预测具有指数-多项式尾部的倾斜金融收益的尾部风险

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-05-15 DOI:10.1002/for.3154
Albert Antwi, Emmanuel N. Gyamfi, Anokye M. Adam
{"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,&nbsp;Emmanuel N. Gyamfi,&nbsp;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,&nbsp;Emmanuel N. Gyamfi,&nbsp;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}
引用次数: 0

摘要

随着时间的推移,投机资产的多头和空头总交易风险头寸很可能是不平等的。这可能是由于交易者和投资者的非理性决策,以及导致市场崩盘或显著崩盘的灾难性事件。因此,这类资产的收益更有可能出现一个多项式尾部和一个指数尾部。众所周知,广义双曲(GH)偏斜 Student-t 分布能很好地处理这种情况。在本文中,我们使用广义自回归条件异方差(GARCH)模型,通过实证研究表明,与一些竞争性分布相比,GH 偏态 Student-t 分布在预测加密货币回报率的极端尾部风险方面更具优势,因为它存在很大的偏度。此外,我们还展示了基于 GH 偏态 Student-t 分布的风险预测在计算每日资本要求方面的实际意义。研究证据表明,GH 偏态 Student-t 分布模型在预测波动率和预期缺口 (ES) 方面往往更胜一筹,但在预测风险价值方面却并非如此。此外,该分布产生了更高的风险价值(VaR)异常,但却出人意料地避开了《巴塞尔 II 新资本协议》惩罚区的红色区域,并产生了更低但却是最佳的每日资本要求。因此,在存在具有指数-多项式尾部的大幅倾斜回报的情况下,我们建议在预测极端尾部风险时使用参数 GARCH 模型的 GH 倾斜 Student-t 分布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
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.
期刊最新文献
Issue Information Issue Information Predictor Preselection for Mixed‐Frequency Dynamic Factor Models: A Simulation Study With an Empirical Application to GDP Nowcasting Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning Demand Forecasting New Fashion Products: A Review Paper
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1