Deep Learning Strategies for Intraday Optimal Carbon Options Trading with Price Impact Considerations

This paper solves the optimal trading problem of carbon options with a deep learning approach. In this setting, a trader wants to sell out the option inventory within a day. Since trading a large-size order in the market will influence the price, the trader needs to design a trading strategy to maximize the profit and loss (PnL). We propose a deep learning strategy for carbon options optimal trading, which can also be extended to stock options. Using the data from the European carbon market, we apply our deep learning strategy to four types of price impact functions: linear, logarithmic, power law, and time-varying. We show that our deep learning strategy performs much better than the naive strategy and the TWAP (time-weighted average price) strategy, which are widely used in the industry, especially when the price impact function is time-varying. Our neural network strategy’s advantage becomes larger when the market is more illiquid.