{"title":"Modeling maxima with a regime-switching Fréchet model","authors":"Keqi Tan, Yu Chen, Pengzhan Chen","doi":"10.21314/jor.2022.045","DOIUrl":"https://doi.org/10.21314/jor.2022.045","url":null,"abstract":"","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67719330","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}
Borger Melsom, Christian Bakke Vennerød, P. D. de Lange, Lars Ole Hjelkrem, Sjur Westgaard
{"title":"Explainable artificial intelligence for credit scoring in banking","authors":"Borger Melsom, Christian Bakke Vennerød, P. D. de Lange, Lars Ole Hjelkrem, Sjur Westgaard","doi":"10.21314/jor.2022.046","DOIUrl":"https://doi.org/10.21314/jor.2022.046","url":null,"abstract":"","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67719368","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 traditional risk-based margin models are risk sensitive but can be procyclical, especially under stressed market conditions. The issue of procyclicality has returned to the forefront of policy discussions due to the significant increases in margins because of market turmoil related to the Covid-19 pandemic. In this paper, we revisit the procyclicality issue in risk-based margin models. Most of the existing procyclicality mitigations focus on imposing a buffer or floor on the initial margin to avoid inadequately low margins during quiet periods. However, a more efficient anti-procyclicality mechanism should be able to provide relatively stable and adequate margins across different market conditions in a dynamic way, especially during stress periods. To address this issue, we develop a simple technique that explicitly provides a smooth transition of the key risk drivers in risk-based margin models across different market conditions. Specifically, we use a dynamic scaling factor to control procyclicality. This dynamic scaling factor scales up the key risk drivers during quiet periods to avoid inadequately low risk coverage and tempers down their elevated levels during stress periods. Finally, we show that the technique can provide an efficient control to mitigate procyclicality in risk-based margin models using simple illustrations.
{"title":"Procyclicality Control in Risk-based Margin Models","authors":"Lauren W. Wong, Yang Zhang","doi":"10.21314/jor.2021.010","DOIUrl":"https://doi.org/10.21314/jor.2021.010","url":null,"abstract":"The traditional risk-based margin models are risk sensitive but can be procyclical, especially under stressed market conditions. The issue of procyclicality has returned to the forefront of policy discussions due to the significant increases in margins because of market turmoil related to the Covid-19 pandemic. In this paper, we revisit the procyclicality issue in risk-based margin models. Most of the existing procyclicality mitigations focus on imposing a buffer or floor on the initial margin to avoid inadequately low margins during quiet periods. However, a more efficient anti-procyclicality mechanism should be able to provide relatively stable and adequate margins across different market conditions in a dynamic way, especially during stress periods. To address this issue, we develop a simple technique that explicitly provides a smooth transition of the key risk drivers in risk-based margin models across different market conditions. Specifically, we use a dynamic scaling factor to control procyclicality. This dynamic scaling factor scales up the key risk drivers during quiet periods to avoid inadequately low risk coverage and tempers down their elevated levels during stress periods. Finally, we show that the technique can provide an efficient control to mitigate procyclicality in risk-based margin models using simple illustrations.","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49310897","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 review of the foreign exchange base currency approach under the standardized approach of the Fundamental Review of the Trading Book and issues related to the pegged reporting currency","authors":"T. Yu","doi":"10.21314/JOR.2020.450","DOIUrl":"https://doi.org/10.21314/JOR.2020.450","url":null,"abstract":"","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44985035","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":"Body and tail: an automated tail-detecting procedure","authors":"Ingo Hoffmann, Christoph J. Börner","doi":"10.21314/JOR.2020.447","DOIUrl":"https://doi.org/10.21314/JOR.2020.447","url":null,"abstract":"","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42321050","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":"Standard errors of risk and performance estimators for serially dependent returns","authors":"Xin Chen, R. Martin","doi":"10.21314/JOR.2020.446","DOIUrl":"https://doi.org/10.21314/JOR.2020.446","url":null,"abstract":"","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42159563","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":"Performance measures adjusted for the risk situation (PARS)","authors":"Christoph Peters,Roland Seydel","doi":"10.21314/jor.2021.007","DOIUrl":"https://doi.org/10.21314/jor.2021.007","url":null,"abstract":"","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":"6 2","pages":""},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138494942","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":"Fund size and the stability of portfolio risk","authors":"Martin Ewen, M. Rieger","doi":"10.21314/jor.2020.439","DOIUrl":"https://doi.org/10.21314/jor.2020.439","url":null,"abstract":"","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2020-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46662630","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}
To capture the "long-memory" effect in volatility, a multiplicative component conditional autoregressive range (MCCARR) model is proposed. We show theoretically that the MCCARR model can capture the long-memory effect well. An empirical study is performed on the Standard & Poor's 500 index, and the results show that the MCCARR model outperforms both conditional autoregressive range and heterogeneous autoregressive models for in-sample and out-of-sample volatility forecasting.
{"title":"Range-based Volatility Forecasting: A Multiplicative Component Conditional Autoregressive Range Model","authors":"Haibin Xie","doi":"10.21314/jor.2020.433","DOIUrl":"https://doi.org/10.21314/jor.2020.433","url":null,"abstract":"To capture the \"long-memory\" effect in volatility, a multiplicative component conditional autoregressive range (MCCARR) model is proposed. We show theoretically that the MCCARR model can capture the long-memory effect well. An empirical study is performed on the Standard & Poor's 500 index, and the results show that the MCCARR model outperforms both conditional autoregressive range and heterogeneous autoregressive models for in-sample and out-of-sample volatility forecasting.","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2020-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46809866","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}
Previous studies provide evidence that trade related uncertainty tends to predict an increase in Bitcoin returns. In this paper, we extend the related literature by examining whether the information on the U.S. – China trade war can be used to forecast the future path of Bitcoin returns controlling for various explanatory variables. We apply ordinary least square (OLS) regression, support vector regression (SVR), and the least absolute shrinkage and selection operator (LASSO) techniques that stem from the field of machine learning, and find weak evidence of the role of the trade war in forecasting Bitcoin returns. Given that out-of-sample tests are more reliable than in-sample tests, our results tend to suggest that future Bitcoin returns are unaffected by trade related uncertainties, and investors can use Bitcoin as a safe haven in this context.
{"title":"Forecasting Bitcoin Returns: Is There a Role for the US–China Trade War?","authors":"Vasilios Plakandaras, Elie Bouri, Rangan Gupta","doi":"10.21314/JOR.2021.001","DOIUrl":"https://doi.org/10.21314/JOR.2021.001","url":null,"abstract":"Previous studies provide evidence that trade related uncertainty tends to predict an increase in Bitcoin returns. In this paper, we extend the related literature by examining whether the information on the U.S. – China trade war can be used to forecast the future path of Bitcoin returns controlling for various explanatory variables. We apply ordinary least square (OLS) regression, support vector regression (SVR), and the least absolute shrinkage and selection operator (LASSO) techniques that stem from the field of machine learning, and find weak evidence of the role of the trade war in forecasting Bitcoin returns. Given that out-of-sample tests are more reliable than in-sample tests, our results tend to suggest that future Bitcoin returns are unaffected by trade related uncertainties, and investors can use Bitcoin as a safe haven in this context.","PeriodicalId":46697,"journal":{"name":"Journal of Risk","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2020-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42510063","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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