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Korean Academic Society of Business Administration
[ Article ]
korean management review - Vol. 45, No. 4, pp.1173-1211
ISSN: 1226-1874 (Print)
Print publication date 31 Aug 2016
Received 03 Aug 2015 Revised 04 Jan 2016 Accepted 10 May 2016
DOI: https://doi.org/10.17287/kmr.2016.45.4.1173

변동성 예측에서 실현 왜도와 첨도가 갖는 정보효과: 이질적 자기회귀모형의 개선을 중심으로

엄철준* ; 박종원**
*(주저자) 부산대학교 경영대학 교수 shunter@pusan.ac.kr
**(교신저자) 서울시립대학교 경영대학 교수 parkjw@uos.ac.kr
A Study on the Information Effect of Realized Skewness and Kurtosis in Volatility Forecasting Using the Heterogeneous Autoregressive Model
Cheoljun Eom* ; Jong Won Park**
*Professor, School of Business, Pusan National University, First Author
**Professor, College of Business Administration, University of Seoul, Corresponding Author

초록

금융시장에서 일중 고빈도 자료의 이용이 증대함에 따라 이를 이용한 실현변동성(RV)과 이질적 자기회귀모형(HAR)을 결합하여 미래 변동성의 예측성과를 개선하기 위한 다양한 연구가 진행되고 있다. 본 연구에서는 KOSPI의 일중 고빈도 자료를 이용하여 실현변동성의 측정, 실현왜도와 실현첨도를 새로이 포함하는 이질적 자기회귀모형의 구성과 이에 기초한 미래 변동성의 예측성과를 검증하였다. 연구를 위해 실현변동성을 측정하고 실현변동성으로부터 불연속적 점프요소를 비모수적으로 분리하는 과정에서 기존연구에서 확인되는 문제점을 보완하여 개선된 방법을 이용하였다. 본 연구에서 확인된 주요 결과를 요약·정리하면 다음과 같다. 첫째, 한국주식시장에서 과거기간의 실현변동성을 반영한 이질적 자기회귀모형은 미래 실현변동성의 변화에 대하여 높은 설명력을 보여주며, 상이한 속성을 갖는 실현변동성의 연속적 요소와 불연속적 점프요소를 분리하여 모형에 적용하는 것은 미래 실현변동성의 변화에 대한 설명력을 개선하는데 유용하다. 둘째, 새로이 실현왜도와 실현첨도를 포함한 HAR-RV모형은 미래 실현변동성에 대한 설명력과 예측성과를 분명하게 개선하는 증거를 보여준다. 즉, 실현왜도와 실현첨도는 이질적 투자자들의 특성에 따른 장단기 변동성의 변화를 보다 잘 설명할 수 있는 추가적 정보를 가지며, 미래 변동성에 대한 설명력과 예측력을 개선할 수 있는 주요한 변수이다. 또한 새로운 설명변수의 유용성은 기존에 알려진 변동성 레버리지효과와 하루 중 수익률 변동성의 특성에 무관하게 성립한다.

Abstract

In financial markets, there are many studies for improving the predictability of future volatility by combining the heterogeneous autoregressive (HAR) model and realized volatility (RV) using intraday high-frequency data. We examine the predictability of future volatility based on expanded HAR-RV models including realized skewness and realized kurtosis proposed newly in this study using the intraday high-frequency data of KOSPI. In the study, we utilize a modified method that improves the problems from the previous studies, which may occur in the process of separating the continuos elements and the discontinuous jump elements from the realized volatility. The main results are as follows. First, the HAR-RV model shows the high explanatory power with respect to changes in future volatility, and application of the continuous element and discontinuous jump element of realization volatility into the model separately is useful for enhancing the explanatory power. Second, the HAR-RV model including realized skewness and kurtosis proposed in this study shows obviously improvement of both the predictability and explanatory power for the future volatility. Realized skewness and kurtosis can have additional information reflecting the volatility characteristics resulting from the action and reaction of various heterogeneous investors with different time horizons. And our results suggest that realized skewness and kurtosis are useful variables with additive information for predicting the future volatility in the HAR-RV model. The usefulness of these variables is well established regardless of controlling the volatility leverage effect and intra-day return seasonality effect in the model.

