Abstract
Financial markets are complex processes where investors interact
to set prices. We present a framework for option valuation under imperfect
information, taking risk neutral parameter uncertainty into account. The
framework is a direct generalization of the existing valuation methodology.
Many investors base their decisions on mathematical models that have been
calibrated to market prices. We argue that the calibration process introduces
a source of uncertainty that needs to be taken into account. The models
and parameters used may differ to such extent that one investor may find an
option under-priced whereas another investor may find the very same option
over-priced. This problem is not taken into account by any of the standard
models.
The paper is concluded by presenting simulations and an empirical study
on FX options where we demonstrate improved predictive performance (in
sample and out of sample) using this framework.
to set prices. We present a framework for option valuation under imperfect
information, taking risk neutral parameter uncertainty into account. The
framework is a direct generalization of the existing valuation methodology.
Many investors base their decisions on mathematical models that have been
calibrated to market prices. We argue that the calibration process introduces
a source of uncertainty that needs to be taken into account. The models
and parameters used may differ to such extent that one investor may find an
option under-priced whereas another investor may find the very same option
over-priced. This problem is not taken into account by any of the standard
models.
The paper is concluded by presenting simulations and an empirical study
on FX options where we demonstrate improved predictive performance (in
sample and out of sample) using this framework.
Original language | English |
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Pages (from-to) | 1-15 |
Journal | Advances in Decision Sciences |
Volume | 2010 |
DOIs | |
Publication status | Published - 2010 |
Bibliographical note
http://www.hindawi.com/journals/ads/Föregående titel: Journal of Applied Mathematics and Decision Sciences
Subject classification (UKÄ)
- Probability Theory and Statistics