Version 1
: Received: 25 July 2019 / Approved: 28 July 2019 / Online: 28 July 2019 (16:17:51 CEST)
How to cite:
Ota, Y.; Jiang, Y. Parameters Identification for Inverse Option Problems Using Markov Chain Monte Carlo Methods. Preprints2019, 2019070318. https://doi.org/10.20944/preprints201907.0318.v1
Ota, Y.; Jiang, Y. Parameters Identification for Inverse Option Problems Using Markov Chain Monte Carlo Methods. Preprints 2019, 2019070318. https://doi.org/10.20944/preprints201907.0318.v1
Ota, Y.; Jiang, Y. Parameters Identification for Inverse Option Problems Using Markov Chain Monte Carlo Methods. Preprints2019, 2019070318. https://doi.org/10.20944/preprints201907.0318.v1
APA Style
Ota, Y., & Jiang, Y. (2019). Parameters Identification for Inverse Option Problems Using Markov Chain Monte Carlo Methods. Preprints. https://doi.org/10.20944/preprints201907.0318.v1
Chicago/Turabian Style
Ota, Y. and Yu Jiang. 2019 "Parameters Identification for Inverse Option Problems Using Markov Chain Monte Carlo Methods" Preprints. https://doi.org/10.20944/preprints201907.0318.v1
Abstract
This paper investigates the inverse option problems (IOP) in the extended Black--Scholes model arising in financial markets. We identify the volatility and the drift coefficient from the measured data in financial markets using a Bayesian inference approach, which is presented as an IOP solution. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC) algorithm, which exploits the posterior state space. The efficient sampling strategy of the MCMC algorithm enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown trend and volatility coefficients from the measured data.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
