Accelerated American Option Pricing with Deep Neural Networks

David Anderson and Urban Ulrych
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David Anderson: University of Zurich
Urban Ulrych: University of Zurich - Department of Banking and Finance; Swiss Finance Institute

No 22-03, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: Given the competitiveness of a market-making environment, the ability to speedily quote option prices consistent with an ever-changing market environment is essential. Thus, the smallest acceleration or improvement over traditional pricing methods is crucial to avoid arbitrage. We propose a novel method for accelerating the pricing of American options to near-instantaneous using a feed-forward neural network. This neural network is trained over the chosen (e.g., Heston) stochastic volatility specification. Such an approach facilitates parameter interpretability, as generally required by the regulators, and establishes our method in the area of eXplainable Artificial Intelligence (XAI) for finance. We show that the proposed deep explainable pricer induces a speed accuracy trade-off compared to the typical Monte Carlo or Partial Differential Equation-based pricing methods. Moreover, the proposed approach allows for pricing derivatives with path dependent and more complex payoffs and is, given the sufficient accuracy of computation and its tractable nature, applicable in a market-making environment.

Keywords: American Option Pricing; Deep Neural Networks; Explainable Artificial Intelligence; Speed-Accuracy Trade-Off; Market Making; Heston Model; Computational Finance. (search for similar items in EconPapers)
JEL-codes: C45 C63 G13 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2022-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-ore and nep-rmg
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2203

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