Traditional hedging procedures are based on local sensitivities of risk factors called Greeks. However, such myopic approaches do not consider the interaction of hedging errors through time. Global hedging procedures, which are expressed as optimization problems, consider such interactions. These algorithms received recent attention in the literature. Such procedures relying on machine learning algorithms such as dynamic programming and reinforcement learning have the potential of greatly outperforming the traditional Greeks-based hedging, especially for long-dated options.
Frédéric Godin is an Assistant Professor at Concordia University (Montreal, Canada) in the Mathematics and Statistics Department. His expertise and areas of research are financial engineering, risk management, actuarial science and data science. He also holds the Fellow of the Society of Actuaries (FSA) and Associate of the Canadian Institute of Actuaries (ACIA) designations. He published several papers in various international mathematical finance and actuarial science journals such as Quantitative Finance, Journal of Risk and Insurance, ASTIN Bulletin, Scandinavian Actuarial Research, Insurance: Mathematics and Economics and Journal of Economic Dynamics and Control. His most active research topics are dynamic hedging procedures, variable annuities and derivatives pricing.