Value-at-Risk Estimation of the KOSPI Returns by Employing Long-Memory Volatility Models

In this paper, we investigate the need to employ long-memory volatility models in terms of Value-at-Risk(VaR)estimation. We estimate the VaR of the KOSPI returns using long-memory volatility models such as FIGARCH and FIEGARCH; in addition, via back-testing we compare the performance of the obtained VaR with short memory processes such as GARCH and EGARCH. Back-testing says that there exists a long-memory property in the volatility process of KOSPI returns and that it is essential to employ long-memory volatility models for the right estimation of VaR.