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Bootstrap Value-at-Risk estimation based on CCC-GARCH models
Journal of the Korean Data & Information Science Society 2018;29:747-67
Published online May 31, 2018
© 2018 Korean Data and Information Science Society.

Gyuyeon Kim1 · Taewook Lee2

12Department of Statistics, Hankuk University of Foreign Studies
Correspondence to: Professor, Department of Statistics, Hankuk University of Foreign Studies, Gyeonggi-do 17035, Korea. E-mail: twlee@hufs.ac.kr
Received April 5, 2018; Revised May 22, 2018; Accepted May 24, 2018.
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
In this paper, we study the Value-at-Risk estimation using bootstrap. Specifically, VaR was estimated by using bootstrap return data based on CCC-GARCH model. In real data analysis, we constructed a portfolio using KOSPI 200 and S&P500 stock price index and compared VaR with the bootstrap method and the exponentially weighted moving average (EWMA) method on the assumption of normal distribution. The backtesting results show that VaR estimation using bootstrap method is more efficient than VaR estimation using EWMA method.
Keywords : Bootstrap, KOSPI200, loss function, S&P500, Value-at-Risk.