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Optimal adaptive sequential calibration of option models

Lindström, Erik LU and Åkerlindh, Carl LU (2018) In International Series in Operations Research and Management Science 257. p.165-181
Abstract

Option models needs to be recalibrated as new data becomes available. The updated model parameters will depend on previous parameters and new data, making adaptive sequential calibration a suitable choice. We introduce a method for optimally tuning the parameter adaptivity when non-linear filters are used for calibration, as well as extending the dynamics of the parameters. The adaptivity is optimized by defining a statistical model, including both the option models and the adaptivity parameters. It turns out the corresponding (log-)likelihood function can be optimized through the EM algorithm, which ensures that the optimization is robust. We evaluate the method on simulated data and S&P 500 index options, seeing that we can track... (More)

Option models needs to be recalibrated as new data becomes available. The updated model parameters will depend on previous parameters and new data, making adaptive sequential calibration a suitable choice. We introduce a method for optimally tuning the parameter adaptivity when non-linear filters are used for calibration, as well as extending the dynamics of the parameters. The adaptivity is optimized by defining a statistical model, including both the option models and the adaptivity parameters. It turns out the corresponding (log-)likelihood function can be optimized through the EM algorithm, which ensures that the optimization is robust. We evaluate the method on simulated data and S&P 500 index options, seeing that we can track variations in the model parameters well. The likelihood framework is also used for model selection where we find support for both complex option models as well as non-trivial adaptivity. This is made feasible with the optimal tuning presented in this chapter.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
EM algorithm, Fourier Gauss-Laguerre option pricing, Sequential option calibration, Unscented Kalman filter
in
International Series in Operations Research and Management Science
volume
257
pages
17 pages
publisher
Springer New York LLC
external identifiers
  • scopus:85030719596
ISSN
08848289
DOI
10.1007/978-3-319-61320-8_8
language
English
LU publication?
yes
id
00ce4990-b816-4395-80e0-04c504e2e925
date added to LUP
2017-10-16 07:57:12
date last changed
2017-10-16 07:57:12
@inbook{00ce4990-b816-4395-80e0-04c504e2e925,
  abstract     = {<p>Option models needs to be recalibrated as new data becomes available. The updated model parameters will depend on previous parameters and new data, making adaptive sequential calibration a suitable choice. We introduce a method for optimally tuning the parameter adaptivity when non-linear filters are used for calibration, as well as extending the dynamics of the parameters. The adaptivity is optimized by defining a statistical model, including both the option models and the adaptivity parameters. It turns out the corresponding (log-)likelihood function can be optimized through the EM algorithm, which ensures that the optimization is robust. We evaluate the method on simulated data and S&amp;P 500 index options, seeing that we can track variations in the model parameters well. The likelihood framework is also used for model selection where we find support for both complex option models as well as non-trivial adaptivity. This is made feasible with the optimal tuning presented in this chapter.</p>},
  author       = {Lindström, Erik and Åkerlindh, Carl},
  issn         = {08848289},
  keyword      = {EM algorithm,Fourier Gauss-Laguerre option pricing,Sequential option calibration,Unscented Kalman filter},
  language     = {eng},
  pages        = {165--181},
  publisher    = {Springer New York LLC},
  series       = {International Series in Operations Research and Management Science},
  title        = {Optimal adaptive sequential calibration of option models},
  url          = {http://dx.doi.org/10.1007/978-3-319-61320-8_8},
  volume       = {257},
  year         = {2018},
}