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.
Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
Department of Finance, HEC Montréal, Canada（蒙特利尔HEC金融系）；GERAD and Department of Decision Sciences, HEC Montréal, Canada（GERAD与蒙特利尔HEC决策科学系）；Concordia University, Department of Mathematics and Statistics, Canada（康科迪亚大学数学与统计学系）
