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

G. Mazzei, F. G. Bellora and J. A. Serur

Papers from arXiv.org

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.

Date: 2021-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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Published in MACI 8 2021 p.459-462

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