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Implied Volatility Index of KOSPI200: Information Contents and Properties

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  • Doojin Ryu

Abstract

This paper investigates the properties and information contents of an implied volatility index based on Korea's index options contract, which is the most liquid options product in the world. Analyzing the recent 100-month-long volatility index series (VKOSPI; Volatility Index of KOSPI200) constructed using the KOSPI200 index and options prices, we measure the in-sample and out-of-sample forecasting performances of the implied volatility index and examine its quality as a market volatility indicator. The VKOSPI exhibits an asymmetric volatility response to positive and negative return shocks and has a significantly positive effect on the explanatory power of nested GARCH models. Though the VKOSPI provides slightly biased forecasts, as other risk-adjusted volatility measures also do, it outperforms the Black-Scholes implied volatility, the RiskMetrics approach, and the GJR-GARCH model (which generally shows the best in-sample performance among the GARCH-family models) in forecasting future realized volatilities.

Suggested Citation

  • Doojin Ryu, 2012. "Implied Volatility Index of KOSPI200: Information Contents and Properties," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(0), pages 24-39, July.
    • Handle: RePEc:mes:emfitr:v:48:y:2012:i:0:p:24-39
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    Citations

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    Cited by:

    1. Fassas, Athanasios P. & Siriopoulos, Costas, 2021. "Implied volatility indices – A review," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 303-329.
    2. Najmi Ismail Murad Samsudin & Azhar Mohamad & Imtiaz Mohammad Sifat & Zarinah Hamid, 2022. "Predictive power of implied volatility of structured call warrants: Evidence from Singapore," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4412-4430, October.
    3. Kim, Jun Sik & Ryu, Doojin, 2014. "Intraday price dynamics in spot and derivatives markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 247-253.
    4. Doojin RYU & Hyein SHIM, 2017. "Intraday Dynamics of Asset Returns, Trading Activities, and Implied Volatilities: A Trivariate GARCH Framework," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 45-61, June.
    5. Murad Samsudin, Najmi Ismail & Mohamad, Azhar & Sifat, Imtiaz Mohammad, 2021. "Implied volatility of structured warrants: Emerging market evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 464-479.
    6. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2020. "Economic indicators and stock market volatility in an emerging economy," Economic Systems, Elsevier, vol. 44(2).
    7. Lee, Bong Soo & Ryu, Doojin, 2013. "Stock returns and implied volatility: A new VAR approach," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 7, pages 1-20.
    8. Shailesh Rastogi & Chaitaly Athaley, 2019. "Volatility Integration in Spot, Futures and Options Markets: A Regulatory Perspective," JRFM, MDPI, vol. 12(2), pages 1-15, June.
    9. Song, Wonho & Ryu, Doojin & Webb, Robert I., 2016. "Overseas market shocks and VKOSPI dynamics: A Markov-switching approach," Finance Research Letters, Elsevier, vol. 16(C), pages 275-282.
    10. Kim, Jun Sik & Ryu, Doojin, 2015. "Are the KOSPI 200 implied volatilities useful in value-at-risk models?," Emerging Markets Review, Elsevier, vol. 22(C), pages 43-64.
    11. Atilgan, Yigit & Demirtas, K. Ozgur & Simsek, Koray D., 2016. "Derivative markets in emerging economies: A survey," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 88-102.
    12. Doojin Ryu, 2013. "Spread and depth adjustment process: an analysis of high-quality microstructure data," Applied Economics Letters, Taylor & Francis Journals, vol. 20(16), pages 1506-1510, November.
    13. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2019. "Forecasting the KOSPI200 spot volatility using various volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 156-166.
    14. Kim, Jungmu & Park, Yuen Jung & Ryu, Doojin, 2018. "Testing CEV stochastic volatility models using implied volatility index data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 224-232.
    15. Han, Heejoon & Kutan, Ali M. & Ryu, Doojin, 2015. "Effects of the US stock market return and volatility on the VKOSPI," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-34.
    16. Han, Heejoon & Kutan, Ali M. & Ryu, Doojin, 2015. "Modeling and predicting the market volatility index: The case of VKOSPI," Economics Discussion Papers 2015-7, Kiel Institute for the World Economy (IfW Kiel).
    17. Yue, Tian & Ruan, Xinfeng & Gehricke, Sebastian & Zhang, Jin E., 2023. "The volatility index and volatility risk premium in China," The Quarterly Review of Economics and Finance, Elsevier, vol. 91(C), pages 40-55.
    18. Park, Sung Y. & Ryu, Doojin & Song, Jeongseok, 2017. "The dynamic conditional relationship between stock market returns and implied volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 638-648.
    19. Ryu, Doojin, 2013. "What types of investors generate the two-phase phenomenon?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5939-5946.
    20. Kang, Bo Soo & Ryu, Doojin & Ryu, Doowon, 2014. "Phase-shifting behaviour revisited: An alternative measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 167-173.
    21. Daehyeon Park & Jiyeon Park & Doojin Ryu, 2020. "Volatility Spillovers between Equity and Green Bond Markets," Sustainability, MDPI, vol. 12(9), pages 1-12, May.

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