{"title":"利用极值理论和共轭方法调整流动性风险价值","authors":"Harish Kamal, Samit Paul","doi":"10.1002/for.3105","DOIUrl":null,"url":null,"abstract":"<p>In this study, we propose the application of the GARCH-EVT-Copula model in estimating liquidity-adjusted value-at-risk (L-VaR) of energy stocks while modeling nonlinear dependence between return and bid-ask spread. Using the L-VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non-zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student-<i>t</i>. Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH-EVT-Clayton copula is superior and most consistent in forecasting L-VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Liquidity-adjusted value-at-risk using extreme value theory and copula approach\",\"authors\":\"Harish Kamal, Samit Paul\",\"doi\":\"10.1002/for.3105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In this study, we propose the application of the GARCH-EVT-Copula model in estimating liquidity-adjusted value-at-risk (L-VaR) of energy stocks while modeling nonlinear dependence between return and bid-ask spread. Using the L-VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non-zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student-<i>t</i>. Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH-EVT-Clayton copula is superior and most consistent in forecasting L-VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation.</p>\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/for.3105\",\"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.3105","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Liquidity-adjusted value-at-risk using extreme value theory and copula approach
In this study, we propose the application of the GARCH-EVT-Copula model in estimating liquidity-adjusted value-at-risk (L-VaR) of energy stocks while modeling nonlinear dependence between return and bid-ask spread. Using the L-VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non-zero skewness, excess kurtosis, and volatility clustering of both return and spread distributions of energy stocks. Moreover, to measure the nonlinear dependence between return and spread series, we use multiple copulas: Clayton, Gumbel, Frank, Normal, and Student-t. Based on the statistical backtesting and economic loss functions, our results suggest that the GARCH-EVT-Clayton copula is superior and most consistent in forecasting L-VaR compared with other competing models. This finding has several implications for investors, market makers, and daily traders who appreciate the importance of liquidity in market risk computation.
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.