Abstract
Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex ante best individual forecasting model. In this paper, we study a generalized method of aggregation in the form of a nonlinear transformation of a linear mixture model. The major advantage of the nonlinear transformation is an excellent flexibility to calibrate predictive cumulative distributions. This method proves to be particularly useful to accommodate complex volatility in the stock market.
As for applications, we study two stock market indices, namely the Vietnamese VN30 index and the Thai SET50 index. The forecasts are in the form of empirical densities estimated by Bayesian inference.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bassetti, F., Casarin, R., Ravazzolo, F.: Bayesian nonparametric calibration and combination of predictive distributions. J. Am. Stat. Assoc. (2017)
Berkowitz, J.: Testing density forecasts, with applications to risk management. J. Bus. Econ. Stat. 19(4), 465–474 (2001)
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econ. 31(3), 307–327 (1986)
Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M.A., Guo, J., Li, P., Riddell, A.: Stan: a probabilistic programming language. J. Stat. Softw. 20, 1–37 (2016)
Casarin, R., Mantoan, G., Ravazzolo, F.: Bayesian calibration of generalized pools of predictive distributions. Econometrics 4(1), 17 (2016)
Clemen, R.T., Winkler, R.L.: Aggregating probability distributions. na (2005)
Dawid, A.P.: Present position and potential developments: some personal views: statistical theory: the prequential approach. J. Royal Stat. Soc. Ser. A (General) 147, 278–292 (1984)
Diebold, F.X., Gunther, T.A., Tay, A.S.: Evaluating density forecasts (1997)
Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econ. J. Econ. Soc. 50, 987–1007 (1982)
Genest, C., Zidek, J.V.: Combining probability distributions: a critique and an annotated bibliography. Stat. Sci. 1, 114–135 (1986)
McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, Hoboken (2004)
Mitchell, J., Wallis, K.F.: Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness. J. Appl. Econ. 26(6), 1023–1040 (2011)
Stone, M., et al.: The opinion pool. Ann. Math. Stat. 32(4), 1339–1342 (1961)
Acknowledgement
We would like to express our gratitude to professor Nguyen Trung Hung of New Mexico State University and Chiang Mai University for his generous support, guidance, encouragements and discussions over many years.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Nguyen, S.P., Pham, U.H., Nguyen, T.D. (2018). On a Generalized Method of Combining Predictive Distributions for Stock Market Index. In: Huynh, VN., Inuiguchi, M., Tran, D., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2018. Lecture Notes in Computer Science(), vol 10758. Springer, Cham. https://doi.org/10.1007/978-3-319-75429-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-75429-1_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75428-4
Online ISBN: 978-3-319-75429-1
eBook Packages: Computer ScienceComputer Science (R0)