DOI QR코드

DOI QR Code

국내 선물시장의 장기기억과 시장의 효율성에 관한 연구

Long Memory and Market Efficiency in Korean Futures Markets

  • 조대형 (국립순천대학교 경제학전공)
  • 투고 : 2020.11.30
  • 심사 : 2020.12.21
  • 발행 : 2020.12.30

초록

Purpose - This paper analyzes the market efficiency focusing on the long memory properties of the domestic futures market. By decomposing futures prices into yield and volatility and looking at the long memory properties of the time series, this study aims to understand the futures market pricing and change behavior and risks, specifically and in detail. Design/methodology/approach - This study analyzes KOSPI 200 futures, KOSDAQ 150 futures, 3 and 10-year government bond futures, US dollar futures, yen futures, and euro futures, which are among the most actively traded on the Korea Exchange. To analyze the long memory and market efficiency, we used the Variance Ratio, Rescaled-Range(R/S), Geweke and Porter-Hudak(GPH) tests as semi- parametric methods, and ARFIMA-FIGARCH model as the parametric method. Findings - It was found that all seven futures supported the efficiency market hypothesis because the property of long memory turned out not to exist in their yield curves. On the other hand, in futures volatility, all 7 futures showed long memory properties in the analysis, which means that if new information is generated in the domestic futures market and the market volatility once expanded due to the impact, it does not decrease or shrink for a long period of time, but continues to affect the volatility. Research implications or Originality - The results of this paper suggest that it can be useful information for predicting changes and risks of volatility in the domestic futures market. In particular, it was found that the long memory properties would be further strengthened in the currency futures and bond rate futures markets after the global financial crisis if the regime changes of the domestic financial market are taken into account in the analysis.

