  The price of carbon emission rights play a crucial role in carbon trading
markets. Therefore, accurate prediction of the price is critical. Taking the
Shanghai pilot market as an example, this paper attempted to design a carbon
emission purchasing strategy for enterprises, and establish a carbon emission
price prediction model to help them reduce the purchasing cost. To make
predictions more precise, we built a hybrid deep learning model by embedding
Generalized Autoregressive Conditional Heteroskedastic (GARCH) into the Gate
Recurrent Unit (GRU) model, and compared the performance with those of other
models. Then, based on the Iceberg Order Theory and the predicted price, we
proposed the purchasing strategy of carbon emission rights. As a result, the
prediction errors of the GARCH-GRU model with a 5-day sliding time window were
the minimum values of all six models. And in the simulation, the purchasing
strategy based on the GARCH-GRU model was executed with the least cost as well.
The carbon emission purchasing strategy constructed by the hybrid deep learning
method can accurately send out timing signals, and help enterprises reduce the
purchasing cost of carbon emission permits.
