2023/02/16 by Ali Fathi, Fathi, Ali, Bernhard Hientzsch +1
Business, Management and Accounting · Economics, Econometrics and Finance · Engineering · #Advanced Queuing Theory Analysis #Computational Finance (q-fin.CP) #Energy Load and Power Forecasting #FOS: Economics and business #Stochastic processes and financial applications
paper · pdf · doi:10.48550/arxiv.2302.07996
openalex publication_date 2023/02/16 · openalex created_date 2023/02/18 · openalex updated_date 2026/07/28
We consider two data-driven approaches to hedging, Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control, under a stepwise mean-variance objective. We compare their performance for a European call option in the presence of transaction costs under discrete trading schedules. We do this for a setting where stock prices follow Black-Scholes-Merton dynamics and the "book-keeping" price for the option is given by the Black-Scholes-Merton model with the same parameters. This simulated data setting provides a "sanitized" lab environment with simple enough features where we can conduct a detailed study of strengths, features, issues, and limitations of these two approaches. However, the formulation is model free and could allow any other setting with available book-keeping prices. We consider this study as a first step to develop, test, and validate autonomous hedging agents, and we provide blueprints for such efforts that address various concerns and requirements.