{"title":"可观测和马尔可夫切换分数驱动模型的区间波动动态","authors":"Szabolcs Blazsek, Dejun Kong, Samantha R. Shadoff","doi":"10.1016/j.frl.2024.106631","DOIUrl":null,"url":null,"abstract":"We study the novel Markov-switching (MS) Beta-<mml:math altimg=\"si414.svg\" display=\"inline\"><mml:mi>t</mml:mi></mml:math>-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, using within-regime volatility dynamics, similar to the recent observable-switching (OS) Beta-<mml:math altimg=\"si414.svg\" display=\"inline\"><mml:mi>t</mml:mi></mml:math>-EGARCH model. We report in-sample results on the Standard & Poor’s 500 (S&P 500) and a random sample of 50 firms from the S&P 500 from March 1986 to July 2024. We compare the out-of-sample forecasting performances of OS-Beta-<mml:math altimg=\"si414.svg\" display=\"inline\"><mml:mi>t</mml:mi></mml:math>-EGARCH and MS-Beta-<mml:math altimg=\"si414.svg\" display=\"inline\"><mml:mi>t</mml:mi></mml:math>-EGARCH from May 2005 to July 2024 and confirm that OS-Beta-<mml:math altimg=\"si414.svg\" display=\"inline\"><mml:mi>t</mml:mi></mml:math>-EGARCH is superior to MS-Beta-<mml:math altimg=\"si414.svg\" display=\"inline\"><mml:mi>t</mml:mi></mml:math>-EGARCH.","PeriodicalId":12167,"journal":{"name":"Finance Research Letters","volume":"81 1","pages":""},"PeriodicalIF":7.4000,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Within-regime volatility dynamics for observable- and Markov-switching score-driven models\",\"authors\":\"Szabolcs Blazsek, Dejun Kong, Samantha R. Shadoff\",\"doi\":\"10.1016/j.frl.2024.106631\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We study the novel Markov-switching (MS) Beta-<mml:math altimg=\\\"si414.svg\\\" display=\\\"inline\\\"><mml:mi>t</mml:mi></mml:math>-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, using within-regime volatility dynamics, similar to the recent observable-switching (OS) Beta-<mml:math altimg=\\\"si414.svg\\\" display=\\\"inline\\\"><mml:mi>t</mml:mi></mml:math>-EGARCH model. We report in-sample results on the Standard & Poor’s 500 (S&P 500) and a random sample of 50 firms from the S&P 500 from March 1986 to July 2024. We compare the out-of-sample forecasting performances of OS-Beta-<mml:math altimg=\\\"si414.svg\\\" display=\\\"inline\\\"><mml:mi>t</mml:mi></mml:math>-EGARCH and MS-Beta-<mml:math altimg=\\\"si414.svg\\\" display=\\\"inline\\\"><mml:mi>t</mml:mi></mml:math>-EGARCH from May 2005 to July 2024 and confirm that OS-Beta-<mml:math altimg=\\\"si414.svg\\\" display=\\\"inline\\\"><mml:mi>t</mml:mi></mml:math>-EGARCH is superior to MS-Beta-<mml:math altimg=\\\"si414.svg\\\" display=\\\"inline\\\"><mml:mi>t</mml:mi></mml:math>-EGARCH.\",\"PeriodicalId\":12167,\"journal\":{\"name\":\"Finance Research Letters\",\"volume\":\"81 1\",\"pages\":\"\"},\"PeriodicalIF\":7.4000,\"publicationDate\":\"2024-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Finance Research Letters\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1016/j.frl.2024.106631\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Finance Research Letters","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1016/j.frl.2024.106631","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Within-regime volatility dynamics for observable- and Markov-switching score-driven models
We study the novel Markov-switching (MS) Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, using within-regime volatility dynamics, similar to the recent observable-switching (OS) Beta-t-EGARCH model. We report in-sample results on the Standard & Poor’s 500 (S&P 500) and a random sample of 50 firms from the S&P 500 from March 1986 to July 2024. We compare the out-of-sample forecasting performances of OS-Beta-t-EGARCH and MS-Beta-t-EGARCH from May 2005 to July 2024 and confirm that OS-Beta-t-EGARCH is superior to MS-Beta-t-EGARCH.
期刊介绍:
Finance Research Letters welcomes submissions across all areas of finance, aiming for rapid publication of significant new findings. The journal particularly encourages papers that provide insight into the replicability of established results, examine the cross-national applicability of previous findings, challenge existing methodologies, or demonstrate methodological contingencies.
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