{"title":"股票市场的动态依赖性和套期保值:时变 Copula 与非对称马尔可夫模型的证据","authors":"Jia Wang;MengChu Zhou;Xiwang Guo;Xu Wang;Yusuf Al-Turki","doi":"10.1109/TCSS.2023.3346439","DOIUrl":null,"url":null,"abstract":"To study the asymmetric jump behaviors of the stock markets, we propose a novel autoregressive conditional jump intensity (ARJI)—generalized autoregressive conditional heteroskedasticity (GARCH) model with a Markov chain. Compared with the existing models, it considers the asymmetric effects of the positive and negative shocks on jump volatilities. It is proposed to estimate the asymmetric jump volatilities of the stock markets in mainland China and Hong Kong under different volatility regimes. Multiple time-varying copula models are used to analyze the dynamic dependences of the jump risks between the two markets. Furthermore, we construct dynamic hedging portfolios for their spot and futures markets, estimate the minimum risk hedging ratios, and measure the hedging performance. Compared with other benchmark models, the results show that the proposed one has the best fitting effect for the Chinese stock markets. The correlations between the Chinese mainland and Hong Kong markets are always positive. When constructing hedging portfolios, the proposed model is superior to other models, which means that introducing asymmetric shocks on both normal and jump volatilities into a Markovian ARJI-GARCH model can effectively improve the performance of hedging portfolios. In addition, the results of the robustness test indicates that our proposed model performs well and is robust.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic Dependence and Hedging of Stock Markets: Evidence From Time-Varying Copula With Asymmetric Markovian Models\",\"authors\":\"Jia Wang;MengChu Zhou;Xiwang Guo;Xu Wang;Yusuf Al-Turki\",\"doi\":\"10.1109/TCSS.2023.3346439\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To study the asymmetric jump behaviors of the stock markets, we propose a novel autoregressive conditional jump intensity (ARJI)—generalized autoregressive conditional heteroskedasticity (GARCH) model with a Markov chain. Compared with the existing models, it considers the asymmetric effects of the positive and negative shocks on jump volatilities. It is proposed to estimate the asymmetric jump volatilities of the stock markets in mainland China and Hong Kong under different volatility regimes. Multiple time-varying copula models are used to analyze the dynamic dependences of the jump risks between the two markets. Furthermore, we construct dynamic hedging portfolios for their spot and futures markets, estimate the minimum risk hedging ratios, and measure the hedging performance. Compared with other benchmark models, the results show that the proposed one has the best fitting effect for the Chinese stock markets. The correlations between the Chinese mainland and Hong Kong markets are always positive. When constructing hedging portfolios, the proposed model is superior to other models, which means that introducing asymmetric shocks on both normal and jump volatilities into a Markovian ARJI-GARCH model can effectively improve the performance of hedging portfolios. In addition, the results of the robustness test indicates that our proposed model performs well and is robust.\",\"PeriodicalId\":13044,\"journal\":{\"name\":\"IEEE Transactions on Computational Social Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-02-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Computational Social Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10422863/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10422863/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
Dynamic Dependence and Hedging of Stock Markets: Evidence From Time-Varying Copula With Asymmetric Markovian Models
To study the asymmetric jump behaviors of the stock markets, we propose a novel autoregressive conditional jump intensity (ARJI)—generalized autoregressive conditional heteroskedasticity (GARCH) model with a Markov chain. Compared with the existing models, it considers the asymmetric effects of the positive and negative shocks on jump volatilities. It is proposed to estimate the asymmetric jump volatilities of the stock markets in mainland China and Hong Kong under different volatility regimes. Multiple time-varying copula models are used to analyze the dynamic dependences of the jump risks between the two markets. Furthermore, we construct dynamic hedging portfolios for their spot and futures markets, estimate the minimum risk hedging ratios, and measure the hedging performance. Compared with other benchmark models, the results show that the proposed one has the best fitting effect for the Chinese stock markets. The correlations between the Chinese mainland and Hong Kong markets are always positive. When constructing hedging portfolios, the proposed model is superior to other models, which means that introducing asymmetric shocks on both normal and jump volatilities into a Markovian ARJI-GARCH model can effectively improve the performance of hedging portfolios. In addition, the results of the robustness test indicates that our proposed model performs well and is robust.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.