Markov Switching Beta-skewed-t EGARCH
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 a...
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| Published in | Integrated Uncertainty in Knowledge Modelling and Decision Making Vol. 11471; pp. 184 - 196 |
|---|---|
| Main Authors | , , |
| Format | Book Chapter |
| Language | English |
| Published |
Switzerland
Springer International Publishing AG
2019
Springer International Publishing |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 3030148149 9783030148140 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-030-14815-7_16 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Yamaka, Woraphon Sriboonchitta, Songsak Maneejuk, Paravee |
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| Copyright | Springer Nature Switzerland AG 2019 |
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| DOI | 10.1007/978-3-030-14815-7_16 |
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| Editor | Seki, Hirosato Huynh, Van-Nam Inuiguchi, Masahiro Nguyen, Canh Hao |
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| Snippet | This study extends the work of Harvey and Sucarrat [15] and present Markov regime-switching (MS) Beta-skewed-t-EGARCH (exponential generalized autoregressive... |
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| SubjectTerms | AIC and BIC weights Beta-skewed-t-EGARCH Markov Switching Volatility forecasting |
| Title | Markov Switching Beta-skewed-t EGARCH |
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