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Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets

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  • G. Mazzei
  • F. G. Bellora
  • J. A. Serur

Abstract

In most real scenarios the construction of a risk-neutral portfolio must be performed in discrete time and with transaction costs. Two human imposed constraints are the risk-aversion and the profit maximization, which together define a nonlinear optimization problem with a model-dependent solution. In this context, an optimal fixed frequency hedging strategy can be determined a posteriori by maximizing a sharpe ratio simil path dependent reward function. Sampling from Heston processes, a convolutional neural network was trained to infer which period is optimal using partial information, thus leading to a dynamic hedging strategy in which the portfolio is hedged at various frequencies, each weighted by the probability estimate of that frequency being optimal.

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  • G. Mazzei & F. G. Bellora & J. A. Serur, 2021. "Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets," Papers 2109.12337, arXiv.org.
    • Handle: RePEc:arx:papers:2109.12337
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    References listed on IDEAS

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    1. Roger Lord & Christian Kahl, 2006. "Optimal Fourier Inversion in Semi-analytical Option Pricing," Tinbergen Institute Discussion Papers 06-066/2, Tinbergen Institute, revised 05 Jun 2007.
    2. Lionel Martellini, 2000. "Efficient Option Replication in the Presence of Transactions Costs," Review of Derivatives Research, Springer, vol. 4(2), pages 107-131, May.
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