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Investment Strategies for KOSPI200 Index Futures Using VKOSPI and Control Chart

변동성지수와 관리도를 이용한 KOSPI200 지수선물 투자전략

  • Ryu, Jaepil (Dept. of Management Engineering, Sangmyung University) ;
  • Shin, Hyun Joon (Dept. of Management Engineering, Sangmyung University)
  • 유재필 (상명대학교 경영공학과) ;
  • 신현준 (상명대학교 경영공학과)
  • Received : 2012.10.02
  • Accepted : 2012.11.15
  • Published : 2012.12.01

Abstract

This paper proposes quantitative investment strategies for KOSPI200 index futures using VKOSPI and control chart. Stochastic control chart is employed to decide when to take a position as well as what position out of long and short should be taken by monitoring whether VKOSPI or difference of VKOSPI touches the control limit lines. The strategies include 4 approaches, which are traditional control chart and 2-Area control chart coupled with VKOSPI and its difference, respectively. Computational experiments using real KOSPI200 futures index for recent 3 years are conducted to show the excellence of the proposed investment strategies under control chart framework.

Keywords

References

  1. Becker, R., Clements, A. E., and McClelland, A. (2009), The Jump Component of S&P 500 Volatility and the VIX index, Journal of Banking and Finance, 33(6), 1033-1038. https://doi.org/10.1016/j.jbankfin.2008.10.015
  2. Canina, L. and Figlewski, S. (1993), The Information Content of Implied Volatility, Review of Financial Studies, 6, 659-681. https://doi.org/10.1093/rfs/6.3.659
  3. Choi, C. K. and Kim, G. B. (2009), Assesment of Applicable Strategy of VKOSPI, Derivatives Weekly Report of 2009. 05. 12, Woori Investment and Securities.
  4. Choi, I. H. (2010), Empirical Comparison of Forecasting Performance of the Volatility from KOSPI 200 Index Options Market : Comparison Forecasting Power between Volatility Index (VKOSPI) and Volatility Models, Master's Thesis, The Graduate School of Ewha Womans University.
  5. Christen, B. J. and Prabhala, N. R. (1998), The Relation between Implied and Realized Volatility, Journal of Financial Economics, 50, 125-150. https://doi.org/10.1016/S0304-405X(98)00034-8
  6. Corrado, C. J. and Miller, T. W. (2005), The Forecast Quality of CBOE Implied Volatility Indexes, The Journal of Future Markets, 25, 339-373. https://doi.org/10.1002/fut.20148
  7. Day, T. and Lewis, C. (1992), Stock Market Volatility and the Information content of Stock Index Options, Journal of Economics, 52, 267-287. https://doi.org/10.1016/0304-4076(92)90073-Z
  8. Giot, P. (2005), Relationships Between Implied Volatility Indexes and Stock Index Returns, The Journal of Portfolio Management, 31, 92-100. https://doi.org/10.3905/jpm.2005.500363
  9. Jorion, P. (1995), Predicting Volatility in the Foreign Exchange Market, Journal of Finance, 50, 507-528. https://doi.org/10.1111/j.1540-6261.1995.tb04793.x
  10. Kim D. S. and Ryoo H. S. (2007), Portfolio Management Using Statistical Process Control Chart, Korean Institute of Industrial Engineers, 20(2), 94-102.
  11. Koopman, S. J., Jungbacker. B., and Eugenie, H. (2005), Forecasting Daily Vari-ability of the S&P 100 Stock Index using Historical, Realized and Implied Volatility, Journal of Empirical Finance, 12, 445-475. https://doi.org/10.1016/j.jempfin.2004.04.009
  12. Martens, M. (2002), Measuring and Forecasting S&P 500 Index-futures Volatility using High-frequency Data, The Journal of Futures Markets, 22, 497-518. https://doi.org/10.1002/fut.10016
  13. Lee, J. H. (2009), VIX of Korea; VKOSPI Volatility Index, Derivatives Issue Report of 2009.04.13, Tongyang Securities Inc.
  14. Ock, K. Y. (1997), An Empirical Study on the Asymmetric Effect of News on Volatility, Asia-Pacific Journal of Financial Studies, 21, 295-324.

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