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Deep Learning for Exotic Option Valuation

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  • Jay Cao
  • Jacky Chen
  • John Hull
  • Zissis Poulos

Abstract

A common approach to valuing exotic options involves choosing a model and then determining its parameters to fit the volatility surface as closely as possible. We refer to this as the model calibration approach (MCA). A disadvantage of MCA is that some information in the volatility surface is lost during the calibration process and the prices of exotic options will not in general be consistent with those of plain vanilla options. We consider an alternative approach where the structure of the user's preferred model is preserved but points on the volatility are features input to a neural network. We refer to this as the volatility feature approach (VFA) model. We conduct experiments showing that VFA can be expected to outperform MCA for the volatility surfaces encountered in practice. Once the upfront computational time has been invested in developing the neural network, the valuation of exotic options using VFA is very fast.

Suggested Citation

  • Jay Cao & Jacky Chen & John Hull & Zissis Poulos, 2021. "Deep Learning for Exotic Option Valuation," Papers 2103.12551, arXiv.org, revised Sep 2021.
    • Handle: RePEc:arx:papers:2103.12551
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    References listed on IDEAS

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    Cited by:

    1. Valentin Tissot-Daguette, 2021. "Projection of Functionals and Fast Pricing of Exotic Options," Papers 2111.03713, arXiv.org, revised Apr 2022.

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