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Delta-hedging in fractional volatility models

Author

Listed:
  • Qi Zhao

    (University of Illinois at Urbana-Champaign)

  • Alexandra Chronopoulou

    (University of Illinois at Urbana-Champaign)

Abstract

In this paper, we propose a delta-hedging strategy for a long memory stochastic volatility model (LMSV). This is a model in which the volatility is driven by a fractional Ornstein–Uhlenbeck process with long-memory parameter H. We compute the so-called hedging bias, i.e. the difference between the Black–Scholes Delta and the LMSV Delta as a function of H, and we determine when a European-type option is over-hedged or under-hedged.

Suggested Citation

  • Qi Zhao & Alexandra Chronopoulou, 2023. "Delta-hedging in fractional volatility models," Annals of Finance, Springer, vol. 19(1), pages 119-140, March.
    • Handle: RePEc:kap:annfin:v:19:y:2023:i:1:d:10.1007_s10436-022-00415-w
    DOI: 10.1007/s10436-022-00415-w
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    References listed on IDEAS

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    1. Alexandra Chronopoulou & Frederi G. Viens, 2012. "Stochastic volatility and option pricing with long-memory in discrete and continuous time," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 635-649, December.
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    More about this item

    Keywords

    Long-memory; Stochastic volatility; Hedging; Hedging bias;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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