Abstract
In this study, the non-parametric Inferential Model or IM with the entropy-based random set has been proposed for the investigation of financial data in the two statistical domains i.e. estimation and prediction. The samples from five financial markets were chosen for representing the different types of financial assets to make a conclusion about this new framework. We found that the Inferential Model performed equally well compared with the traditional method but was more robust so that it might be more appropriate for some specific uses.
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Notes
- 1.
These earlier versions, e.g. unpublished, pre-print or non-public versions, were revised for official releases but are worth exploring.
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Acknowledgements
The first and second authors thank to Assoc. Prof. Dr. Ryan Martin for clarifications on some suspicions pertaining fundamental concepts of the Inferential Model during his attendance in the 10th International Conference of the Thailand Econometric Society (TES 2017). This research is financially supported by the Center of Excellence in Econometrics and the Faculty of Economics, Chiang Mai University, Thailand.
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Rattanadamrongaksorn, T., Sirikanchanarak, D., Sirisrisakulchai, J., Sriboonchitta, S. (2018). Estimating and Predicting Financial Series by Entropy-Based Inferential Model. 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_28
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