키워드

참고문헌

  1. 강태훈, 이명월 (2015), "기초자산시장과 옵션시장의 장기기억속성 비교에 관한 연구", Journal of The Korean Data Analysis Society, 17(3), 1379-1408.
  2. 강태훈, 이상식 (2015), "KOSPI200 지수 옵션가격에 내재된 Hurst 지수", Journal of The Korean Data Analysis Society, 17(4), 2055-2067.
  3. 김남종, 박성욱, 박춘성 (2020), "우리나라 금융시장 변동요인 분석", 금융연구원, KIF VIP 리포트.
  4. 노현승, 강상훈 (2014), "아시아 이머징 주식시장에서의 변동성 장기기억모형 예측력 분석", 금융공학연구, 13(3), 27-49. https://doi.org/10.35527/KFEDOI.2014.13.3.002
  5. 박재곤, 이필상 (2009), "장기기억 속성을 이용한 주가 변동성 예측에 관한 연구", 금융연구, 23(4), 33-62.
  6. 엄철준, 오갑진, 김승환, 김태혁 (2007), "주식가격변화의 장기기억 속성 존재 및 영향요인에 대한 실증연구," 재무관리연구, 24(3), 63-89.
  7. 윤성민 (2011), "한국 금융시장 장기기억 특성의 시간가변성", Journal of The Korean Data Analysis Society, 13(5), 2561-2572.
  8. 이정형, 강관중, 조신섭 (2004), "한국선물시장의 수익률과 변동성에 대한 장기기억 특성", Journal of The Korean Data Analysis Society, 6(4), 1063-1072.
  9. 이지현, 김동석, 이회경 (2002), "FIGARCH 모형을 이용한 주가 수익률 변동성의 장기기억에 관한 연구," 선물연구, 10(2), 95-114.
  10. 최상규 (2014), "KOSPI 200 수익률 변동성의 장기기억과정 탐색", Journal of The Korean Data Analysis Society, 15(12), 7018-7024.
  11. 홍정훈 (1998), "우리나라 주식수익률에 있어서의 장기적 기억에 관한 연구," 금융연구, 12(2), 57-76.
  12. Andersen, T., G. Torben and T. Bollerslev (1997), "Intraday Periodicity and Volatility Persistence in Financial Markets", Journal of Empirical Finance, 4, 115-158. https://doi.org/10.1016/S0927-5398(97)00004-2
  13. Andersen, T., G. Torben and T. Bollerslev, F. Diebold and P. Labys (2001), "The Distribution of Realized Exchange Rate Volatility", Journal of the American Statistical Association, 96(453), 42-55. https://doi.org/10.1198/016214501750332965
  14. Andersen, T., G. Torben and T. Bollerslev, F. Diebold and P. Labys (2003), "Modeling and Forecasting Realized Volatility", Econometrica, 71, 579-625. https://doi.org/10.1111/1468-0262.00418
  15. Baillie, T. R. (1996), "Long Memory Processes and Fractional Integration in Econometrics," Journal of Econometrics, 73, 5-59. https://doi.org/10.1016/0304-4076(95)01732-1
  16. Baillie, R. T., T. Bollerslev and H. O. Mikkelsen (1996), "Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity," Journal of Econometrics, 74, 3-30. https://doi.org/10.1016/S0304-4076(95)01749-6
  17. Beran, J. (2000), "Statistics for Long-memory Processes," Journal of the Royal Statistical Society, 49(3), 434-436
  18. Cajueiro, D. and B. Tabak, (2004a), "Ranking Efficiency for Emerging Markets". Chaos, Solitons & Fractals, 22(2), 349-352. https://doi.org/10.1016/j.chaos.2004.02.005
  19. Cajueiro, D. and B. Tabak (2004b), "The Hurst Exponent Over Time: Testing the Assertion that Emerging Markets Are Becoming More Efficient", Physica A: Statistical Mechanics and its Applications, 336(3-4), 521-537. https://doi.org/10.1016/j.physa.2003.12.031
  20. Choi, K. and S. Hammoudeh (2009), "Long Memory in Oil and Refined Product Markets", The Energy Journal, 30, 97-116.
  21. Crato, N. and P. de Lima (1994), "Long Range Dependence in the Conditional Variance of Stock Returns", Economic Letters, 45, 281-285. https://doi.org/10.1016/0165-1765(94)90024-8
  22. Darrat, A. F. and M. Zhong, (2000): "On Testing the Random-Walk Hypothesis: A Model Comparison Approach", The Financial Review, 35(3), 105-124. https://doi.org/10.1111/j.1540-6288.2000.tb01423.x
  23. Ding, Z., C. W. J. Granger and R. F. Engle (1993), "A Long Memory Property of Stock Returns and A New Model", Journal of Empirical Finance, 1, 83-106. https://doi.org/10.1016/0927-5398(93)90006-D
  24. Ding, Z. and C. W. J. Granger (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
  25. Fama, E. F. (1965), "The Behavior of Stock Market Prices," Journal of Business, 38(1), 34-105. https://doi.org/10.1086/294743
  26. Geweke, J. and S. Porter-Hudak (1983), "The Estimation and Application of Long Memory Time Series Models", The Journal of Time Series Analysis, 4(4), 221-238, https://doi.org/10.1111/j.1467-9892.1983.tb00371.x
  27. Henry, T. (2002), "Long Memory in Stock Returns: Some International Evidence", Journal Applied Financial Economics, 12(10), 725-729. https://doi.org/10.1080/09603100010025733
  28. Hull, M. and F. McGroarty (2014), "Do Emerging Markets Become More Efficient as They Develop? Long Memory Persistence in Equity Indices", Emerging Markets Review, 18, 45-61. https://doi.org/10.1016/j.ememar.2013.11.001
  29. Kang, Sang-Hoon, Kyung-Sik Kim and Seong-Min Yoon (2006), "Dual Long Memory Properties in the Korean Stock Market," Journal of Economic Studies, 24(2), 259-286
  30. Lo, A. W. (1991), "Long-Term Memory in Stock Market Prices", Econometrica, 59(5), 1279-1313. https://doi.org/10.2307/2938368
  31. Lo, A. W. and A. C. MacKinlay (1988), "Stock Market Prices Do Not Follow Random Walks: Evidence from A Simple Specification Test", Review of Financial Studies, 1(1), 41-66. https://doi.org/10.1093/rfs/1.1.41
  32. Mandelbrot, B. B. (1971), "A Fast Fractional Gaussian Noise Generator", Water Resources Research, 7(3), 543-553. https://doi.org/10.1029/WR007i003p00543
  33. Mandelbrot, B. B. (1997), "Fractals and Scaling in Finance: Discontinuity, Concentration, Risk", New York: Springer Publishing Company. 1-551.
  34. Nagayasu, J. (2003), "The Efficiency of the Japanese Equity Market," IMF Working Paper, WP/03/142, 1-23.
  35. Osborne, M. F. M. (1959), "Brownian Motion in the Stock Market," Operations Research, 7, 145-173. https://doi.org/10.1287/opre.7.2.145
  36. Qian, B. and K. Rasheed (2007), "Stock Market Prediction with Multiple Classifiers", Applied Intelligence, 26, 25-33. https://doi.org/10.1007/s10489-006-0001-7
  37. Su, J. J. (2003), "On the Power of the Multivariate KPSS Test of Stationarity Against Fractionally Integrated Alternatives," Applied Economics Letters, 10, 637-641. https://doi.org/10.1080/1350485032000133336
  38. Tolvi, J. (2003), "Long Memory and Outliers in Stock Market Return", Applied Financial Economics, 13, 495-502. https://doi.org/10.1080/09603100210161983

피인용 문헌

  1. 환경쿠즈네츠곡선을 이용한 한국의 농업 생산과 온실가스 배출의 관계 분석 vol.12, pp.1, 2020, https://doi.org/10.32599/apjb.12.1.202103.209