A Bayesian Beta Markov Random Field calibration of the term structure of implied risk neutral densities
We build on the derivative pricing calibration literature, and propose a more general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates through a Bayesian dynamic Beta Markov Random Field. Our appr...
- Autores:
-
Casarin, Roberto
Leisen, Fabrizio
Molina, Germán
ter Horst, Enrique
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2015
- Institución:
- Colegio de Estudios Superiores de Administración
- Repositorio:
- Repositorio CESA
- Idioma:
- eng
- OAI Identifier:
- oai:repository.cesa.edu.co:10726/5120
- Acceso en línea:
- http://hdl.handle.net/10726/5120
https://projecteuclid.org/journals/bayesian-analysis/volume-10/issue-4/A-Bayesian-Beta-Markov-Random-Field-Calibration-of-the-Term/10.1214/15-BA960SI.full
- Palabra clave:
- Bayesian inference
Beta Markov Random Fields
Density calibration
Distortion function
Exchange Metropolis Hastings
Risk neutral measure
- Rights
- openAccess
- License
- Abierto (Texto Completo)
| Summary: | We build on the derivative pricing calibration literature, and propose a more general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates through a Bayesian dynamic Beta Markov Random Field. Our approach allows for possible time dependence between densities with the same maturity, and for dependence across maturities at the same point in time. This approach to the risk neutral density calibration problem encompasses model flexibility, parameter parsimony, and, more importantly, information pooling across densities. This proposed methodology can be naturally extended to other areas where multidimensional calibration is needed. |
|---|