Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets
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.
Keywords
Cite
@article{arxiv.2109.12337,
title = {Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets},
author = {G. Mazzei and F. G. Bellora and J. A. Serur},
journal= {arXiv preprint arXiv:2109.12337},
year = {2021}
}