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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andersen, T.G., Bollerslev, T.: Intraday periodicity and volatility persistence in financial markets. J. Empir. Financ. 4(2–3), 115–158 (1997)
Bauwens, L., Laurent, S., Rombouts, J.V.: Multivariate GARCH models: a survey. J. Appl. Econ. 21(1), 79–109 (2006)
Black, F., Scholes, M.: The pricing of options and corporate liabilities. J. Polit. Econ. 81(3), 637–654 (1973)
Blazsek, S., Downarowicz, A.: Regime switching models of hedge fund returns. Working Papers (Universidad de Navarra. Facultad de Ciencias Económicas y Empresariales) (12), 1 (2008)
Blazsek, S., Villatoro, M.: Is Beta-t-EGARCH (1, 1) superior to GARCH (1, 1)? Appl. Econ. 47(17), 1764–1774 (2015)
Blazsek, S., Ho, H.C.: Markov regime-switching Beta-t-EGARCH. Appl. Econ. 49(47), 4793–4805 (2017)
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econ. 31(3), 307–327 (1986)
Chodchuangnirun, B., Zhu, K., Yamaka, W.: Pairs trading via nonlinear autoregressive GARCH models. In: Huynh, V.-N., Inuiguchi, M., Tran, D.H., Denoeux, T. (eds.) IUKM 2018. LNCS, vol. 10758, pp. 276–288. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75429-1_23
Davidson, J.: Moment and memory properties of linear conditional heteroscedasticity models, and a new model. J. Bus. Econ. Stat. 22(1), 16–29 (2004)
Ding, Z., Granger, C.W., Engle, R.F.: A long memory property of stock market returns and a new model. J. Empir. Financ. 1(1), 83–106 (1993)
Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econ. J. Econ. Soc. 50, 987–1007 (1982)
Glosten, L.R., Jagannathan, R., Runkle, D.E.: On the relation between the expected value and the volatility of the nominal excess return on stocks. J. Financ. 48(5), 1779–1801 (1993)
Hamilton, J.D.: A new approach to the economic analysis of nonstationary time series and the business cycle. Econ. J. Econ. Soc. 57, 357–384 (1989)
Harvey, A.C., Chakravarty, T.: Beta-t-(E) GARCH. University of Cambridge, Faculty of Economics, Working paper CWPE 08340 (2008)
Harvey, A., Sucarrat, G.: EGARCH models with fat tails, skewness and leverage. Comput. Stat. Data Anal. 76, 320–338 (2014)
Kang, S.H., Kang, S.M., Yoon, S.M.: Forecasting volatility of crude oil markets. Energy Econ. 31(1), 119–125 (2009)
Lopez, J.A.: Evaluating the predictive accuracy of volatility models. J. Forecast. 20(2), 87–109 (2001)
Maneejuk, P., Yamaka, W., Sriboonchitta, S.: A Markov-switching model with mixture distribution regimes. In: Huynh, V.-N., Inuiguchi, M., Tran, D.H., Denoeux, T. (eds.) IUKM 2018. LNCS, vol. 10758, pp. 312–323. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75429-1_26
Merton, R.C.: Lifetime portfolio selection under uncertainty: the continuous-time case. Rev. Econ. Stat. 51, 247–257 (1969)
Nelson, D.B.: Conditional heteroskedasticity in asset returns: a new approach. Econ. J. Econ. Soc. 59, 347–370 (1991)
Page, R., Satake, E.: Beyond P values and hypothesis testing: using the minimum Bayes factor to teach statistical inference in undergraduate introductory statistics courses. J. Educ. Learn. 6(4), 254 (2017)
Salisu, A.A.: Modelling oil price volatility with the Beta-Skew-t-EGARCH framework. Econ. Bull. 36(3), 1315–1324 (2016)
Sucarrat, G.: betategarch: simulation, estimation and forecasting of Beta-Skew-t-EGARCH models. R J. 5(2), 137–147 (2013)
Wagenmakers, E.J., Farrell, S.: AIC model selection using Akaike weights. Psychon. Bull. Rev. 11(1), 192–196 (2004)
Wei, Y., Wang, Y., Huang, D.: Forecasting crude oil market volatility: further evidence using GARCH-class models. Energy Econ. 32(6), 1477–1484 (2010)
Zhu, K., Yamaka, W., Sriboonchitta, S.: Pair trading rule with switching regression GARCH Model. In: Huynh, V.-N., Inuiguchi, M., Le, B., Le, B.N., Denoeux, T. (eds.) IUKM 2016. LNCS, vol. 9978, pp. 586–598. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49046-5_50
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yamaka, W., Maneejuk, P., Sriboonchitta, S. (2019). Markov Switching Beta-skewed-t EGARCH. In: Seki, H., Nguyen, C., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2019. Lecture Notes in Computer Science(), vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-14815-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14814-0
Online ISBN: 978-3-030-14815-7
eBook Packages: Computer ScienceComputer Science (R0)