Nonparametric predictive inference for European option pricing based on the binomial tree model
| Autor: | Frank P. A. Coolen, Ting He, Tahani Coolen-Maturi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Marketing
021103 operations research Strategy and Management 0211 other engineering and technologies Nonparametric statistics Astrophysics::Cosmology and Extragalactic Astrophysics 02 engineering and technology Management Science and Operations Research Imprecise probability Management Information Systems Binomial distribution Predictive inference Valuation of options 0202 electrical engineering electronic engineering information engineering Econometrics 020201 artificial intelligence & image processing Asset (economics) Binomial options pricing model Constant (mathematics) Mathematics |
| Zdroj: | Journal of the Operational Research Society. 70:1692-1708 |
| ISSN: | 1476-9360 0160-5682 |
| Popis: | In finance, option pricing is one of the main topics. A basic model for option pricing is the Binomial Tree Model, proposed by Cox, Ross, and Rubinstein in 1979 (CRR). This model assumes that the underlying asset price follows a binomial distribution with a constant upward probability, the so-called risk-neutral probability. In this paper, we propose a novel method based on the binomial tree. Rather than using the risk-neutral probability, we apply Nonparametric Predictive Inference (NPI) to infer imprecise probabilities of movements, reflecting more uncertainty while learning from data. To study its performance, we price the same European options utilizing both the NPI method and the CRR model and compare the results in two different scenarios, firstly where the CRR assumptions are right, and secondly where the CRR model assumptions deviate from the real market. It turns out that our NPI method, as expected, cannot perform better than the CRR in the first scenario, but can do better in the second scenario. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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