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Modelling the Implied Volatility of Options on Long Gilt Futures

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  • Chris Brooks
  • M. Currim Oozeer

Abstract

This paper investigates the properties of implied volatility series calculated from options on Treasury bond futures, traded on LIFFE. We demonstrate that the use of near‐maturity at the money options to calculate implied volatilities causes less mis‐pricing and is therefore superior to, a weighted average measure encompassing all relevant options. We demonstrate that, whilst a set of macroeconomic variables has some predictive power for implied volatilities, we are not able to earn excess returns by trading on the basis of these predictions once we allow for typical investor transactions costs.

Suggested Citation

  • Chris Brooks & M. Currim Oozeer, 2002. "Modelling the Implied Volatility of Options on Long Gilt Futures," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 29(1‐2), pages 111-137.
    • Handle: RePEc:bla:jbfnac:v:29:y:2002:i:1-2:p:111-137
    DOI: 10.1111/1468-5957.00426
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    Cited by:

    1. Lanne, Markku & Ahoniemi, Katja, 2008. "Implied Volatility with Time-Varying Regime Probabilities," MPRA Paper 23721, University Library of Munich, Germany.
    2. Francesco Audrino & Dominik Colangelo, 2009. "Option trading strategies based on semi-parametric implied volatility surface prediction," University of St. Gallen Department of Economics working paper series 2009 2009-24, Department of Economics, University of St. Gallen.
    3. Yanhui Chen & Kin Lai & Jiangze Du, 2014. "Modeling and forecasting Hang Seng index volatility with day-of-week effect, spillover effect based on ARIMA and HAR," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(2), pages 113-132, December.
    4. Carlos Alberto Piscarreta Pinto Ferreira, 2022. "Revisiting The Determinants Of Sovereign Bond Yield Volatility," Working Papers REM 2022/0241, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    5. Ahoniemi, Katja & Lanne, Markku, 2009. "Joint modeling of call and put implied volatility," International Journal of Forecasting, Elsevier, vol. 25(2), pages 239-258.
    6. Carlos Alberto Piscarreta Pinto Ferreira, 2022. "Investor Base Dynamics and Sovereign Bond Yield Volatility," Working Papers REM 2022/0234, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    7. 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.
    8. Psaradellis, Ioannis & Sermpinis, Georgios, 2016. "Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1268-1283.
    9. Viteva, Svetlana & Veld-Merkoulova, Yulia V. & Campbell, Kevin, 2014. "The forecasting accuracy of implied volatility from ECX carbon options," Energy Economics, Elsevier, vol. 45(C), pages 475-484.
    10. Markopoulou, Chryssa & Skintzi, Vasiliki & Refenes, Apostolos, 2016. "On the predictability of model-free implied correlation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 527-547.
    11. Adam Clements & Joanne Fuller, 2012. "Forecasting increases in the VIX: A time-varying long volatility hedge for equities," NCER Working Paper Series 88, National Centre for Econometric Research.
    12. Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.
    13. Swasti R. Khuntia & Jose L. Rueda & Mart A.M.M. Van der Meijden, 2018. "Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model," Energies, MDPI, vol. 11(12), pages 1-19, November.

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