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The information content of implied volatility indexes for forecasting volatility and market risk

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  • GIOT, Pierre

Abstract

In this paper, we assess the efficiency, information content and unbiasedness of volatility forecasts based on the VIX/VXN implied volatility indexes, RiskMetrics and GARCHtype models at the 5-, 10- and 22-day time horizon. Our empirical application focuses on the S&P100 and NASDAQ100 indexes. We also deal with the information content of the competing volatility forecasts in a market risk (VaR type) evaluation framework. The performance of the models is evaluated using LR, independence, conditional coverage and density forecast tests. Our results show that volatility forecasts based on the VIX/VXN indexes have the highest information content, both in the volatility forecasting and market risk assessment frameworks. Because they are easy-to-use and compare very favorably with much more complex econometric models that use historical returns, we argue that options and futures exchanges should compute implied volatility indexes and make these available to investors.

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  • GIOT, Pierre, 2003. "The information content of implied volatility indexes for forecasting volatility and market risk," LIDAM Discussion Papers CORE 2003027, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2003027
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    3. Marc Bohmann & Vinay Patel, 2020. "Information Leakage in Energy Derivatives around News Announcements," Published Paper Series 2020-2, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    4. Lima, Luiz Renato & Néri, Breno Pinheiro, 2007. "Comparing Value-at-Risk Methodologies," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 27(1), May.
    5. Huiling Yuan & Yong Zhou & Zhiyuan Zhang & Xiangyu Cui, 2019. "Forecasting security's volatility using low-frequency historical data, high-frequency historical data and option-implied volatility," Papers 1907.02666, arXiv.org.
    6. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    7. Jungmu Kim & Yuen Jung Park, 2020. "Predictability of OTC Option Volatility for Future Stock Volatility," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
    8. Massimo Peri & Lucia Baldi & Daniela Vandone, 2013. "Price discovery in commodity markets," Applied Economics Letters, Taylor & Francis Journals, vol. 20(4), pages 397-403, March.
    9. Degiannakis, Stavros, 2018. "Multiple days ahead realized volatility forecasting: Single, combined and average forecasts," Global Finance Journal, Elsevier, vol. 36(C), pages 41-61.
    10. Apostolos Kourtis & Raphael N. Markellos & Lazaros Symeonidis, 2016. "An International Comparison of Implied, Realized, and GARCH Volatility Forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1164-1193, December.
    11. Youssef, Manel & Belkacem, Lotfi & Mokni, Khaled, 2015. "Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach," Energy Economics, Elsevier, vol. 51(C), pages 99-110.
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    13. Söylemez, Arif Orçun, 2020. "How Do Volatility and Return Series Interact?," MPRA Paper 104687, University Library of Munich, Germany.
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    16. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.

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