId":43447,"journal":{"name":"Journal of Risk Model Validation","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67720746","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：