{"title":"预测具有指数-多项式尾部的倾斜金融收益的尾部风险","authors":"Albert Antwi, Emmanuel N. Gyamfi, Anokye M. Adam","doi":"10.1002/for.3154","DOIUrl":null,"url":null,"abstract":"<p>Aggregated long and short trading risk positions of speculative assets over time are likely to be unequal. This may be because of irrational decisions of traders and investors as well as catastrophic events that lead to pronounce or salient market crashes. Returns of such assets are therefore more likely to have one polynomial tail and one exponential tail. The generalized hyperbolic (GH) skewed Student-<i>t</i> distribution is known to handle such situations quite well. In this paper, we use generalized autoregressive conditional heteroscedasticity (GARCH) models to empirically show the superiority of the GH skewed Student-<i>t</i> distribution in forecasting the extreme tail risks of cryptocurrency returns in the presence of substantial skewness in comparison with some competing distributions. Furthermore, we show the practical significance of the GH skewed Student-<i>t</i> distribution-based risk forecasts in computing daily capital requirements. Evidence from the study suggests that the GH skewed Student-<i>t</i> distribution model tends to be superior in forecasting volatility and expected shortfall (ES) but not value-at-risk. In addition, the distribution yields higher value-at-risk (VaR) exceptions but surprisingly avoids the red zone of the Basel II accord penalty zones and produces lower but optimal daily capital requirements. Therefore, in the presence of substantially skewed returns having exponential-polynomial tails, we recommend the use of the GH skewed Student-<i>t</i> distribution for parametric GARCH models in forecasting extreme tail risk.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2731-2748"},"PeriodicalIF":3.4000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3154","citationCount":"0","resultStr":"{\"title\":\"Forecasting tail risk of skewed financial returns having exponential-polynomial tails\",\"authors\":\"Albert Antwi, Emmanuel N. Gyamfi, Anokye M. Adam\",\"doi\":\"10.1002/for.3154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Aggregated long and short trading risk positions of speculative assets over time are likely to be unequal. This may be because of irrational decisions of traders and investors as well as catastrophic events that lead to pronounce or salient market crashes. Returns of such assets are therefore more likely to have one polynomial tail and one exponential tail. The generalized hyperbolic (GH) skewed Student-<i>t</i> distribution is known to handle such situations quite well. In this paper, we use generalized autoregressive conditional heteroscedasticity (GARCH) models to empirically show the superiority of the GH skewed Student-<i>t</i> distribution in forecasting the extreme tail risks of cryptocurrency returns in the presence of substantial skewness in comparison with some competing distributions. Furthermore, we show the practical significance of the GH skewed Student-<i>t</i> distribution-based risk forecasts in computing daily capital requirements. Evidence from the study suggests that the GH skewed Student-<i>t</i> distribution model tends to be superior in forecasting volatility and expected shortfall (ES) but not value-at-risk. In addition, the distribution yields higher value-at-risk (VaR) exceptions but surprisingly avoids the red zone of the Basel II accord penalty zones and produces lower but optimal daily capital requirements. Therefore, in the presence of substantially skewed returns having exponential-polynomial tails, we recommend the use of the GH skewed Student-<i>t</i> distribution for parametric GARCH models in forecasting extreme tail risk.</p>\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":\"43 7\",\"pages\":\"2731-2748\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3154\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/for.3154\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/for.3154","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Forecasting tail risk of skewed financial returns having exponential-polynomial tails
Aggregated long and short trading risk positions of speculative assets over time are likely to be unequal. This may be because of irrational decisions of traders and investors as well as catastrophic events that lead to pronounce or salient market crashes. Returns of such assets are therefore more likely to have one polynomial tail and one exponential tail. The generalized hyperbolic (GH) skewed Student-t distribution is known to handle such situations quite well. In this paper, we use generalized autoregressive conditional heteroscedasticity (GARCH) models to empirically show the superiority of the GH skewed Student-t distribution in forecasting the extreme tail risks of cryptocurrency returns in the presence of substantial skewness in comparison with some competing distributions. Furthermore, we show the practical significance of the GH skewed Student-t distribution-based risk forecasts in computing daily capital requirements. Evidence from the study suggests that the GH skewed Student-t distribution model tends to be superior in forecasting volatility and expected shortfall (ES) but not value-at-risk. In addition, the distribution yields higher value-at-risk (VaR) exceptions but surprisingly avoids the red zone of the Basel II accord penalty zones and produces lower but optimal daily capital requirements. Therefore, in the presence of substantially skewed returns having exponential-polynomial tails, we recommend the use of the GH skewed Student-t distribution for parametric GARCH models in forecasting extreme tail risk.
期刊介绍:
The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.