Extreme Value Scenario Tree Generation Approaches

DOI

10.2991/icemaess-15.2016.155How to use a DOI?

Keywords

Scenario; Scenario tree; Extreme value k-means clustering

Abstract

In actual investment decision activity, investors are usually risk averse and always pay more attention to the tail characteristic of the loss distribution. However, the tail matching effect is not considered in existing scenario tree generation approaches. In light of this phenomenon, a new extreme value k-means clustering method is first presented to generate the scenario tree, and also, by simultaneously utilizing simulation, extreme value k-means clustering, we devise one new multistage scenario tree generation approach. Empirical results show that the better actual performance effect of the new multistage scenario tree generation approach.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li Yang
AU  - Weize Wang
PY  - 2015/12
DA  - 2015/12
TI  - Extreme Value Scenario Tree Generation Approaches
BT  - Proceedings of the 2015 3rd International Conference on Education, Management, Arts, Economics and Social Science
PB  - Atlantis Press
SP  - 731
EP  - 735
SN  - 2352-5398
UR  - https://doi.org/10.2991/icemaess-15.2016.155
DO  - 10.2991/icemaess-15.2016.155
ID  - Yang2015/12
ER  -