The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Value-at-Risk Estimation of the KOSPI Returns by Employing Long-Memory Volatility Models

장기기억 변동성 모형을 이용한 KOSPI 수익률의 Value-at-Risk의 추정

  • Oh, Jeongjun (Department of Statistics, University of Seoul) ;
  • Kim, Sunggon (Department of Statistics, University of Seoul)
  • Received : 2012.11.15
  • Accepted : 2013.01.23
  • Published : 2013.02.28
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Abstract

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.

본 논문에서는 장기기억 변동성 모형의 필요성을 Value-at-Risk(VaR) 추정의 관점에서 알아본다. 이를 위해, KOSPI 수익률의 VaR을 FIGARCH, FIEGACH와 같은 장기기억 변동성 모형과 GARCH, EGARCH와 같은 단기기억 변동성 모형을 적용하여 각각 추정한 후, 각 변동성 모형에 따른 추정의 적절성을 사후검증을 통하여 비교해 본다. 사후검증을 통해, KOSPI 수익률 과정이 장기기억 속성을 가짐을 확인할 수 있으며, 적절한 VaR의 추정을 위해서는 장기기억 변동성 모형을 적용하는 것이 필요함을 알 수 있다.

Keywords

References

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