The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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An outlier-adaptive forecast method for realized volatilities

이상치에 근거한 선택적 실현변동성 예측 방법

  • 신지원 (이화여자대학교 통계학과) ;
  • 신동완 (이화여자대학교 통계학과)
  • Received : 2016.11.28
  • Accepted : 2017.03.30
  • Published : 2017.06.30
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Abstract

We note that the dynamics of realized volatilities (RVs) are near the boundary between stationarity and non-stationarity because RVs have persistent long-memory and are often subject to fairly large outlying values. To forecast realized volatility, we consider a new method that adaptively use models with and without unit root according to the abnormality of observed RV: heterogeneous autoregressive (HAR) model and the Integrated HAR (IHAR) model. The resulting method is called the IHAR-O-HAR method. In an out-of-sample forecast comparison for the realized volatility datasets of the 3 major indexes of the S&P 500, the NASDAQ, and the Nikkei 225, the new IHAR-O-HAR method is shown superior to the existing HAR and IHAR method.

실현변동성(RVs)이 지속적인 장기기억성과 상당히 큰 이상치의 존재로 인해 정상계열과 비정상계열의 경계에 위치한다는 것에 주목하였다. 실현변동성을 예측하기 위해 실현변동성 이상치 관측 유무에 따라 heterogeneous autoregressive (HAR) 모형과 integrated HAR (IHAR) 모형을 번갈아 사용하는 새로운 방법을 제안하였고, 이 방법을 IHAR-O-HAR라 칭하였다. 예측력 비교는 주요 지수인 S&P 500, Nasdaq과 Nikkei 225의 실현변동성 데이터를 이용하였으며 표본 외 예측력 비교에서 새로운 IHAR-O-HAR 방법은 RW 방법, HAR 방법이나 IHAR 방법의 예측력보다 우수함을 확인하였다.

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References

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