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Regime‐dependent smile‐adjusted delta hedging

Author

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  • Carol Alexander
  • Alexander Rubinov
  • Markus Kalepky
  • Stamatis Leontsinis

Abstract

Most research on option hedging has compared the performance of delta hedges derived from different stochastic volatility models with Black-Scholes-Merton (BSM) deltas, and in particular with the `implied BSM' model in which an option's delta is based on its own market implied volatility. Various empirical studies of vanilla options on different equity indices have provided substantial evidence that minimum variance deltas outperform the partial derivative delta, but no clear evidence that they can consistently outperform the implied BSM delta, or other simple smile-adjusted deltas that are popular with option traders. This paper focuses exclusively on smile adjustments to BSM deltas with an emphasis of those which depend on the market regime. Using 16.5-years of daily closing prices for FTSE 100 vanilla options, out-of-sample tests of their hedging performance clearly demonstrate that even the simplest of the regime-dependent smile adjustments will consistently and significantly improve on implied BSM delta hedging, for options of all moneyness and maturities and whether rebalancing is daily, weekly or fortnightly. For most options and over all hedging horizons the regime-dependent smile-adjusted delta-hedging errors are only 50% - 60% as large as the implied BSM hedging errors, on average. During volatile market periods the risk reduction is much greater than it is during tranquil periods.
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Suggested Citation

  • Carol Alexander & Alexander Rubinov & Markus Kalepky & Stamatis Leontsinis, 2012. "Regime‐dependent smile‐adjusted delta hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(3), pages 203-229, March.
  • Handle: RePEc:wly:jfutmk:v:32:y:2012:i:3:p:203-229
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    Cited by:

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    2. Tsuji, Chikashi, 2020. "Correlation and spillover effects between the US and international banking sectors: New evidence and implications for risk management," International Review of Financial Analysis, Elsevier, vol. 70(C).
    3. Hull, John & White, Alan, 2017. "Optimal delta hedging for options," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 180-190.
    4. Johannes Ruf & Weiguan Wang, 2020. "Hedging with Linear Regressions and Neural Networks," Papers 2004.08891, arXiv.org, revised Jun 2021.
    5. Xia, Kun & Yang, Xuewei & Zhu, Peng, 2023. "Delta hedging and volatility-price elasticity: A two-step approach," Journal of Banking & Finance, Elsevier, vol. 153(C).
    6. Maciej Augustyniak & Mathieu Boudreault, 2017. "Mitigating Interest Rate Risk in Variable Annuities: An Analysis of Hedging Effectiveness under Model Risk," North American Actuarial Journal, Taylor & Francis Journals, vol. 21(4), pages 502-525, October.
    7. Roberto Daluiso & Marco Pinciroli & Michele Trapletti & Edoardo Vittori, 2023. "CVA Hedging by Risk-Averse Stochastic-Horizon Reinforcement Learning," Papers 2312.14044, arXiv.org.

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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G19 - Financial Economics - - General Financial Markets - - - Other

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