VaR methods for the dynamic impawn rate of steel in inventory financing under autocorrelative return

This paper proposes the way of setting the dynamic impawn rate by dividing the impawn periods into different risk windows. In an efficient financial market, the return is hypothetically independent, while in a pledged inventory market where spot transactions predominate, the return is auto-correlative. Therefore, the key to setting the impawn rate is to predict the long-term risk. In this experiment, using the database of spot steel, we established a model with the formula AR (1)-GARCH (1,1)-GED, forecasting the VaR of steel during the different risk windows in the impawn period through a method of out-of-sample, and got the impawn rate according with the risk exposure of banks. The results of our experiment indicated that the introduction of coefficient K into the model can significantly improve bank risk coverage and reduce its efficiency loss. Besides, the impawn rate obtained by the model correlates positively with the lowest price in the future risk windows.

► The core of setting impawn rate of inventory is long-term risk forecasting. ► The log-returns of inventory is autocorrelative. ► A framework of setting dynamic impawn rate for banks. ► To establish VaR-GARCH(1,1)-GED model. ► To introduce the coefficient K to revise the model.