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

Emmanuel M. Tadjouddine*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 languageEnglish
Pages (from-to)431-438
Number of pages8
JournalApplied Soft Computing
Volume42
DOIs
Publication statusPublished - 1 May 2016
Externally publishedYes

Keywords

  • Binomial tree
  • Entropy minimization
  • Model calibration
  • Pricing algorithms
  • Sequential auctions

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