Calibration based on entropy minimization for a class of asset pricing models

We consider the problem of calibrating pricing models based on the binomial tree method to market data in a network of auctions where agents are supposed to maximize a given utility function. The calibration is carried out using the minimum entropy principle to find a probability distribution that minimizes a weighted misfit between predicted and observed data. Numerical results from calibrating the mid prices from the bid-ask pairs of the buyer and seller to Taobao data demonstrated the feasibility of this approach in the case of pricing goods in a sequential auction. Further numerical test cases have been presented and have shown promising results. This work can equip those engaged in electronic trading with computational tools to improve their decision-making process in an uncertain environment.