Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Using rough set to develop a volatility reverting strategy in options market

러프집합을 활용한 KOSPI200 옵션시장의 변동성 회귀 전략

  • Kang, Young Joong (Department of Information and Industrial Engineering, Yonsei University) ;
  • Oh, Kyong Joo (Department of Information and Industrial Engineering, Yonsei University)
  • 강영중 (연세대학교 정보산업공학과) ;
  • 오경주 (연세대학교 정보산업공학과)
  • Received : 2012.11.13
  • Accepted : 2013.01.15
  • Published : 2013.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study proposes a novel option strategy by using characteristic of volatility reversion and rough set algorithm in options market. Until now, various research has been conducted on stock and future markets, but minimal research has been done in options market. Particularly, research on the option trading strategy using high frequency data is limited. This study consists of two purposes. The first is to enjoy a profit using volatility reversion model when volatility gap is occurred. The second is to pursue a more stable profit by filtering inaccurate entry point through rough set algorithm. Since options market is affected by various elements like underlying assets, volatility and interest rate, the point of this study is to hedge elements except volatility and enjoy the profit following the volatility gap.

본 논문에서는 옵션시장에서의 변동성 회귀특성과 러프집합 알고리즘을 이용한 옵션전략을 개발하는 것을 제안한다. 이제까지 주식, 선물 시장에서는 다양한 연구가 선행되어 왔지만, 옵션시장에 대한 연구는 활발하지 않았다. 특히 고빈도 자료를 이용한 옵션 트레이딩 전략은 미미한 수준이다. 본 연구의 목적은 두가지로 구성된다. 첫째는 내재변동성 고평가, 저평가 상태를 측정하여 괴리가 발생했을 때 이익을 향유하는 변동성 회귀 모델을 구축하는 것이다. 둘째는 옵션트레이딩전략에 러프집합 알고리즘을 사용하여 부정확한 진입신호를 필터링하여 더 안정적인 수익을 추구하는 것이다. 이 논문의 요점은 옵션시장이 기초자산, 변동성, 이자율과 같은 다양한 요소에 영향을 받기 때문에, 변동성을 제외한 요인 (기초자산의 방향성)을 선물로 헤지하면서, 변동성괴리에 따른 이익만을 향유하는 것이다.

Keywords

References

  1. Cheung, C. H., Chen, T. L. and Wei, L. Y. (2010). A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Information Sciences, 180, 1610-1629 https://doi.org/10.1016/j.ins.2010.01.014
  2. Choi, S. I. (2007). Short time scale stochastic volatility in KOSPI 200 index. Korean Academy Financial Engineering, 2, 43-58
  3. Huh, J. Y., Kim, K. J. and Han, I. G. (2010). Rough set analysis for stock market timing. Journal of Intelligence and Information Systems, 16, 77-97
  4. Kim, M. S. and Oh, K. J. (2011). Using rough set to support arbitrage box spread strategies in KOSPI 200 option markets. Journal of the Korean Data & Information Science Society, 22, 37-47
  5. Kim, S. W., Choi, H. S. and Bae, M. G. (2010). Probability of intra-day short volatility strategy using volatility risk premium. The Korean Operations Research and Management Science Society, 27, 33-41
  6. Lee, C. H. and Seo, S. H. (2002). Discovering classification knowledge for data mining using rough sets and hierarchical classification structure. Korean Institute of Intelligent Systems, 12, 202-209 https://doi.org/10.5391/JKIIS.2002.12.3.202
  7. Lee, J. S., Song, Y. K. and Heo, S. H. (2000). A forecasting system for KOSPI 200 option trading using artificial neural network ensemble. Korea Intelligent Information System Society, 2, 489-497
  8. Moon, S. J. (2004). The informational content of volatility in KOSPI 200 index option. Korean Industrial Economic Association, 17, 2475-2490
  9. Pawlak, Z. (1997). Rough set approach to knowledge-based decision support. European Journal of Operational Research, 99, 48-57 https://doi.org/10.1016/S0377-2217(96)00382-7
  10. Pawlak, Z. and Sowinski, R. (1994). Rough set approach to multi-attribute decision analysis. European Journal of Operational Research, 72, 443-459 https://doi.org/10.1016/0377-2217(94)90415-4
  11. Siriopoulos, C. and Fassas, A. (2012). An investor sentiment barometer-Greek implied volatility index (GRIV). Global Finance Journal, 23, 77-93 https://doi.org/10.1016/j.gfj.2012.03.001
  12. Song, C. W. and Oh, K. J. (2009). Study of validation process according to various option strategies in a KOSPI 200 options market. Journal of the Korean Data & Information Science Society, 20, 1061-1073
  13. Tung, W. L. and Quek, C. (2011). Financial volatility trading using a self-organising neural-fuzzy semantic network and option Straddle-based approach. Expert Systems with Applications, 38, 4668-4688 https://doi.org/10.1016/j.eswa.2010.07.116
  14. Yao, J., Li, Y. and Tan, C. L. (2000). Option price forecasting using neural networks. Omega, 28, 455-466 https://doi.org/10.1016/S0305-0483(99)00066-3

Cited by

  1. Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market vol.25, pp.2, 2014, https://doi.org/10.7465/jkdi.2014.25.2.281