On nonparametric predictive inference for asset and European option trading in the binomial tree model
| Autor: | Tahani Coolen-Maturi, Frank P. A. Coolen, Junbin Chen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Marketing
021103 operations research Binomial (polynomial) Computer science Strategy and Management 0211 other engineering and technologies Nonparametric statistics 02 engineering and technology Management Science and Operations Research Imprecise probability Management Information Systems Predictive inference 0202 electrical engineering electronic engineering information engineering Econometrics 020201 artificial intelligence & image processing Asset (economics) Binomial options pricing model |
| Zdroj: | Journal of the Operational Research Society, 2019, Vol.70(10), pp.1678-1691 [Peer Reviewed Journal] |
| ISSN: | 1476-9360 0160-5682 |
| Popis: | This paper introduces a novel method for asset and option trading in a binomial scenario. This method uses nonparametric predictive inference (NPI), a statistical methodology within im- precise probability theory. Instead of inducing a single probability distribution from the existing observations, the imprecise method used here induces a set of probability distributions. Based on the induced imprecise probability, one could form a set of conservative trading strategies for assets and options. By integrating NPI imprecise probability and expectation with the classical nancial binomial tree model, two rational decision routes for asset trading and for European option trading are suggested. The performances of these trading routes are investigated by com- puter simulations. The simulation results indicate that the NPI based trading routes presented in this paper have good predictive properties. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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