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ETF risk management
Journal of the Korean Data & Information Science Society 2017;28:843-51
Published online July 31, 2017
© 2017 Korean Data & Information Science Society.

Woosik Lee1

1Department of Information Statistics, Anyang University
Correspondence to: Woosik Lee
Adjunct faculty, Department of Information Statistics, Anyang University, Gyeonggi-do 14028, Korea. Email: woosiklee@hotmail.com
Received June 22, 2017; Revised July 14, 2017; Accepted July 24, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The rise of the Robo-advisor represents one of the most profound shifts in FinTech. It also raises concerns about their financial management. As the most Robo-Advisors utilize ETFs, we seek to determine the appropriate risk management model in estimating 95% Value-at-Risk (VaR) and 99% VaR in this paper. The GARCH and the Markov regime wwitching GARCH are evaluated in terms of the accuracy of probability, the independence of extreme events occurrence and both. The result shows that the Markov regime switching GARCH can be a good ETF risk management tool since it can reflect financial market structural changes into the volatility.
Keywords : Exchange traded fund, FinTech, Market structural changes, Markov regime switching, Robo-Advisor


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