Abstract
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.
Original language | English |
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Pages (from-to) | 431-438 |
Number of pages | 8 |
Journal | Applied Soft Computing |
Volume | 42 |
DOIs | |
Publication status | Published - 1 May 2016 |
Externally published | Yes |
Keywords
- Binomial tree
- Entropy minimization
- Model calibration
- Pricing algorithms
- Sequential auctions