Markov Switching Beta-skewed-t EGARCH

• Published in: Integrated Uncertainty in Knowledge Modelling and Decision Making Vol. 11471; pp. 184 - 196
• Main Authors: Yamaka, Woraphon, Maneejuk, Paravee, Sriboonchitta, Songsak
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: AIC and BIC weights; Beta-skewed-t-EGARCH; Markov Switching; Volatility forecasting
• ISBN: 3030148149; 9783030148140
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-14815-7_16

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.