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이상치에 근거한 선택적 실현변동성 예측 방법

An outlier-adaptive forecast method for realized volatilities

  • 신지원 (이화여자대학교 통계학과) ;
  • 신동완 (이화여자대학교 통계학과)
  • 투고 : 2016.11.28
  • 심사 : 2017.03.30
  • 발행 : 2017.06.30

초록

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.

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.

키워드

참고문헌

  1. Andersen, T. G., Bollerslev, T., Diebold, F. X., and Ebens, H. (2001). The distribution of realized stock return volatility, Journal of Financial Economics, 61, 43-76. https://doi.org/10.1016/S0304-405X(01)00055-1
  2. Andersen, T. G. and Bollerslev, T. (1997). Heterogeneous information arrivals and return volatility dynamics: uncovering the long-run in high frequency returns, The Journal of Finance, 52, 975-1005. https://doi.org/10.1111/j.1540-6261.1997.tb02722.x
  3. Cho, S. and Shin, D. W. (2016). An integrated heteroscedastic autoregressive model for forecasting realized volatilities, Journal of the Korean Statistical Society, 45, 371-380. https://doi.org/10.1016/j.jkss.2015.12.004
  4. Corsi, F. (2009). A simple approximate long-memory model of realized volatility, Journal of Financial Econometrics, 7, 174-196.
  5. Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74, 427-431.
  6. Ding, Z. and Granger, C. W. (1996). Modeling volatility persistence of speculative returns: a new approach, Journal of Econometrics, 73, 185-215. https://doi.org/10.1016/0304-4076(95)01737-2
  7. Franses, P. H. and Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility, International Journal of Forecasting, 15, 1-9. https://doi.org/10.1016/S0169-2070(98)00053-3
  8. Franses, P. H. and Haldrup, N. (1994). The effects of additive outliers on tests for unit roots and cointegration, Journal of Business & Economic Statistics, 12, 471-478.
  9. Geweke, J. and Porter-Hudak, S. (1983). The estimation and application of long memory time series models, Journal of Time Series Analysis, 4, 221-238. https://doi.org/10.1111/j.1467-9892.1983.tb00371.x
  10. Kwiatkowski, D., Phillips, P. C., Schmidt, P., and Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root?, Journal of Econometrics, 54, 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y
  11. Lamoureux, C. G. and Lastrapes, W. D. (1990). Persistence in variance, structural change and the GARCH model, Journal of Business and Economic Statistics, 8, 225-234.
  12. Lin, X., Fei, F., and Wang, Y. (2011). Analysis of the efficiency of the Shanghai stock market: a volatility perspective, Physica A: Statistical Mechanics and its Applications, 390, 3486-3495. https://doi.org/10.1016/j.physa.2011.05.017
  13. Lobato, I. N. and Velasco, C. (2000). Long memory in stock-market trading volume, Journal of Business & Economic Statistics, 18, 410-427.
  14. McAleer, M. (2005). Automated inference and learning in modeling financial volatility, Econometric Theory, 21, 232-261.
  15. Mikosch, T. and Starica, C. (2004). Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects, The Review of Economics and Statistics, 86, 378-390. https://doi.org/10.1162/003465304323023886
  16. Park, B. J. (2002). An outlier robust GARCH model and forecasting volatility of exchange rate returns, Journal of Forecasting, 21, 381-393. https://doi.org/10.1002/for.827
  17. Park, S. and Shin, D. W. (2014). Modeling and forecasting realized volatilities of Korean financial assets featuring long memory and asymmetry, Asia-Pacific Journal of Financial Studies, 43, 31-58. https://doi.org/10.1111/ajfs.12039
  18. Poon, S. H. and Granger, C. (2005). Practical issues in forecasting volatility, Financial Analysts Journal, 61, 45-56.
  19. Szakmary, A., Ors, E., Kim, J. K., and Davidson, W. N. (2003). The predictive power of implied volatility: evidence from 35 futures markets, Journal of Banking & Finance, 27, 2151-2175. https://doi.org/10.1016/S0378-4266(02)00323-0