Keywords:

Intraday high frequency data, Realized Volatility, Heterogenous autoregressive model, Realized skewness, Realized kurtosis

키워드:

고빈도자료, 실현변동성, 이질적 자기회귀모형, 실현왜도, 실현첨도

Acknowledgments

이 논문은 2015년도 서울시립대학교 교내학술연구비에 의하여 지원되었음.This work was supported by the 2015 Research Fund of the University of Seoul

References

  • 엄철준(2015), “고빈도 수익률과 실현변동성을 이용한 금융자료의 통계적 속성에 관한 재고찰,” 재무연구, 28(3), 453-485.
  • 전찬수(2013), “Value-at-Risk를 통한 실현변동성 모형과 GARCH 계열 모형의 예측성과 비교,” 선물연구, 21(2), 135-167.
  • 정재식·주상영·이승문(2003), “원/달러 환율의 실현변동성,” 대외경제연구, 2, 55-78.
  • 한상범·김형태(2002), “코스닥지수 및 벤처지수의 변동성분석,” 경제학연구, 50(1), 117-143.
  • Amaya D., P. Christoffersen, K. Jacobs and A. Vasquez(2013), “Does Realized Skewness Predict the Cross-Section of Equity Returns?,” Working Paper Available in SSRN Website.
  • Andersen, T. G., and T. Bollerslev(1998), “Answering the Skeptics: YES, Standard Volatility Models do Provide Accurate Forecasts,” International Economic Review,39(4), 885-905. [https://doi.org/10.2307/2527343]
  • Andersen, T. G., T. Bollerslev, F. X. Diebold and P. Labys(2001a), “The Distribution of Realized Exchange Rate Volatility,” Journal of the American Statistical Association, 96, 42-55. [https://doi.org/10.1198/016214501750332965]
  • Andersen, T. G., T. Bollerslev, F. X. Diebold and H. Ebens(2001b), “The Distribution of Realized Stock Return Volatility,” Journal of Financial Economics 61, 43-76. [https://doi.org/10.1016/S0304-405X(01)00055-1]
  • Andersen, T. G., T. Bollerslev, F. X. Diebold and P. Labys(2003), “Modeling and Forecasting Realized Volatility,” Econometrica 71, 579-625. [https://doi.org/10.1111/1468-0262.00418]
  • Andersen, T. G., T. Bollerslev and F. X. Diebold (2007), “Roughing it up: including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility,” Review of Economics and Statistics 89, 701-720. [https://doi.org/10.1162/rest.89.4.701]
  • Andersen T. G., T. Bollerslev, and X. Huang(2011), “A Reduced Form Framework for Modeling Volatility of Speculative Prices based on Realized Variance Measures,” Journal of Econometrics 160(1), 176-189. [https://doi.org/10.1016/j.jeconom.2010.03.029]
  • Andersen T. G., D. Dobrev and E. Schaumburg (2012), “Jump-robust Volatility Estimation using Nearest Neighbor Truncation,” Journal of Econometrics 169, 75-93. [https://doi.org/10.1016/j.jeconom.2012.01.011]
  • Bardorff-Nielsen, O. E., and N. Shephard(2004), “Power and Bipower Variation with Stochastic Volatility and Jumps (with discussion),” Journal of Financial Economics 2, 1-57. [https://doi.org/10.1093/jjfinec/nbh001]
  • Bardorff-Nielsen, O. E., and N. Shephard(2006), “Econometircs of Testing for Jumps in Financial Economics using Bipower Variation,” Journal of Financial Economics 4, 1-30. [https://doi.org/10.1093/jjfinec/nbi022]
  • Bollerslev T.(1986), “Generalized Autoregressive Conditional Heteroscedasticity,” Journal of Econometrics 31(3), 307-327. [https://doi.org/10.1016/0304-4076(86)90063-1]
  • Busch T., B. J. Christensen, and M. Nielsen(2011), “The Role of Implied Volatility in Forecasting Future Realized Volatility and Jumps in Foreign Exchange, Stock, and Bond markets,” Journal of Econometrics 160, 48-57. [https://doi.org/10.1016/j.jeconom.2010.03.014]
  • Byun S. J., and J. S. Kim(2013), “The Information Content of Risk-Neutral Skewness for Volatility Forecasting,” Journal of Empirical Finance 23, 142-161. [https://doi.org/10.1016/j.jempfin.2013.05.006]
  • Chui, A. C., S. Titman and K. C. J. Wei(2010), “Individualism and Momentum around the World,” Journal of Finance 65(1), 361-392 [https://doi.org/10.1111/j.1540-6261.2009.01532.x]
  • Clark, T. E., and McCracken, M. W.(2001), “Tests of Equal Forecast Accuracy and Encompassing for Nested Models,” Journal of Econometrics 105, 85-110. [https://doi.org/10.1016/S0304-4076(01)00071-9]
  • Clark, T. E., and McCracken, M. W.(2004), “Evaluating Long-Horizon Forecasts,” Manuscript, University of Missouri at Columbia.
