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Forecasting the Term Structure of Implied Volatilities

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  • Guo, Biao
  • Han, Qian
  • Lin, Hai

Abstract

Neumann and Skiadopoulos (2013) document that although the implied volatilities are predictable, their economic pro ts become insignificant once the cost is accounted for. We show that the trading strategies based on the predictability of implied volatilities could generate significant risk-adjusted returns after controlling for the transaction cost. The implied volatility curve information is useful for the out-of-sample forecast of implied volatilities up to one week. Short-maturity implied volatilities tend to be more predictable than long-maturity implied volatilities. Although the long-maturity options are much less traded than the short-maturity options, their implied volatilities provide much more information on the price discovery.

Suggested Citation

  • Guo, Biao & Han, Qian & Lin, Hai, 2015. "Forecasting the Term Structure of Implied Volatilities," Working Paper Series 20148, Victoria University of Wellington, School of Economics and Finance.
    • Handle: RePEc:vuw:vuwecf:20148
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    File URL: https://ir.wgtn.ac.nz/handle/123456789/20148
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    References listed on IDEAS

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    1. Peter Christoffersen & Steven Heston & Kris Jacobs, 2009. "The Shape and Term Structure of the Index Option Smirk: Why Multifactor Stochastic Volatility Models Work So Well," Management Science, INFORMS, vol. 55(12), pages 1914-1932, December.
    2. Chalamandaris, Georgios & Tsekrekos, Andrianos E., 2010. "Predictable dynamics in implied volatility surfaces from OTC currency options," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1175-1188, June.
    3. Konstantinidi, Eirini & Skiadopoulos, George & Tzagkaraki, Emilia, 2008. "Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2401-2411, November.
    4. repec:bla:jfinan:v:59:y:2004:i:3:p:1235-1258 is not listed on IDEAS
    5. Sílvia Gonçalves & Massimo Guidolin, 2006. "Predictable Dynamics in the S&P 500 Index Options Implied Volatility Surface," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1591-1636, May.
    6. Roger K. Loh & René M. Stulz, 2018. "Is Sell‐Side Research More Valuable in Bad Times?," Journal of Finance, American Finance Association, vol. 73(3), pages 959-1013, June.
    7. Hodrick, Robert J, 1992. "Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement," The Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 357-386.
    8. Edith S. Hotchkiss & Tavy Ronen, 2002. "The Informational Efficiency of the Corporate Bond Market: An Intraday Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 15(5), pages 1325-1354.
    9. Hogan, Steve & Jarrow, Robert & Teo, Melvyn & Warachka, Mitch, 2004. "Testing market efficiency using statistical arbitrage with applications to momentum and value strategies," Journal of Financial Economics, Elsevier, vol. 73(3), pages 525-565, September.
    10. Haoxiang Zhu, 2014. "Do Dark Pools Harm Price Discovery?," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 747-789.
    11. Chalamandaris, Georgios & Tsekrekos, Andrianos E., 2011. "How important is the term structure in implied volatility surface modeling? Evidence from foreign exchange options," Journal of International Money and Finance, Elsevier, vol. 30(4), pages 623-640, June.
    12. Phillips, Susan M. & Smith, Clifford Jr., 1980. "Trading costs for listed options : The implications for market efficiency," Journal of Financial Economics, Elsevier, vol. 8(2), pages 179-201, June.
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