응용통계연구 (The Korean Journal of Applied Statistics)

  • 제29권7호
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  • Pages.1213-1229
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  • 2016
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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장기기억성과 비대칭성을 띠는 실현변동성의 예측을 위한 LIHAR모형

LIHAR model for forecasting realized volatilities featuring long-memory and asymmetry

  • 신지원 (이화여자대학교 통계학과) ;
  • 신동완 (이화여자대학교 통계학과)
  • Shin, Jiwon (Department of Statistics, Ewha Womans University) ;
  • Shin, Dong Wan (Department of Statistics, Ewha Womans University)
  • 투고 : 2016.07.15
  • 심사 : 2016.09.14
  • 발행 : 2016.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

최근에 Cho와 Shin (2016)가 변동성 예측 모형으로 유명한 HAR (Corsi, 2009) 모형보다 단위근을 부과한 IHAR 모형이 더 우수하다는 것이 보고하였다. 금융시계열에 비대칭 변동성이 존재한다는 것은 널리 알려져 있다. 이 논문에서는 IHAR 모형에 레버리지를 고려한 LIHAR 모형을 제안한다. LIHAR 모형과 IHAR 모형 기존의 HAR 모형, LHAR 모형과의 예측력 비교를 통해 LIHAR 모형의 우수성을 보인다. 모형을 평가하기 위해 Oxford-Man 라이브러리 20개의 실현변동성 데이터를 이용하였다. 특히 DJIA, S&P 500, Russell 2000, KOSPI Composite 데이터는 다양한 분석을 하였다. 주가와 같은 금융지수의 변동성에는 장기기억성과 비대칭 변동성이 존재하고, 이런 특징을 LIHAR 모형이 HAR, IHAR, LHAR 모형보다 적절하게 반영하고 있는 것을 확인 하였다. 또한 예측력도 LIHAR 모형이 가장 우수하였다. 금융시계열의 실현변동성에 장기기억성, 비대칭변동성, 비정상성을 모두 반영하여 예측하는 것이 상당한 가치가 있음을 확인하였다.

Cho and Shin (2016) recently demonstrated that an integrated HAR model has a forecast advantage over the HAR model of Corsi (2009). Recalling that realized volatilities of financial assets have asymmetries, we add a leverage term to the integrated HAR model, yielding the LIHAR model. Out-of-sample forecast comparisons show superiority of the LIHAR model over the HAR and IHAR models. The comparison was made for all the 20 realized volatilities in the Oxford-Man Realized Library focusing specially on the DJIA, the S&P 500, the Russell 2000, and the KOSPI. Analysis of the realized volatility data sets reveal apparent long-memory and asymmetry. The LIHAR model takes advantage of the long-memory and asymmetry and produces better forecasts than the HAR, IHAR, LHAR models.

키워드

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