{"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}
引用次数: 0
Abstract
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.
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