Markov switching beta-skewed-t EGARCH
© Springer Nature Switzerland AG 2019. This study extends the work of Harvey and Sucarrat [15] and present Markov regime-switching (MS) Beta-skewed-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model to predict the volatility. To examine the performance of our mode...
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th-cmuir.6653943832-655422019-08-05T04:39:40Z Markov switching beta-skewed-t EGARCH Woraphon Yamaka Paravee Maneejuk Songsak Sriboonchitta Computer Science Mathematics © Springer Nature Switzerland AG 2019. This study extends the work of Harvey and Sucarrat [15] and present Markov regime-switching (MS) Beta-skewed-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model to predict the volatility. To examine the performance of our model, in-sample point forecast precision and AIC and BIC weights are conducted. We study the volatility of five Exchange Traded Fund returns for period from January 2012 to October 2018. Our proposed model is not found to outperform all the other models. However, the dominance of MS-Beta-skewed-t-EGARCH for SPY, VGT, and AGG may support the application of the MS-Beta-skewed-t-EGARCH model for some financial data series. 2019-08-05T04:35:07Z 2019-08-05T04:35:07Z 2019-01-01 Book Series 16113349 03029743 2-s2.0-85064196834 10.1007/978-3-030-14815-7_16 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85064196834&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/65542 |
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Computer Science Mathematics |
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Computer Science Mathematics Woraphon Yamaka Paravee Maneejuk Songsak Sriboonchitta Markov switching beta-skewed-t EGARCH |
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© Springer Nature Switzerland AG 2019. This study extends the work of Harvey and Sucarrat [15] and present Markov regime-switching (MS) Beta-skewed-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model to predict the volatility. To examine the performance of our model, in-sample point forecast precision and AIC and BIC weights are conducted. We study the volatility of five Exchange Traded Fund returns for period from January 2012 to October 2018. Our proposed model is not found to outperform all the other models. However, the dominance of MS-Beta-skewed-t-EGARCH for SPY, VGT, and AGG may support the application of the MS-Beta-skewed-t-EGARCH model for some financial data series. |
| format |
Book Series |
| author |
Woraphon Yamaka Paravee Maneejuk Songsak Sriboonchitta |
| author_facet |
Woraphon Yamaka Paravee Maneejuk Songsak Sriboonchitta |
| author_sort |
Woraphon Yamaka |
| title |
Markov switching beta-skewed-t EGARCH |
| title_short |
Markov switching beta-skewed-t EGARCH |
| title_full |
Markov switching beta-skewed-t EGARCH |
| title_fullStr |
Markov switching beta-skewed-t EGARCH |
| title_full_unstemmed |
Markov switching beta-skewed-t EGARCH |
| title_sort |
markov switching beta-skewed-t egarch |
| publishDate |
2019 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85064196834&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/65542 |
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