  • Cont R.(2001), “Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues,” Quantitative Finance 1, 223-236. [https://doi.org/10.1080/713665670]
  • Corsi F.(2009), “A Simple Approximate Long-memory Model of Realized Volatility,” Journal of Financial Econometrics 7(2), 174-196. [https://doi.org/10.1093/jjfinec/nbp001]
  • Corsi F., and R. Reno(2012), “Discrete-time Volatility Forecasting with Persistent Leverage Effect and the Link with Continuous-time Volatility Modeling,” Journal of Business and Economic Statistics 30(3), 368-380. [https://doi.org/10.1080/07350015.2012.663261]
  • Engle R. F.(1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica 50(4), 987-1001. [https://doi.org/10.2307/1912773]
  • Hansen P. R., and A. Lunde(2006), “Consistent Ranking of Volatility Models,” Journal of Econometrics 131(1-2), 97-121. [https://doi.org/10.1016/j.jeconom.2005.01.005]
  • Huang C., X. Gong, X. Chen and F. Wen(2013), “Measuring and Forecasting Volatility in Chinese Stock Market using HAR-CJ-M Model,” Abstract and Applied Analysis Volume 2013, Article ID 143194, 13 pages. [https://doi.org/10.1155/2013/143194]
  • Huang X., and G. Tauchen(2005), “The Relative Contribution of Jumps to Total Price Variance,” Journal of Financial Econometrics 3(4), 456-499. [https://doi.org/10.1093/jjfinec/nbi025]
  • Jegadeesh, N., and S. Titman(1993), “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency,” Journal of Finance 48, 65-91. [https://doi.org/10.1111/j.1540-6261.1993.tb04702.x]
  • Koopman S. J., B. Jungbacker and E. Hol(2005), “Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realized and Implied Volatility Measurements,” Journal of Empirical Finance 12, 445-475.
  • McCracken, M. W.(2007), “Asymptotics for Out-of-Sample Tests of Granger Causality,” Journal of Econometrics 140, 719-752. [https://doi.org/10.1016/j.jeconom.2006.07.020]
  • Merton R.(1980), “On Estimating the Expected Return on the Market: An explanatory Investigation,” Journal of Financial Economics 8, 323-361 [https://doi.org/10.1016/0304-405X(80)90007-0]
  • Mincer J., and V. Zarnowitz(1969), The Evaluation of Economic Forecasts. in Mincer, J. (Ed.) Economic Forecasts and Expectations, NBER, New York, 3-46.
  • Muller, U., M. Dacorogna, R. Dave, O. Pictet, R. Olsen, and J. Ward(1993), “Fractals and Intrinsic Time – A Challenge to Econometricians,” 39th International AEA Conference on Real Time Econometrics, 15-15 October, Luxenbourg.
  • Muller, U., M. Dacorogna, R. Dave, R. Olsen, O. Pictet, J. Weizsacker(1997), “Volatilities of Different Time Resolutions – Analyzing the Dynamic of Market Components,” Journal of Empirical Finance 4, 213-239. [https://doi.org/10.1016/S0927-5398(97)00007-8]
  • Naes R., J. A. Skjeltorp and B. A. Odegaard(2011), “Stock Market Liquidity and the Business Cycle,” Journal of Finance 66(1), 139-176. [https://doi.org/10.1111/j.1540-6261.2010.01628.x]
  • Newesy W. K., and K. West(1987), “A Simple, Positive Semi-definite, Heteroscedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica 55, 703-708. [https://doi.org/10.2307/1913610]
  • Rapach, D. E., and M. E. Wohar(2006), “In-sample vs. Out-of-Sample Tests of Stock Return Predictability in the Context of Data Mining,” Journal of Empirical Finance 13, 231-247. [https://doi.org/10.1016/j.jempfin.2005.08.001]
  • Wood, R. A., T. H. McInish and J. K. Ord(1985), “An Investigation of Transactions Data for NYSE Stocks,” Journal of Finance 40(3), 723-739. [https://doi.org/10.1111/j.1540-6261.1985.tb04996.x]

• 저자 엄철준은 현재 부산대학교 경영대학 재무관리 전공 교수로 재직 중이다. 부산대학교 경영대학 및 대학원 경영학과를 졸업하였으며, 박사 취득 이후에는 POSTECH 전산수학연구센터와 뇌연구센터에서 박사후연구원으로 일했다. 주요연구분야는 포트폴리오 최적화 선택, 계량금융 설계 및 실험, 학제간연구(Econo-physics) 등이다.

• 저자 박종원은 현재 서울시립대학교 경영대학 재무금융 전공 교수로 재직 중이다. 국민대학교 경영학과를 졸업하였으며, 서울대학교 대학원 경영학과에서 재무금융전공으로 경영학 석사 및 박사를 취득하였다. 주요연구분야는 자산가격결정이론의 실증, 금융시장 변동성, 위험관리, 금융제도, 파생상품시장 등이다.